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Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/tasks/allegro_kuka/allegro_kuka_throw.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from typing import List
import torch
from isaacgym import gymapi
from torch import Tensor
from isaacgymenvs.utils.torch_jit_utils import to_torch, torch_rand_float
from isaacgymenvs.tasks.allegro_kuka.allegro_kuka_base import AllegroKukaBase
from isaacgymenvs.tasks.allegro_kuka.allegro_kuka_utils import tolerance_successes_objective
class AllegroKukaThrow(AllegroKukaBase):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.bucket_asset = self.bucket_pose = None
self.bucket_object_indices = []
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
def _object_keypoint_offsets(self):
"""Throw task uses only a single object keypoint since we do not care about object orientation."""
return [[0, 0, 0]]
def _load_additional_assets(self, object_asset_root, arm_pose):
"""
returns: tuple (num_rigid_bodies, num_shapes)
"""
bucket_asset_options = gymapi.AssetOptions()
bucket_asset_options.disable_gravity = False
bucket_asset_options.fix_base_link = True
bucket_asset_options.collapse_fixed_joints = True
bucket_asset_options.vhacd_enabled = True
bucket_asset_options.vhacd_params = gymapi.VhacdParams()
bucket_asset_options.vhacd_params.resolution = 500000
bucket_asset_options.vhacd_params.max_num_vertices_per_ch = 32
bucket_asset_options.vhacd_params.min_volume_per_ch = 0.001
self.bucket_asset = self.gym.load_asset(
self.sim, object_asset_root, self.asset_files_dict["bucket"], bucket_asset_options
)
self.bucket_pose = gymapi.Transform()
self.bucket_pose.p = gymapi.Vec3()
self.bucket_pose.p.x = arm_pose.p.x - 0.6
self.bucket_pose.p.y = arm_pose.p.y - 1
self.bucket_pose.p.z = arm_pose.p.z + 0.45
bucket_rb_count = self.gym.get_asset_rigid_body_count(self.bucket_asset)
bucket_shapes_count = self.gym.get_asset_rigid_shape_count(self.bucket_asset)
print(f"Bucket rb {bucket_rb_count}, shapes {bucket_shapes_count}")
return bucket_rb_count, bucket_shapes_count
def _create_additional_objects(self, env_ptr, env_idx, object_asset_idx):
bucket_handle = self.gym.create_actor(
env_ptr, self.bucket_asset, self.bucket_pose, "bucket_object", env_idx, 0, 0
)
bucket_object_idx = self.gym.get_actor_index(env_ptr, bucket_handle, gymapi.DOMAIN_SIM)
self.bucket_object_indices.append(bucket_object_idx)
def _after_envs_created(self):
self.bucket_object_indices = to_torch(self.bucket_object_indices, dtype=torch.long, device=self.device)
def _reset_target(self, env_ids: Tensor) -> None:
# whether we place the bucket to the left or to the right of the table
left_right_random = torch_rand_float(-1.0, 1.0, (len(env_ids), 1), device=self.device)
x_pos = torch.where(
left_right_random > 0, 0.5 * torch.ones_like(left_right_random), -0.5 * torch.ones_like(left_right_random)
)
x_pos += torch.sign(left_right_random) * torch_rand_float(0, 0.4, (len(env_ids), 1), device=self.device)
# y_pos = torch_rand_float(-0.6, 0.4, (len(env_ids), 1), device=self.device)
y_pos = torch_rand_float(-1.0, 0.7, (len(env_ids), 1), device=self.device)
z_pos = torch_rand_float(0.0, 1.0, (len(env_ids), 1), device=self.device)
self.root_state_tensor[self.bucket_object_indices[env_ids], 0:1] = x_pos
self.root_state_tensor[self.bucket_object_indices[env_ids], 1:2] = y_pos
self.root_state_tensor[self.bucket_object_indices[env_ids], 2:3] = z_pos
self.goal_states[env_ids, 0:1] = x_pos
self.goal_states[env_ids, 1:2] = y_pos
self.goal_states[env_ids, 2:3] = z_pos + 0.05
# we also reset the object to its initial position
self.reset_object_pose(env_ids)
# since we put the object back on the table, also reset the lifting reward
self.lifted_object[env_ids] = False
object_indices_to_reset = [self.bucket_object_indices[env_ids], self.object_indices[env_ids]]
self.deferred_set_actor_root_state_tensor_indexed(object_indices_to_reset)
def _extra_object_indices(self, env_ids: Tensor) -> List[Tensor]:
return [self.bucket_object_indices[env_ids]]
def _true_objective(self) -> Tensor:
true_objective = tolerance_successes_objective(
self.success_tolerance, self.initial_tolerance, self.target_tolerance, self.successes
)
return true_objective
| 6,261 | Python | 49.096 | 120 | 0.694458 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/tasks/utils/generate_cuboids.py | import os
from os.path import join
from jinja2 import Environment, select_autoescape, FileSystemLoader
def generate_assets(scales, min_volume, max_volume, generated_assets_dir, base_mesh):
template_dir = join(os.path.dirname(os.path.abspath(__file__)), "../../../assets/asset_templates")
print(f'Assets template dir: {template_dir}')
env = Environment(
loader=FileSystemLoader(template_dir),
autoescape=select_autoescape(),
)
template = env.get_template("cube_multicolor.urdf.template")
cube_size_m = 0.05
idx = 0
for x_scale in scales:
for y_scale in scales:
for z_scale in scales:
volume = x_scale * y_scale * z_scale / (100 * 100 * 100)
if volume > max_volume:
continue
if volume < min_volume:
continue
curr_scales = [x_scale, y_scale, z_scale]
curr_scales.sort()
if curr_scales[0] * 3 <= curr_scales[1]:
# skip thin "plates"
continue
asset = template.render(base_mesh=base_mesh,
x_scale=cube_size_m * (x_scale / 100),
y_scale=cube_size_m * (y_scale / 100),
z_scale=cube_size_m * (z_scale / 100))
fname = f"{idx:03d}_cube_{x_scale}_{y_scale}_{z_scale}.urdf"
idx += 1
with open(join(generated_assets_dir, fname), "w") as fobj:
fobj.write(asset)
def generate_small_cuboids(assets_dir, base_mesh):
scales = [100, 50, 66, 75, 125, 150, 175, 200, 250, 300]
min_volume = 0.75
max_volume = 1.5
generate_assets(scales, min_volume, max_volume, assets_dir, base_mesh)
def generate_big_cuboids(assets_dir, base_mesh):
scales = [100, 125, 150, 200, 250, 300, 350]
min_volume = 2.5
max_volume = 15.0
generate_assets(scales, min_volume, max_volume, assets_dir, base_mesh)
| 2,053 | Python | 35.035087 | 102 | 0.541646 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/tasks/amp/__init__.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
| 1,558 | Python | 54.678569 | 80 | 0.784339 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/tasks/amp/humanoid_amp_base.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymtorch
from isaacgym import gymapi
from isaacgymenvs.utils.torch_jit_utils import quat_mul, to_torch, get_axis_params, calc_heading_quat_inv, \
exp_map_to_quat, quat_to_tan_norm, my_quat_rotate, calc_heading_quat_inv
from ..base.vec_task import VecTask
DOF_BODY_IDS = [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14]
DOF_OFFSETS = [0, 3, 6, 9, 10, 13, 14, 17, 18, 21, 24, 25, 28]
NUM_OBS = 13 + 52 + 28 + 12 # [root_h, root_rot, root_vel, root_ang_vel, dof_pos, dof_vel, key_body_pos]
NUM_ACTIONS = 28
KEY_BODY_NAMES = ["right_hand", "left_hand", "right_foot", "left_foot"]
class HumanoidAMPBase(VecTask):
def __init__(self, config, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = config
self._pd_control = self.cfg["env"]["pdControl"]
self.power_scale = self.cfg["env"]["powerScale"]
self.randomize = self.cfg["task"]["randomize"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.camera_follow = self.cfg["env"].get("cameraFollow", False)
self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"]
self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"]
self.plane_restitution = self.cfg["env"]["plane"]["restitution"]
self.max_episode_length = self.cfg["env"]["episodeLength"]
self._local_root_obs = self.cfg["env"]["localRootObs"]
self._contact_bodies = self.cfg["env"]["contactBodies"]
self._termination_height = self.cfg["env"]["terminationHeight"]
self._enable_early_termination = self.cfg["env"]["enableEarlyTermination"]
self.cfg["env"]["numObservations"] = self.get_obs_size()
self.cfg["env"]["numActions"] = self.get_action_size()
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
dt = self.cfg["sim"]["dt"]
self.dt = self.control_freq_inv * dt
# get gym GPU state tensors
actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
rigid_body_state = self.gym.acquire_rigid_body_state_tensor(self.sim)
contact_force_tensor = self.gym.acquire_net_contact_force_tensor(self.sim)
sensors_per_env = 2
self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, sensors_per_env * 6)
dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs, self.num_dof)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self._root_states = gymtorch.wrap_tensor(actor_root_state)
self._initial_root_states = self._root_states.clone()
self._initial_root_states[:, 7:13] = 0
# create some wrapper tensors for different slices
self._dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self._dof_pos = self._dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self._dof_vel = self._dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
self._initial_dof_pos = torch.zeros_like(self._dof_pos, device=self.device, dtype=torch.float)
right_shoulder_x_handle = self.gym.find_actor_dof_handle(self.envs[0], self.humanoid_handles[0], "right_shoulder_x")
left_shoulder_x_handle = self.gym.find_actor_dof_handle(self.envs[0], self.humanoid_handles[0], "left_shoulder_x")
self._initial_dof_pos[:, right_shoulder_x_handle] = 0.5 * np.pi
self._initial_dof_pos[:, left_shoulder_x_handle] = -0.5 * np.pi
self._initial_dof_vel = torch.zeros_like(self._dof_vel, device=self.device, dtype=torch.float)
self._rigid_body_state = gymtorch.wrap_tensor(rigid_body_state)
self._rigid_body_pos = self._rigid_body_state.view(self.num_envs, self.num_bodies, 13)[..., 0:3]
self._rigid_body_rot = self._rigid_body_state.view(self.num_envs, self.num_bodies, 13)[..., 3:7]
self._rigid_body_vel = self._rigid_body_state.view(self.num_envs, self.num_bodies, 13)[..., 7:10]
self._rigid_body_ang_vel = self._rigid_body_state.view(self.num_envs, self.num_bodies, 13)[..., 10:13]
self._contact_forces = gymtorch.wrap_tensor(contact_force_tensor).view(self.num_envs, self.num_bodies, 3)
self._terminate_buf = torch.ones(self.num_envs, device=self.device, dtype=torch.long)
if self.viewer != None:
self._init_camera()
return
def get_obs_size(self):
return NUM_OBS
def get_action_size(self):
return NUM_ACTIONS
def create_sim(self):
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
# If randomizing, apply once immediately on startup before the fist sim step
if self.randomize:
self.apply_randomizations(self.randomization_params)
return
def reset_idx(self, env_ids):
self._reset_actors(env_ids)
self._refresh_sim_tensors()
self._compute_observations(env_ids)
return
def set_char_color(self, col):
for i in range(self.num_envs):
env_ptr = self.envs[i]
handle = self.humanoid_handles[i]
for j in range(self.num_bodies):
self.gym.set_rigid_body_color(env_ptr, handle, j, gymapi.MESH_VISUAL,
gymapi.Vec3(col[0], col[1], col[2]))
return
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
plane_params.static_friction = self.plane_static_friction
plane_params.dynamic_friction = self.plane_dynamic_friction
plane_params.restitution = self.plane_restitution
self.gym.add_ground(self.sim, plane_params)
return
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../../assets')
asset_file = "mjcf/amp_humanoid.xml"
if "asset" in self.cfg["env"]:
#asset_root = self.cfg["env"]["asset"].get("assetRoot", asset_root)
asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)
asset_options = gymapi.AssetOptions()
asset_options.angular_damping = 0.01
asset_options.max_angular_velocity = 100.0
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
humanoid_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
actuator_props = self.gym.get_asset_actuator_properties(humanoid_asset)
motor_efforts = [prop.motor_effort for prop in actuator_props]
# create force sensors at the feet
right_foot_idx = self.gym.find_asset_rigid_body_index(humanoid_asset, "right_foot")
left_foot_idx = self.gym.find_asset_rigid_body_index(humanoid_asset, "left_foot")
sensor_pose = gymapi.Transform()
self.gym.create_asset_force_sensor(humanoid_asset, right_foot_idx, sensor_pose)
self.gym.create_asset_force_sensor(humanoid_asset, left_foot_idx, sensor_pose)
self.max_motor_effort = max(motor_efforts)
self.motor_efforts = to_torch(motor_efforts, device=self.device)
self.torso_index = 0
self.num_bodies = self.gym.get_asset_rigid_body_count(humanoid_asset)
self.num_dof = self.gym.get_asset_dof_count(humanoid_asset)
self.num_joints = self.gym.get_asset_joint_count(humanoid_asset)
start_pose = gymapi.Transform()
start_pose.p = gymapi.Vec3(*get_axis_params(0.89, self.up_axis_idx))
start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
self.start_rotation = torch.tensor([start_pose.r.x, start_pose.r.y, start_pose.r.z, start_pose.r.w], device=self.device)
self.humanoid_handles = []
self.envs = []
self.dof_limits_lower = []
self.dof_limits_upper = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
contact_filter = 0
handle = self.gym.create_actor(env_ptr, humanoid_asset, start_pose, "humanoid", i, contact_filter, 0)
self.gym.enable_actor_dof_force_sensors(env_ptr, handle)
for j in range(self.num_bodies):
self.gym.set_rigid_body_color(
env_ptr, handle, j, gymapi.MESH_VISUAL, gymapi.Vec3(0.4706, 0.549, 0.6863))
self.envs.append(env_ptr)
self.humanoid_handles.append(handle)
if (self._pd_control):
dof_prop = self.gym.get_asset_dof_properties(humanoid_asset)
dof_prop["driveMode"] = gymapi.DOF_MODE_POS
self.gym.set_actor_dof_properties(env_ptr, handle, dof_prop)
dof_prop = self.gym.get_actor_dof_properties(env_ptr, handle)
for j in range(self.num_dof):
if dof_prop['lower'][j] > dof_prop['upper'][j]:
self.dof_limits_lower.append(dof_prop['upper'][j])
self.dof_limits_upper.append(dof_prop['lower'][j])
else:
self.dof_limits_lower.append(dof_prop['lower'][j])
self.dof_limits_upper.append(dof_prop['upper'][j])
self.dof_limits_lower = to_torch(self.dof_limits_lower, device=self.device)
self.dof_limits_upper = to_torch(self.dof_limits_upper, device=self.device)
self._key_body_ids = self._build_key_body_ids_tensor(env_ptr, handle)
self._contact_body_ids = self._build_contact_body_ids_tensor(env_ptr, handle)
if (self._pd_control):
self._build_pd_action_offset_scale()
return
def _build_pd_action_offset_scale(self):
num_joints = len(DOF_OFFSETS) - 1
lim_low = self.dof_limits_lower.cpu().numpy()
lim_high = self.dof_limits_upper.cpu().numpy()
for j in range(num_joints):
dof_offset = DOF_OFFSETS[j]
dof_size = DOF_OFFSETS[j + 1] - DOF_OFFSETS[j]
if (dof_size == 3):
lim_low[dof_offset:(dof_offset + dof_size)] = -np.pi
lim_high[dof_offset:(dof_offset + dof_size)] = np.pi
elif (dof_size == 1):
curr_low = lim_low[dof_offset]
curr_high = lim_high[dof_offset]
curr_mid = 0.5 * (curr_high + curr_low)
# extend the action range to be a bit beyond the joint limits so that the motors
# don't lose their strength as they approach the joint limits
curr_scale = 0.7 * (curr_high - curr_low)
curr_low = curr_mid - curr_scale
curr_high = curr_mid + curr_scale
lim_low[dof_offset] = curr_low
lim_high[dof_offset] = curr_high
self._pd_action_offset = 0.5 * (lim_high + lim_low)
self._pd_action_scale = 0.5 * (lim_high - lim_low)
self._pd_action_offset = to_torch(self._pd_action_offset, device=self.device)
self._pd_action_scale = to_torch(self._pd_action_scale, device=self.device)
return
def _compute_reward(self, actions):
self.rew_buf[:] = compute_humanoid_reward(self.obs_buf)
return
def _compute_reset(self):
self.reset_buf[:], self._terminate_buf[:] = compute_humanoid_reset(self.reset_buf, self.progress_buf,
self._contact_forces, self._contact_body_ids,
self._rigid_body_pos, self.max_episode_length,
self._enable_early_termination, self._termination_height)
return
def _refresh_sim_tensors(self):
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
self.gym.refresh_force_sensor_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
return
def _compute_observations(self, env_ids=None):
obs = self._compute_humanoid_obs(env_ids)
if (env_ids is None):
self.obs_buf[:] = obs
else:
self.obs_buf[env_ids] = obs
return
def _compute_humanoid_obs(self, env_ids=None):
if (env_ids is None):
root_states = self._root_states
dof_pos = self._dof_pos
dof_vel = self._dof_vel
key_body_pos = self._rigid_body_pos[:, self._key_body_ids, :]
else:
root_states = self._root_states[env_ids]
dof_pos = self._dof_pos[env_ids]
dof_vel = self._dof_vel[env_ids]
key_body_pos = self._rigid_body_pos[env_ids][:, self._key_body_ids, :]
obs = compute_humanoid_observations(root_states, dof_pos, dof_vel,
key_body_pos, self._local_root_obs)
return obs
def _reset_actors(self, env_ids):
self._dof_pos[env_ids] = self._initial_dof_pos[env_ids]
self._dof_vel[env_ids] = self._initial_dof_vel[env_ids]
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self._initial_root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self._dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
self._terminate_buf[env_ids] = 0
return
def pre_physics_step(self, actions):
self.actions = actions.to(self.device).clone()
if (self._pd_control):
pd_tar = self._action_to_pd_targets(self.actions)
pd_tar_tensor = gymtorch.unwrap_tensor(pd_tar)
self.gym.set_dof_position_target_tensor(self.sim, pd_tar_tensor)
else:
forces = self.actions * self.motor_efforts.unsqueeze(0) * self.power_scale
force_tensor = gymtorch.unwrap_tensor(forces)
self.gym.set_dof_actuation_force_tensor(self.sim, force_tensor)
return
def post_physics_step(self):
self.progress_buf += 1
self._refresh_sim_tensors()
self._compute_observations()
self._compute_reward(self.actions)
self._compute_reset()
self.extras["terminate"] = self._terminate_buf
# debug viz
if self.viewer and self.debug_viz:
self._update_debug_viz()
return
def render(self):
if self.viewer and self.camera_follow:
self._update_camera()
super().render()
return
def _build_key_body_ids_tensor(self, env_ptr, actor_handle):
body_ids = []
for body_name in KEY_BODY_NAMES:
body_id = self.gym.find_actor_rigid_body_handle(env_ptr, actor_handle, body_name)
assert(body_id != -1)
body_ids.append(body_id)
body_ids = to_torch(body_ids, device=self.device, dtype=torch.long)
return body_ids
def _build_contact_body_ids_tensor(self, env_ptr, actor_handle):
body_ids = []
for body_name in self._contact_bodies:
body_id = self.gym.find_actor_rigid_body_handle(env_ptr, actor_handle, body_name)
assert(body_id != -1)
body_ids.append(body_id)
body_ids = to_torch(body_ids, device=self.device, dtype=torch.long)
return body_ids
def _action_to_pd_targets(self, action):
pd_tar = self._pd_action_offset + self._pd_action_scale * action
return pd_tar
def _init_camera(self):
self.gym.refresh_actor_root_state_tensor(self.sim)
self._cam_prev_char_pos = self._root_states[0, 0:3].cpu().numpy()
cam_pos = gymapi.Vec3(self._cam_prev_char_pos[0],
self._cam_prev_char_pos[1] - 3.0,
1.0)
cam_target = gymapi.Vec3(self._cam_prev_char_pos[0],
self._cam_prev_char_pos[1],
1.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
return
def _update_camera(self):
self.gym.refresh_actor_root_state_tensor(self.sim)
char_root_pos = self._root_states[0, 0:3].cpu().numpy()
cam_trans = self.gym.get_viewer_camera_transform(self.viewer, None)
cam_pos = np.array([cam_trans.p.x, cam_trans.p.y, cam_trans.p.z])
cam_delta = cam_pos - self._cam_prev_char_pos
new_cam_target = gymapi.Vec3(char_root_pos[0], char_root_pos[1], 1.0)
new_cam_pos = gymapi.Vec3(char_root_pos[0] + cam_delta[0],
char_root_pos[1] + cam_delta[1],
cam_pos[2])
self.gym.viewer_camera_look_at(self.viewer, None, new_cam_pos, new_cam_target)
self._cam_prev_char_pos[:] = char_root_pos
return
def _update_debug_viz(self):
self.gym.clear_lines(self.viewer)
return
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def dof_to_obs(pose):
# type: (Tensor) -> Tensor
#dof_obs_size = 64
#dof_offsets = [0, 3, 6, 9, 12, 13, 16, 19, 20, 23, 24, 27, 30, 31, 34]
dof_obs_size = 52
dof_offsets = [0, 3, 6, 9, 10, 13, 14, 17, 18, 21, 24, 25, 28]
num_joints = len(dof_offsets) - 1
dof_obs_shape = pose.shape[:-1] + (dof_obs_size,)
dof_obs = torch.zeros(dof_obs_shape, device=pose.device)
dof_obs_offset = 0
for j in range(num_joints):
dof_offset = dof_offsets[j]
dof_size = dof_offsets[j + 1] - dof_offsets[j]
joint_pose = pose[:, dof_offset:(dof_offset + dof_size)]
# assume this is a spherical joint
if (dof_size == 3):
joint_pose_q = exp_map_to_quat(joint_pose)
joint_dof_obs = quat_to_tan_norm(joint_pose_q)
dof_obs_size = 6
else:
joint_dof_obs = joint_pose
dof_obs_size = 1
dof_obs[:, dof_obs_offset:(dof_obs_offset + dof_obs_size)] = joint_dof_obs
dof_obs_offset += dof_obs_size
return dof_obs
@torch.jit.script
def compute_humanoid_observations(root_states, dof_pos, dof_vel, key_body_pos, local_root_obs):
# type: (Tensor, Tensor, Tensor, Tensor, bool) -> Tensor
root_pos = root_states[:, 0:3]
root_rot = root_states[:, 3:7]
root_vel = root_states[:, 7:10]
root_ang_vel = root_states[:, 10:13]
root_h = root_pos[:, 2:3]
heading_rot = calc_heading_quat_inv(root_rot)
if (local_root_obs):
root_rot_obs = quat_mul(heading_rot, root_rot)
else:
root_rot_obs = root_rot
root_rot_obs = quat_to_tan_norm(root_rot_obs)
local_root_vel = my_quat_rotate(heading_rot, root_vel)
local_root_ang_vel = my_quat_rotate(heading_rot, root_ang_vel)
root_pos_expand = root_pos.unsqueeze(-2)
local_key_body_pos = key_body_pos - root_pos_expand
heading_rot_expand = heading_rot.unsqueeze(-2)
heading_rot_expand = heading_rot_expand.repeat((1, local_key_body_pos.shape[1], 1))
flat_end_pos = local_key_body_pos.view(local_key_body_pos.shape[0] * local_key_body_pos.shape[1], local_key_body_pos.shape[2])
flat_heading_rot = heading_rot_expand.view(heading_rot_expand.shape[0] * heading_rot_expand.shape[1],
heading_rot_expand.shape[2])
local_end_pos = my_quat_rotate(flat_heading_rot, flat_end_pos)
flat_local_key_pos = local_end_pos.view(local_key_body_pos.shape[0], local_key_body_pos.shape[1] * local_key_body_pos.shape[2])
dof_obs = dof_to_obs(dof_pos)
obs = torch.cat((root_h, root_rot_obs, local_root_vel, local_root_ang_vel, dof_obs, dof_vel, flat_local_key_pos), dim=-1)
return obs
@torch.jit.script
def compute_humanoid_reward(obs_buf):
# type: (Tensor) -> Tensor
reward = torch.ones_like(obs_buf[:, 0])
return reward
@torch.jit.script
def compute_humanoid_reset(reset_buf, progress_buf, contact_buf, contact_body_ids, rigid_body_pos,
max_episode_length, enable_early_termination, termination_height):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, float, bool, float) -> Tuple[Tensor, Tensor]
terminated = torch.zeros_like(reset_buf)
if (enable_early_termination):
masked_contact_buf = contact_buf.clone()
masked_contact_buf[:, contact_body_ids, :] = 0
fall_contact = torch.any(masked_contact_buf > 0.1, dim=-1)
fall_contact = torch.any(fall_contact, dim=-1)
body_height = rigid_body_pos[..., 2]
fall_height = body_height < termination_height
fall_height[:, contact_body_ids] = False
fall_height = torch.any(fall_height, dim=-1)
has_fallen = torch.logical_and(fall_contact, fall_height)
# first timestep can sometimes still have nonzero contact forces
# so only check after first couple of steps
has_fallen *= (progress_buf > 1)
terminated = torch.where(has_fallen, torch.ones_like(reset_buf), terminated)
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), terminated)
return reset, terminated | 24,339 | Python | 42.309608 | 217 | 0.606147 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/tasks/amp/utils_amp/amp_torch_utils.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import torch
import numpy as np
from isaacgymenvs.utils.torch_jit_utils import quat_mul, quat_conjugate, quat_from_angle_axis, \
to_torch, get_axis_params, torch_rand_float, tensor_clamp
@torch.jit.script
def my_quat_rotate(q, v):
shape = q.shape
q_w = q[:, -1]
q_vec = q[:, :3]
a = v * (2.0 * q_w ** 2 - 1.0).unsqueeze(-1)
b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0
c = q_vec * \
torch.bmm(q_vec.view(shape[0], 1, 3), v.view(
shape[0], 3, 1)).squeeze(-1) * 2.0
return a + b + c
@torch.jit.script
def quat_to_angle_axis(q):
# type: (Tensor) -> Tuple[Tensor, Tensor]
# computes axis-angle representation from quaternion q
# q must be normalized
min_theta = 1e-5
qx, qy, qz, qw = 0, 1, 2, 3
sin_theta = torch.sqrt(1 - q[..., qw] * q[..., qw])
angle = 2 * torch.acos(q[..., qw])
angle = normalize_angle(angle)
sin_theta_expand = sin_theta.unsqueeze(-1)
axis = q[..., qx:qw] / sin_theta_expand
mask = sin_theta > min_theta
default_axis = torch.zeros_like(axis)
default_axis[..., -1] = 1
angle = torch.where(mask, angle, torch.zeros_like(angle))
mask_expand = mask.unsqueeze(-1)
axis = torch.where(mask_expand, axis, default_axis)
return angle, axis
@torch.jit.script
def angle_axis_to_exp_map(angle, axis):
# type: (Tensor, Tensor) -> Tensor
# compute exponential map from axis-angle
angle_expand = angle.unsqueeze(-1)
exp_map = angle_expand * axis
return exp_map
@torch.jit.script
def quat_to_exp_map(q):
# type: (Tensor) -> Tensor
# compute exponential map from quaternion
# q must be normalized
angle, axis = quat_to_angle_axis(q)
exp_map = angle_axis_to_exp_map(angle, axis)
return exp_map
@torch.jit.script
def quat_to_tan_norm(q):
# type: (Tensor) -> Tensor
# represents a rotation using the tangent and normal vectors
ref_tan = torch.zeros_like(q[..., 0:3])
ref_tan[..., 0] = 1
tan = my_quat_rotate(q, ref_tan)
ref_norm = torch.zeros_like(q[..., 0:3])
ref_norm[..., -1] = 1
norm = my_quat_rotate(q, ref_norm)
norm_tan = torch.cat([tan, norm], dim=len(tan.shape) - 1)
return norm_tan
@torch.jit.script
def euler_xyz_to_exp_map(roll, pitch, yaw):
# type: (Tensor, Tensor, Tensor) -> Tensor
q = quat_from_euler_xyz(roll, pitch, yaw)
exp_map = quat_to_exp_map(q)
return exp_map
@torch.jit.script
def exp_map_to_angle_axis(exp_map):
min_theta = 1e-5
angle = torch.norm(exp_map, dim=-1)
angle_exp = torch.unsqueeze(angle, dim=-1)
axis = exp_map / angle_exp
angle = normalize_angle(angle)
default_axis = torch.zeros_like(exp_map)
default_axis[..., -1] = 1
mask = angle > min_theta
angle = torch.where(mask, angle, torch.zeros_like(angle))
mask_expand = mask.unsqueeze(-1)
axis = torch.where(mask_expand, axis, default_axis)
return angle, axis
@torch.jit.script
def exp_map_to_quat(exp_map):
angle, axis = exp_map_to_angle_axis(exp_map)
q = quat_from_angle_axis(angle, axis)
return q
@torch.jit.script
def slerp(q0, q1, t):
# type: (Tensor, Tensor, Tensor) -> Tensor
qx, qy, qz, qw = 0, 1, 2, 3
cos_half_theta = q0[..., qw] * q1[..., qw] \
+ q0[..., qx] * q1[..., qx] \
+ q0[..., qy] * q1[..., qy] \
+ q0[..., qz] * q1[..., qz]
neg_mask = cos_half_theta < 0
q1 = q1.clone()
q1[neg_mask] = -q1[neg_mask]
cos_half_theta = torch.abs(cos_half_theta)
cos_half_theta = torch.unsqueeze(cos_half_theta, dim=-1)
half_theta = torch.acos(cos_half_theta);
sin_half_theta = torch.sqrt(1.0 - cos_half_theta * cos_half_theta);
ratioA = torch.sin((1 - t) * half_theta) / sin_half_theta;
ratioB = torch.sin(t * half_theta) / sin_half_theta;
new_q_x = ratioA * q0[..., qx:qx+1] + ratioB * q1[..., qx:qx+1]
new_q_y = ratioA * q0[..., qy:qy+1] + ratioB * q1[..., qy:qy+1]
new_q_z = ratioA * q0[..., qz:qz+1] + ratioB * q1[..., qz:qz+1]
new_q_w = ratioA * q0[..., qw:qw+1] + ratioB * q1[..., qw:qw+1]
cat_dim = len(new_q_w.shape) - 1
new_q = torch.cat([new_q_x, new_q_y, new_q_z, new_q_w], dim=cat_dim)
new_q = torch.where(torch.abs(sin_half_theta) < 0.001, 0.5 * q0 + 0.5 * q1, new_q)
new_q = torch.where(torch.abs(cos_half_theta) >= 1, q0, new_q)
return new_q
@torch.jit.script
def calc_heading(q):
# type: (Tensor) -> Tensor
# calculate heading direction from quaternion
# the heading is the direction on the xy plane
# q must be normalized
ref_dir = torch.zeros_like(q[..., 0:3])
ref_dir[..., 0] = 1
rot_dir = my_quat_rotate(q, ref_dir)
heading = torch.atan2(rot_dir[..., 1], rot_dir[..., 0])
return heading
@torch.jit.script
def calc_heading_quat(q):
# type: (Tensor) -> Tensor
# calculate heading rotation from quaternion
# the heading is the direction on the xy plane
# q must be normalized
heading = calc_heading(q)
axis = torch.zeros_like(q[..., 0:3])
axis[..., 2] = 1
heading_q = quat_from_angle_axis(heading, axis)
return heading_q
@torch.jit.script
def calc_heading_quat_inv(q):
# type: (Tensor) -> Tensor
# calculate heading rotation from quaternion
# the heading is the direction on the xy plane
# q must be normalized
heading = calc_heading(q)
axis = torch.zeros_like(q[..., 0:3])
axis[..., 2] = 1
heading_q = quat_from_angle_axis(-heading, axis)
return heading_q
| 7,111 | Python | 33.192308 | 96 | 0.63451 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/tasks/amp/utils_amp/data_tree.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import json
import copy
import os
from collections import OrderedDict
class data_tree(object):
def __init__(self, name):
self._name = name
self._children, self._children_names, self._picked, self._depleted = \
[], [], [], []
self._data, self._length = [], []
self._total_length, self._num_leaf, self._is_leaf = 0, 0, 0
self._assigned_prob = 0.0
def add_node(self, dict_hierachy, mocap_data):
# data_hierachy -> 'behavior' 'direction' 'type' 'style'
# behavior, direction, mocap_type, style = mocap_data[2:]
self._num_leaf += 1
if len(dict_hierachy) == 0:
# leaf node
self._data.append(mocap_data[0])
self._length.append(mocap_data[1])
self._picked.append(0)
self._depleted.append(0)
self._is_leaf = 1
else:
children_name = dict_hierachy[0].replace('\n', '')
if children_name not in self._children_names:
self._children_names.append(children_name)
self._children.append(data_tree(children_name))
self._picked.append(0)
self._depleted.append(0)
# add the data
index = self._children_names.index(children_name)
self._children[index].add_node(dict_hierachy[1:], mocap_data)
def summarize_length(self):
if self._is_leaf:
self._total_length = np.sum(self._length)
else:
self._total_length = 0
for i_child in self._children:
self._total_length += i_child.summarize_length()
return self._total_length
def to_dict(self, verbose=False):
if self._is_leaf:
self._data_dict = copy.deepcopy(self._data)
else:
self._data_dict = OrderedDict()
for i_child in self._children:
self._data_dict[i_child.name] = i_child.to_dict(verbose)
if verbose:
if self._is_leaf:
verbose_data_dict = []
for ii, i_key in enumerate(self._data_dict):
new_key = i_key + ' (picked {} / {})'.format(
str(self._picked[ii]), self._length[ii]
)
verbose_data_dict.append(new_key)
else:
verbose_data_dict = OrderedDict()
for ii, i_key in enumerate(self._data_dict):
new_key = i_key + ' (picked {} / {})'.format(
str(self._picked[ii]), self._children[ii].total_length
)
verbose_data_dict[new_key] = self._data_dict[i_key]
self._data_dict = verbose_data_dict
return self._data_dict
@property
def name(self):
return self._name
@property
def picked(self):
return self._picked
@property
def total_length(self):
return self._total_length
def water_floating_algorithm(self):
# find the sub class with the minimum picked
assert not np.all(self._depleted)
for ii in np.where(np.array(self._children_names) == 'mix')[0]:
self._depleted[ii] = np.inf
chosen_child = np.argmin(np.array(self._picked) +
np.array(self._depleted))
if self._is_leaf:
self._picked[chosen_child] = self._length[chosen_child]
self._depleted[chosen_child] = np.inf
chosen_data = self._data[chosen_child]
data_info = {'name': [self._name],
'length': self._length[chosen_child],
'all_depleted': np.all(self._depleted)}
else:
chosen_data, data_info = \
self._children[chosen_child].water_floating_algorithm()
self._picked[chosen_child] += data_info['length']
data_info['name'].insert(0, self._name)
if data_info['all_depleted']:
self._depleted[chosen_child] = np.inf
data_info['all_depleted'] = np.all(self._depleted)
return chosen_data, data_info
def assign_probability(self, total_prob):
# find the sub class with the minimum picked
leaves, probs = [], []
if self._is_leaf:
self._assigned_prob = total_prob
leaves.extend(self._data)
per_traj_prob = total_prob / float(len(self._data))
probs.extend([per_traj_prob] * len(self._data))
else:
per_child_prob = total_prob / float(len(self._children))
for i_child in self._children:
i_leave, i_prob = i_child.assign_probability(per_child_prob)
leaves.extend(i_leave)
probs.extend(i_prob)
return leaves, probs
def parse_dataset(env, args):
""" @brief: get the training set and test set
"""
TRAIN_PERCENTAGE = args.parse_dataset_train
info, motion = env.motion_info, env.motion
lengths = env.get_all_motion_length()
train_size = np.sum(motion.get_all_motion_length()) * TRAIN_PERCENTAGE
data_structure = data_tree('root')
shuffle_id = list(range(len(info['mocap_data_list'])))
np.random.shuffle(shuffle_id)
info['mocap_data_list'] = [info['mocap_data_list'][ii] for ii in shuffle_id]
for mocap_data, length in zip(info['mocap_data_list'], lengths[shuffle_id]):
node_data = [mocap_data[0]] + [length]
data_structure.add_node(mocap_data[2:], node_data)
raw_data_dict = data_structure.to_dict()
print(json.dumps(raw_data_dict, indent=4))
total_length = 0
chosen_data = []
while True:
i_data, i_info = data_structure.water_floating_algorithm()
print('Current length:', total_length, i_data, i_info)
total_length += i_info['length']
chosen_data.append(i_data)
if total_length > train_size:
break
data_structure.summarize_length()
data_dict = data_structure.to_dict(verbose=True)
print(json.dumps(data_dict, indent=4))
# save the training and test sets
train_data, test_data = [], []
for i_data in info['mocap_data_list']:
if i_data[0] in chosen_data:
train_data.append(i_data[1:])
else:
test_data.append(i_data[1:])
train_tsv_name = args.mocap_list_file.split('.')[0] + '_' + \
str(int(args.parse_dataset_train * 100)) + '_train' + '.tsv'
test_tsv_name = train_tsv_name.replace('train', 'test')
info_name = test_tsv_name.replace('test', 'info').replace('.tsv', '.json')
save_tsv_files(env._base_dir, train_tsv_name, train_data)
save_tsv_files(env._base_dir, test_tsv_name, test_data)
info_file = open(os.path.join(env._base_dir, 'experiments', 'mocap_files',
info_name), 'w')
json.dump(data_dict, info_file, indent=4)
def save_tsv_files(base_dir, name, data_dict):
file_name = os.path.join(base_dir, 'experiments', 'mocap_files', name)
recorder = open(file_name, "w")
for i_data in data_dict:
line = '{}\t{}\t{}\t{}\t{}\n'.format(*i_data)
recorder.write(line)
recorder.close() | 8,773 | Python | 38.522522 | 80 | 0.596489 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/tasks/amp/utils_amp/gym_util.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from . import logger
from isaacgym import gymapi
import numpy as np
import torch
from isaacgymenvs.utils.torch_jit_utils import scale, unscale, quat_mul, quat_conjugate, quat_from_angle_axis, \
to_torch, get_axis_params, torch_rand_float, tensor_clamp
from isaacgym import gymtorch
def setup_gym_viewer(config):
gym = initialize_gym(config)
sim, viewer = configure_gym(gym, config)
return gym, sim, viewer
def initialize_gym(config):
gym = gymapi.acquire_gym()
if not gym.initialize():
logger.warn("*** Failed to initialize gym")
quit()
return gym
def configure_gym(gym, config):
engine, render = config['engine'], config['render']
# physics engine settings
if(engine == 'FLEX'):
sim_engine = gymapi.SIM_FLEX
elif(engine == 'PHYSX'):
sim_engine = gymapi.SIM_PHYSX
else:
logger.warn("Unknown physics engine. defaulting to FLEX")
sim_engine = gymapi.SIM_FLEX
# gym viewer
if render:
# create viewer
sim = gym.create_sim(0, 0, sim_type=sim_engine)
viewer = gym.create_viewer(
sim, int(gymapi.DEFAULT_VIEWER_WIDTH / 1.25),
int(gymapi.DEFAULT_VIEWER_HEIGHT / 1.25)
)
if viewer is None:
logger.warn("*** Failed to create viewer")
quit()
# enable left mouse click or space bar for throwing projectiles
if config['add_projectiles']:
gym.subscribe_viewer_mouse_event(viewer, gymapi.MOUSE_LEFT_BUTTON, "shoot")
gym.subscribe_viewer_keyboard_event(viewer, gymapi.KEY_SPACE, "shoot")
else:
sim = gym.create_sim(0, -1)
viewer = None
# simulation params
scene_config = config['env']['scene']
sim_params = gymapi.SimParams()
sim_params.solver_type = scene_config['SolverType']
sim_params.num_outer_iterations = scene_config['NumIterations']
sim_params.num_inner_iterations = scene_config['NumInnerIterations']
sim_params.relaxation = scene_config.get('Relaxation', 0.75)
sim_params.warm_start = scene_config.get('WarmStart', 0.25)
sim_params.geometric_stiffness = scene_config.get('GeometricStiffness', 1.0)
sim_params.shape_collision_margin = 0.01
sim_params.gravity = gymapi.Vec3(0.0, -9.8, 0.0)
gym.set_sim_params(sim, sim_params)
return sim, viewer
def parse_states_from_reference_states(reference_states, progress):
# parse reference states from DeepMimicState
global_quats_ref = torch.tensor(
reference_states._global_rotation[(progress,)].numpy(),
dtype=torch.double
).cuda()
ts_ref = torch.tensor(
reference_states._translation[(progress,)].numpy(),
dtype=torch.double
).cuda()
vels_ref = torch.tensor(
reference_states._velocity[(progress,)].numpy(),
dtype=torch.double
).cuda()
avels_ref = torch.tensor(
reference_states._angular_velocity[(progress,)].numpy(),
dtype=torch.double
).cuda()
return global_quats_ref, ts_ref, vels_ref, avels_ref
def parse_states_from_reference_states_with_motion_id(precomputed_state,
progress, motion_id):
assert len(progress) == len(motion_id)
# get the global id
global_id = precomputed_state['motion_offset'][motion_id] + progress
global_id = np.minimum(global_id,
precomputed_state['global_quats_ref'].shape[0] - 1)
# parse reference states from DeepMimicState
global_quats_ref = precomputed_state['global_quats_ref'][global_id]
ts_ref = precomputed_state['ts_ref'][global_id]
vels_ref = precomputed_state['vels_ref'][global_id]
avels_ref = precomputed_state['avels_ref'][global_id]
return global_quats_ref, ts_ref, vels_ref, avels_ref
def parse_dof_state_with_motion_id(precomputed_state, dof_state,
progress, motion_id):
assert len(progress) == len(motion_id)
# get the global id
global_id = precomputed_state['motion_offset'][motion_id] + progress
# NOTE: it should never reach the dof_state.shape, cause the episode is
# terminated 2 steps before
global_id = np.minimum(global_id, dof_state.shape[0] - 1)
# parse reference states from DeepMimicState
return dof_state[global_id]
def get_flatten_ids(precomputed_state):
motion_offsets = precomputed_state['motion_offset']
init_state_id, init_motion_id, global_id = [], [], []
for i_motion in range(len(motion_offsets) - 1):
i_length = motion_offsets[i_motion + 1] - motion_offsets[i_motion]
init_state_id.extend(range(i_length))
init_motion_id.extend([i_motion] * i_length)
if len(global_id) == 0:
global_id.extend(range(0, i_length))
else:
global_id.extend(range(global_id[-1] + 1,
global_id[-1] + i_length + 1))
return np.array(init_state_id), np.array(init_motion_id), \
np.array(global_id)
def parse_states_from_reference_states_with_global_id(precomputed_state,
global_id):
# get the global id
global_id = global_id % precomputed_state['global_quats_ref'].shape[0]
# parse reference states from DeepMimicState
global_quats_ref = precomputed_state['global_quats_ref'][global_id]
ts_ref = precomputed_state['ts_ref'][global_id]
vels_ref = precomputed_state['vels_ref'][global_id]
avels_ref = precomputed_state['avels_ref'][global_id]
return global_quats_ref, ts_ref, vels_ref, avels_ref
def get_robot_states_from_torch_tensor(config, ts, global_quats, vels, avels,
init_rot, progress, motion_length=-1,
actions=None, relative_rot=None,
motion_id=None, num_motion=None,
motion_onehot_matrix=None):
info = {}
# the observation with quaternion-based representation
torso_height = ts[..., 0, 1].cpu().numpy()
gttrny, gqny, vny, avny, info['root_yaw_inv'] = \
quaternion_math.compute_observation_return_info(global_quats, ts,
vels, avels)
joint_obs = np.concatenate([gttrny.cpu().numpy(), gqny.cpu().numpy(),
vny.cpu().numpy(), avny.cpu().numpy()], axis=-1)
joint_obs = joint_obs.reshape(joint_obs.shape[0], -1)
num_envs = joint_obs.shape[0]
obs = np.concatenate([torso_height[:, np.newaxis], joint_obs], -1)
# the previous action
if config['env_action_ob']:
obs = np.concatenate([obs, actions], axis=-1)
# the orientation
if config['env_orientation_ob']:
if relative_rot is not None:
obs = np.concatenate([obs, relative_rot], axis=-1)
else:
curr_rot = global_quats[np.arange(num_envs)][:, 0]
curr_rot = curr_rot.reshape(num_envs, -1, 4)
relative_rot = quaternion_math.compute_orientation_drift(
init_rot, curr_rot
).cpu().numpy()
obs = np.concatenate([obs, relative_rot], axis=-1)
if config['env_frame_ob']:
if type(motion_length) == np.ndarray:
motion_length = motion_length.astype(float)
progress_ob = np.expand_dims(progress.astype(float) /
motion_length, axis=-1)
else:
progress_ob = np.expand_dims(progress.astype(float) /
float(motion_length), axis=-1)
obs = np.concatenate([obs, progress_ob], axis=-1)
if config['env_motion_ob'] and not config['env_motion_ob_onehot']:
motion_id_ob = np.expand_dims(motion_id.astype(float) /
float(num_motion), axis=-1)
obs = np.concatenate([obs, motion_id_ob], axis=-1)
elif config['env_motion_ob'] and config['env_motion_ob_onehot']:
motion_id_ob = motion_onehot_matrix[motion_id]
obs = np.concatenate([obs, motion_id_ob], axis=-1)
return obs, info
def get_xyzoffset(start_ts, end_ts, root_yaw_inv):
xyoffset = (end_ts - start_ts)[:, [0], :].reshape(1, -1, 1, 3)
ryinv = root_yaw_inv.reshape(1, -1, 1, 4)
calibrated_xyz_offset = quaternion_math.quat_apply(ryinv, xyoffset)[0, :, 0, :]
return calibrated_xyz_offset
| 9,996 | Python | 39.971311 | 112 | 0.632753 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/tasks/amp/utils_amp/__init__.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
| 1,558 | Python | 54.678569 | 80 | 0.784339 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/tasks/amp/utils_amp/motion_lib.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import yaml
from ..poselib.poselib.skeleton.skeleton3d import SkeletonMotion
from ..poselib.poselib.core.rotation3d import *
from isaacgymenvs.utils.torch_jit_utils import to_torch, slerp, quat_to_exp_map, quat_to_angle_axis, normalize_angle
from isaacgymenvs.tasks.amp.humanoid_amp_base import DOF_BODY_IDS, DOF_OFFSETS
class MotionLib():
def __init__(self, motion_file, num_dofs, key_body_ids, device):
self._num_dof = num_dofs
self._key_body_ids = key_body_ids
self._device = device
self._load_motions(motion_file)
self.motion_ids = torch.arange(len(self._motions), dtype=torch.long, device=self._device)
return
def num_motions(self):
return len(self._motions)
def get_total_length(self):
return sum(self._motion_lengths)
def get_motion(self, motion_id):
return self._motions[motion_id]
def sample_motions(self, n):
m = self.num_motions()
motion_ids = np.random.choice(m, size=n, replace=True, p=self._motion_weights)
return motion_ids
def sample_time(self, motion_ids, truncate_time=None):
n = len(motion_ids)
phase = np.random.uniform(low=0.0, high=1.0, size=motion_ids.shape)
motion_len = self._motion_lengths[motion_ids]
if (truncate_time is not None):
assert(truncate_time >= 0.0)
motion_len -= truncate_time
motion_time = phase * motion_len
return motion_time
def get_motion_length(self, motion_ids):
return self._motion_lengths[motion_ids]
def get_motion_state(self, motion_ids, motion_times):
n = len(motion_ids)
num_bodies = self._get_num_bodies()
num_key_bodies = self._key_body_ids.shape[0]
root_pos0 = np.empty([n, 3])
root_pos1 = np.empty([n, 3])
root_rot = np.empty([n, 4])
root_rot0 = np.empty([n, 4])
root_rot1 = np.empty([n, 4])
root_vel = np.empty([n, 3])
root_ang_vel = np.empty([n, 3])
local_rot0 = np.empty([n, num_bodies, 4])
local_rot1 = np.empty([n, num_bodies, 4])
dof_vel = np.empty([n, self._num_dof])
key_pos0 = np.empty([n, num_key_bodies, 3])
key_pos1 = np.empty([n, num_key_bodies, 3])
motion_len = self._motion_lengths[motion_ids]
num_frames = self._motion_num_frames[motion_ids]
dt = self._motion_dt[motion_ids]
frame_idx0, frame_idx1, blend = self._calc_frame_blend(motion_times, motion_len, num_frames, dt)
unique_ids = np.unique(motion_ids)
for uid in unique_ids:
ids = np.where(motion_ids == uid)
curr_motion = self._motions[uid]
root_pos0[ids, :] = curr_motion.global_translation[frame_idx0[ids], 0].numpy()
root_pos1[ids, :] = curr_motion.global_translation[frame_idx1[ids], 0].numpy()
root_rot0[ids, :] = curr_motion.global_rotation[frame_idx0[ids], 0].numpy()
root_rot1[ids, :] = curr_motion.global_rotation[frame_idx1[ids], 0].numpy()
local_rot0[ids, :, :]= curr_motion.local_rotation[frame_idx0[ids]].numpy()
local_rot1[ids, :, :] = curr_motion.local_rotation[frame_idx1[ids]].numpy()
root_vel[ids, :] = curr_motion.global_root_velocity[frame_idx0[ids]].numpy()
root_ang_vel[ids, :] = curr_motion.global_root_angular_velocity[frame_idx0[ids]].numpy()
key_pos0[ids, :, :] = curr_motion.global_translation[frame_idx0[ids][:, np.newaxis], self._key_body_ids[np.newaxis, :]].numpy()
key_pos1[ids, :, :] = curr_motion.global_translation[frame_idx1[ids][:, np.newaxis], self._key_body_ids[np.newaxis, :]].numpy()
dof_vel[ids, :] = curr_motion.dof_vels[frame_idx0[ids]]
blend = to_torch(np.expand_dims(blend, axis=-1), device=self._device)
root_pos0 = to_torch(root_pos0, device=self._device)
root_pos1 = to_torch(root_pos1, device=self._device)
root_rot0 = to_torch(root_rot0, device=self._device)
root_rot1 = to_torch(root_rot1, device=self._device)
root_vel = to_torch(root_vel, device=self._device)
root_ang_vel = to_torch(root_ang_vel, device=self._device)
local_rot0 = to_torch(local_rot0, device=self._device)
local_rot1 = to_torch(local_rot1, device=self._device)
key_pos0 = to_torch(key_pos0, device=self._device)
key_pos1 = to_torch(key_pos1, device=self._device)
dof_vel = to_torch(dof_vel, device=self._device)
root_pos = (1.0 - blend) * root_pos0 + blend * root_pos1
root_rot = slerp(root_rot0, root_rot1, blend)
blend_exp = blend.unsqueeze(-1)
key_pos = (1.0 - blend_exp) * key_pos0 + blend_exp * key_pos1
local_rot = slerp(local_rot0, local_rot1, torch.unsqueeze(blend, axis=-1))
dof_pos = self._local_rotation_to_dof(local_rot)
return root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel, key_pos
def _load_motions(self, motion_file):
self._motions = []
self._motion_lengths = []
self._motion_weights = []
self._motion_fps = []
self._motion_dt = []
self._motion_num_frames = []
self._motion_files = []
total_len = 0.0
motion_files, motion_weights = self._fetch_motion_files(motion_file)
num_motion_files = len(motion_files)
for f in range(num_motion_files):
curr_file = motion_files[f]
print("Loading {:d}/{:d} motion files: {:s}".format(f + 1, num_motion_files, curr_file))
curr_motion = SkeletonMotion.from_file(curr_file)
motion_fps = curr_motion.fps
curr_dt = 1.0 / motion_fps
num_frames = curr_motion.tensor.shape[0]
curr_len = 1.0 / motion_fps * (num_frames - 1)
self._motion_fps.append(motion_fps)
self._motion_dt.append(curr_dt)
self._motion_num_frames.append(num_frames)
curr_dof_vels = self._compute_motion_dof_vels(curr_motion)
curr_motion.dof_vels = curr_dof_vels
self._motions.append(curr_motion)
self._motion_lengths.append(curr_len)
curr_weight = motion_weights[f]
self._motion_weights.append(curr_weight)
self._motion_files.append(curr_file)
self._motion_lengths = np.array(self._motion_lengths)
self._motion_weights = np.array(self._motion_weights)
self._motion_weights /= np.sum(self._motion_weights)
self._motion_fps = np.array(self._motion_fps)
self._motion_dt = np.array(self._motion_dt)
self._motion_num_frames = np.array(self._motion_num_frames)
num_motions = self.num_motions()
total_len = self.get_total_length()
print("Loaded {:d} motions with a total length of {:.3f}s.".format(num_motions, total_len))
return
def _fetch_motion_files(self, motion_file):
ext = os.path.splitext(motion_file)[1]
if (ext == ".yaml"):
dir_name = os.path.dirname(motion_file)
motion_files = []
motion_weights = []
with open(os.path.join(os.getcwd(), motion_file), 'r') as f:
motion_config = yaml.load(f, Loader=yaml.SafeLoader)
motion_list = motion_config['motions']
for motion_entry in motion_list:
curr_file = motion_entry['file']
curr_weight = motion_entry['weight']
assert(curr_weight >= 0)
curr_file = os.path.join(dir_name, curr_file)
motion_weights.append(curr_weight)
motion_files.append(curr_file)
else:
motion_files = [motion_file]
motion_weights = [1.0]
return motion_files, motion_weights
def _calc_frame_blend(self, time, len, num_frames, dt):
phase = time / len
phase = np.clip(phase, 0.0, 1.0)
frame_idx0 = (phase * (num_frames - 1)).astype(int)
frame_idx1 = np.minimum(frame_idx0 + 1, num_frames - 1)
blend = (time - frame_idx0 * dt) / dt
return frame_idx0, frame_idx1, blend
def _get_num_bodies(self):
motion = self.get_motion(0)
num_bodies = motion.num_joints
return num_bodies
def _compute_motion_dof_vels(self, motion):
num_frames = motion.tensor.shape[0]
dt = 1.0 / motion.fps
dof_vels = []
for f in range(num_frames - 1):
local_rot0 = motion.local_rotation[f]
local_rot1 = motion.local_rotation[f + 1]
frame_dof_vel = self._local_rotation_to_dof_vel(local_rot0, local_rot1, dt)
frame_dof_vel = frame_dof_vel
dof_vels.append(frame_dof_vel)
dof_vels.append(dof_vels[-1])
dof_vels = np.array(dof_vels)
return dof_vels
def _local_rotation_to_dof(self, local_rot):
body_ids = DOF_BODY_IDS
dof_offsets = DOF_OFFSETS
n = local_rot.shape[0]
dof_pos = torch.zeros((n, self._num_dof), dtype=torch.float, device=self._device)
for j in range(len(body_ids)):
body_id = body_ids[j]
joint_offset = dof_offsets[j]
joint_size = dof_offsets[j + 1] - joint_offset
if (joint_size == 3):
joint_q = local_rot[:, body_id]
joint_exp_map = quat_to_exp_map(joint_q)
dof_pos[:, joint_offset:(joint_offset + joint_size)] = joint_exp_map
elif (joint_size == 1):
joint_q = local_rot[:, body_id]
joint_theta, joint_axis = quat_to_angle_axis(joint_q)
joint_theta = joint_theta * joint_axis[..., 1] # assume joint is always along y axis
joint_theta = normalize_angle(joint_theta)
dof_pos[:, joint_offset] = joint_theta
else:
print("Unsupported joint type")
assert(False)
return dof_pos
def _local_rotation_to_dof_vel(self, local_rot0, local_rot1, dt):
body_ids = DOF_BODY_IDS
dof_offsets = DOF_OFFSETS
dof_vel = np.zeros([self._num_dof])
diff_quat_data = quat_mul_norm(quat_inverse(local_rot0), local_rot1)
diff_angle, diff_axis = quat_angle_axis(diff_quat_data)
local_vel = diff_axis * diff_angle.unsqueeze(-1) / dt
local_vel = local_vel.numpy()
for j in range(len(body_ids)):
body_id = body_ids[j]
joint_offset = dof_offsets[j]
joint_size = dof_offsets[j + 1] - joint_offset
if (joint_size == 3):
joint_vel = local_vel[body_id]
dof_vel[joint_offset:(joint_offset + joint_size)] = joint_vel
elif (joint_size == 1):
assert(joint_size == 1)
joint_vel = local_vel[body_id]
dof_vel[joint_offset] = joint_vel[1] # assume joint is always along y axis
else:
print("Unsupported joint type")
assert(False)
return dof_vel | 12,738 | Python | 38.317901 | 139 | 0.600879 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/tasks/amp/utils_amp/logger.py | # -----------------------------------------------------------------------------
# @brief:
# The logger here will be called all across the project. It is inspired
# by Yuxin Wu ([email protected])
#
# @author:
# Tingwu Wang, 2017, Feb, 20th
# -----------------------------------------------------------------------------
import logging
import sys
import os
import datetime
__all__ = ['set_file_handler'] # the actual worker is the '_logger'
color2id = {"grey": 30, "red": 31, "green": 32, "yellow": 33, "blue": 34, "magenta": 35, "cyan": 36, "white": 37}
def colored(text, color):
return f"\033[{color2id[color]}m{text}\033[0m"
class _MyFormatter(logging.Formatter):
'''
@brief:
a class to make sure the format could be used
'''
def format(self, record):
date = colored('[%(asctime)s @%(filename)s:%(lineno)d]', 'green')
msg = '%(message)s'
if record.levelno == logging.WARNING:
fmt = date + ' ' + \
colored('WRN', 'red', attrs=[]) + ' ' + msg
elif record.levelno == logging.ERROR or \
record.levelno == logging.CRITICAL:
fmt = date + ' ' + \
colored('ERR', 'red', attrs=['underline']) + ' ' + msg
else:
fmt = date + ' ' + msg
if hasattr(self, '_style'):
# Python3 compatibility
self._style._fmt = fmt
self._fmt = fmt
return super(self.__class__, self).format(record)
_logger = logging.getLogger('joint_embedding')
_logger.propagate = False
_logger.setLevel(logging.INFO)
# set the console output handler
con_handler = logging.StreamHandler(sys.stdout)
con_handler.setFormatter(_MyFormatter(datefmt='%m%d %H:%M:%S'))
_logger.addHandler(con_handler)
class GLOBAL_PATH(object):
def __init__(self, path=None):
if path is None:
path = os.getcwd()
self.path = path
def _set_path(self, path):
self.path = path
def _get_path(self):
return self.path
PATH = GLOBAL_PATH()
def set_file_handler(path=None, prefix='', time_str=''):
# set the file output handler
if time_str == '':
file_name = prefix + \
datetime.datetime.now().strftime("%A_%d_%B_%Y_%I:%M%p") + '.log'
else:
file_name = prefix + time_str + '.log'
if path is None:
mod = sys.modules['__main__']
path = os.path.join(os.path.abspath(mod.__file__), '..', '..', 'log')
else:
path = os.path.join(path, 'log')
path = os.path.abspath(path)
path = os.path.join(path, file_name)
if not os.path.exists(path):
os.makedirs(path)
PATH._set_path(path)
path = os.path.join(path, file_name)
from tensorboard_logger import configure
configure(path)
file_handler = logging.FileHandler(
filename=os.path.join(path, 'logger'), encoding='utf-8', mode='w')
file_handler.setFormatter(_MyFormatter(datefmt='%m%d %H:%M:%S'))
_logger.addHandler(file_handler)
_logger.info('Log file set to {}'.format(path))
return path
def _get_path():
return PATH._get_path()
_LOGGING_METHOD = ['info', 'warning', 'error', 'critical',
'warn', 'exception', 'debug']
# export logger functions
for func in _LOGGING_METHOD:
locals()[func] = getattr(_logger, func)
| 3,351 | Python | 26.47541 | 113 | 0.549388 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/pbt.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import os
import random
import shutil
import sys
import time
from os.path import join
from typing import Any, Dict, List, Optional
import numpy as np
import torch
import yaml
from omegaconf import DictConfig
from rl_games.algos_torch.torch_ext import safe_filesystem_op, safe_save
from rl_games.common.algo_observer import AlgoObserver
from isaacgymenvs.pbt.mutation import mutate
from isaacgymenvs.utils.reformat import omegaconf_to_dict
from isaacgymenvs.utils.utils import flatten_dict, project_tmp_dir, safe_ensure_dir_exists
# i.e. value for target objective when it is not known
_UNINITIALIZED_VALUE = float(-1e9)
def _checkpnt_name(iteration):
return f"{iteration:06d}.yaml"
def _model_checkpnt_name(iteration):
return f"{iteration:06d}.pth"
def _flatten_params(params: Dict, prefix="", separator=".") -> Dict:
all_params = flatten_dict(params, prefix, separator)
return all_params
def _filter_params(params: Dict, params_to_mutate: Dict) -> Dict:
filtered_params = dict()
for key, value in params.items():
if key in params_to_mutate:
if isinstance(value, str):
try:
# trying to convert values such as "1e-4" to floats because yaml fails to recognize them as such
float_value = float(value)
value = float_value
except ValueError:
pass
filtered_params[key] = value
return filtered_params
class PbtParams:
def __init__(self, cfg: DictConfig):
params: Dict = omegaconf_to_dict(cfg)
pbt_params = params["pbt"]
self.replace_fraction_best = pbt_params["replace_fraction_best"]
self.replace_fraction_worst = pbt_params["replace_fraction_worst"]
self.replace_threshold_frac_std = pbt_params["replace_threshold_frac_std"]
self.replace_threshold_frac_absolute = pbt_params["replace_threshold_frac_absolute"]
self.mutation_rate = pbt_params["mutation_rate"]
self.change_min = pbt_params["change_min"]
self.change_max = pbt_params["change_max"]
self.task_name = params["task"]["name"]
self.dbg_mode = pbt_params["dbg_mode"]
self.policy_idx = pbt_params["policy_idx"]
self.num_policies = pbt_params["num_policies"]
self.num_envs = params["task"]["env"]["numEnvs"]
self.workspace = pbt_params["workspace"]
self.interval_steps = pbt_params["interval_steps"]
self.start_after_steps = pbt_params["start_after"]
self.initial_delay_steps = pbt_params["initial_delay"]
self.params_to_mutate = pbt_params["mutation"]
mutable_params = _flatten_params(params)
self.mutable_params = _filter_params(mutable_params, self.params_to_mutate)
self.with_wandb = params["wandb_activate"]
RLAlgo = Any # just for readability
def _restart_process_with_new_params(
policy_idx: int,
new_params: Dict,
restart_from_checkpoint: Optional[str],
experiment_name: Optional[str],
algo: Optional[RLAlgo],
with_wandb: bool,
) -> None:
cli_args = sys.argv
modified_args = [cli_args[0]] # initialize with path to the Python script
for arg in cli_args[1:]:
if "=" not in arg:
modified_args.append(arg)
else:
assert "=" in arg
arg_name, arg_value = arg.split("=")
if arg_name in new_params or arg_name in [
"checkpoint",
"+full_experiment_name",
"hydra.run.dir",
"++pbt_restart",
]:
# skip this parameter, it will be added later!
continue
modified_args.append(f"{arg_name}={arg_value}")
modified_args.append(f"hydra.run.dir={os.getcwd()}")
modified_args.append(f"++pbt_restart=True")
if experiment_name is not None:
modified_args.append(f"+full_experiment_name={experiment_name}")
if restart_from_checkpoint is not None:
modified_args.append(f"checkpoint={restart_from_checkpoint}")
# add all the new (possibly mutated) parameters
for param, value in new_params.items():
modified_args.append(f"{param}={value}")
if algo is not None:
algo.writer.flush()
algo.writer.close()
if with_wandb:
try:
import wandb
wandb.run.finish()
except Exception as exc:
print(f"Policy {policy_idx}: Exception {exc} in wandb.run.finish()")
return
print(f"Policy {policy_idx}: Restarting self with args {modified_args}", flush=True)
os.execv(sys.executable, ["python3"] + modified_args)
def initial_pbt_check(cfg: DictConfig):
assert cfg.pbt.enabled
if hasattr(cfg, "pbt_restart") and cfg.pbt_restart:
print(f"PBT job restarted from checkpoint, keep going...")
return
print("PBT run without 'pbt_restart=True' - must be the very start of the experiment!")
print("Mutating initial set of hyperparameters!")
pbt_params = PbtParams(cfg)
new_params = mutate(
pbt_params.mutable_params,
pbt_params.params_to_mutate,
pbt_params.mutation_rate,
pbt_params.change_min,
pbt_params.change_max,
)
_restart_process_with_new_params(pbt_params.policy_idx, new_params, None, None, None, False)
class PbtAlgoObserver(AlgoObserver):
def __init__(self, cfg: DictConfig):
super().__init__()
self.pbt_params: PbtParams = PbtParams(cfg)
self.policy_idx: int = self.pbt_params.policy_idx
self.num_envs: int = self.pbt_params.num_envs
self.pbt_num_policies: int = self.pbt_params.num_policies
self.algo: Optional[RLAlgo] = None
self.pbt_workspace_dir = self.curr_policy_workspace_dir = None
self.pbt_iteration = -1 # dummy value, stands for "not initialized"
self.initial_env_frames = -1 # env frames at the beginning of the experiment, can be > 0 if we resume
self.finished_agents = set()
self.last_target_objectives = [_UNINITIALIZED_VALUE] * self.pbt_params.num_envs
self.curr_target_objective_value: float = _UNINITIALIZED_VALUE
self.target_objective_known = False # switch to true when we have enough data to calculate target objective
# keep track of objective values in the current iteration
# we use best value reached in the current iteration to decide whether to be replaced by another policy
# this reduces the noisiness of evolutionary pressure by reducing the number of situations where a policy
# gets replaced just due to a random minor dip in performance
self.best_objective_curr_iteration: Optional[float] = None
self.experiment_start = time.time()
self.with_wandb = self.pbt_params.with_wandb
def after_init(self, algo):
self.algo = algo
self.pbt_workspace_dir = join(algo.train_dir, self.pbt_params.workspace)
self.curr_policy_workspace_dir = self._policy_workspace_dir(self.pbt_params.policy_idx)
os.makedirs(self.curr_policy_workspace_dir, exist_ok=True)
def process_infos(self, infos, done_indices):
if "true_objective" in infos:
done_indices_lst = done_indices.squeeze(-1).tolist()
self.finished_agents.update(done_indices_lst)
for done_idx in done_indices_lst:
true_objective_value = infos["true_objective"][done_idx].item()
self.last_target_objectives[done_idx] = true_objective_value
# last result for all episodes
self.target_objective_known = len(self.finished_agents) >= self.pbt_params.num_envs
if self.target_objective_known:
self.curr_target_objective_value = float(np.mean(self.last_target_objectives))
else:
# environment does not specify "true objective", use regular reward
# in this case, be careful not to include reward shaping coefficients into the mutation config
self.target_objective_known = self.algo.game_rewards.current_size >= self.algo.games_to_track
if self.target_objective_known:
self.curr_target_objective_value = float(self.algo.mean_rewards)
if self.target_objective_known:
if (
self.best_objective_curr_iteration is None
or self.curr_target_objective_value > self.best_objective_curr_iteration
):
print(
f"Policy {self.policy_idx}: New best objective value {self.curr_target_objective_value} in iteration {self.pbt_iteration}"
)
self.best_objective_curr_iteration = self.curr_target_objective_value
def after_steps(self):
if self.pbt_iteration == -1:
self.pbt_iteration = self.algo.frame // self.pbt_params.interval_steps
self.initial_env_frames = self.algo.frame
print(
f"Policy {self.policy_idx}: PBT init. Env frames: {self.algo.frame}, pbt_iteration: {self.pbt_iteration}"
)
env_frames: int = self.algo.frame
iteration = env_frames // self.pbt_params.interval_steps
print(
f"Policy {self.policy_idx}: Env frames {env_frames}, iteration {iteration}, self iteration {self.pbt_iteration}"
)
if iteration <= self.pbt_iteration:
return
if not self.target_objective_known:
# not enough data yet to calcuate avg true_objective
print(
f"Policy {self.policy_idx}: Not enough episodes finished, wait for more data ({len(self.finished_agents)}/{self.num_envs})..."
)
return
assert self.curr_target_objective_value != _UNINITIALIZED_VALUE
assert self.best_objective_curr_iteration is not None
best_objective_curr_iteration: float = self.best_objective_curr_iteration
# reset for the next iteration
self.best_objective_curr_iteration = None
self.target_objective_known = False
sec_since_experiment_start = time.time() - self.experiment_start
pbt_start_after_sec = 1 if self.pbt_params.dbg_mode else 30
if sec_since_experiment_start < pbt_start_after_sec:
print(
f"Policy {self.policy_idx}: Not enough time passed since experiment start {sec_since_experiment_start}"
)
return
print(f"Policy {self.policy_idx}: New pbt iteration {iteration}!")
self.pbt_iteration = iteration
try:
self._save_pbt_checkpoint()
except Exception as exc:
print(f"Policy {self.policy_idx}: Exception {exc} when saving PBT checkpoint!")
return
try:
checkpoints = self._load_population_checkpoints()
except Exception as exc:
print(f"Policy {self.policy_idx}: Exception {exc} when loading checkpoints!")
return
try:
self._cleanup(checkpoints)
except Exception as exc:
print(f"Policy {self.policy_idx}: Exception {exc} during cleanup!")
policies = list(range(self.pbt_num_policies))
target_objectives = []
for p in policies:
if checkpoints[p] is None:
target_objectives.append(_UNINITIALIZED_VALUE)
else:
target_objectives.append(checkpoints[p]["true_objective"])
policies_sorted = sorted(zip(target_objectives, policies), reverse=True)
objectives = [objective for objective, p in policies_sorted]
best_objective = objectives[0]
policies_sorted = [p for objective, p in policies_sorted]
best_policy = policies_sorted[0]
self._maybe_save_best_policy(best_objective, best_policy, checkpoints[best_policy])
objectives_filtered = [o for o in objectives if o > _UNINITIALIZED_VALUE]
try:
self._pbt_summaries(self.pbt_params.mutable_params, best_objective)
except Exception as exc:
print(f"Policy {self.policy_idx}: Exception {exc} when writing summaries!")
return
if (
env_frames - self.initial_env_frames < self.pbt_params.start_after_steps
or env_frames < self.pbt_params.initial_delay_steps
):
print(
f"Policy {self.policy_idx}: Not enough experience collected to replace weights. "
f"Giving this policy more time to adjust to the latest parameters... "
f"env_frames={env_frames} started_at={self.initial_env_frames} "
f"restart_delay={self.pbt_params.start_after_steps} initial_delay={self.pbt_params.initial_delay_steps}"
)
return
replace_worst = math.ceil(self.pbt_params.replace_fraction_worst * self.pbt_num_policies)
replace_best = math.ceil(self.pbt_params.replace_fraction_best * self.pbt_num_policies)
best_policies = policies_sorted[:replace_best]
worst_policies = policies_sorted[-replace_worst:]
print(f"Policy {self.policy_idx}: PBT best_policies={best_policies}, worst_policies={worst_policies}")
if self.policy_idx not in worst_policies and not self.pbt_params.dbg_mode:
# don't touch the policies that are doing okay
print(f"Current policy {self.policy_idx} is doing well, not among the worst_policies={worst_policies}")
return
if best_objective_curr_iteration is not None and not self.pbt_params.dbg_mode:
if best_objective_curr_iteration >= min(objectives[:replace_best]):
print(
f"Policy {self.policy_idx}: best_objective={best_objective_curr_iteration} "
f"is better than some of the top policies {objectives[:replace_best]}. "
f"This policy should keep training for now, it is doing okay."
)
return
if len(objectives_filtered) <= max(2, self.pbt_num_policies // 2) and not self.pbt_params.dbg_mode:
print(f"Policy {self.policy_idx}: Not enough data to start PBT, {objectives_filtered}")
return
print(f"Current policy {self.policy_idx} is among the worst_policies={worst_policies}, consider replacing weights")
print(
f"Policy {self.policy_idx} objective: {self.curr_target_objective_value}, best_objective={best_objective} (best_policy={best_policy})."
)
replacement_policy_candidate = random.choice(best_policies)
candidate_objective = checkpoints[replacement_policy_candidate]["true_objective"]
targ_objective_value = self.curr_target_objective_value
objective_delta = candidate_objective - targ_objective_value
num_outliers = int(math.floor(0.2 * len(objectives_filtered)))
print(f"Policy {self.policy_idx} num outliers: {num_outliers}")
if len(objectives_filtered) > num_outliers:
objectives_filtered_sorted = sorted(objectives_filtered)
# remove the worst policies from the std calculation, this will allow us to keep improving even if 1-2 policies
# crashed and can't keep improving. Otherwise, std value will be too large.
objectives_std = np.std(objectives_filtered_sorted[num_outliers:])
else:
objectives_std = np.std(objectives_filtered)
objective_threshold = self.pbt_params.replace_threshold_frac_std * objectives_std
absolute_threshold = self.pbt_params.replace_threshold_frac_absolute * abs(candidate_objective)
if objective_delta > objective_threshold and objective_delta > absolute_threshold:
# replace this policy with a candidate
replacement_policy = replacement_policy_candidate
print(f"Replacing underperforming policy {self.policy_idx} with {replacement_policy}")
else:
print(
f"Policy {self.policy_idx}: Difference in objective value ({candidate_objective} vs {targ_objective_value}) is not sufficient to justify replacement,"
f"{objective_delta}, {objectives_std}, {objective_threshold}, {absolute_threshold}"
)
# replacing with "self": keep the weights but mutate the hyperparameters
replacement_policy = self.policy_idx
# Decided to replace the policy weights!
# we can either copy parameters from the checkpoint we're restarting from, or keep our parameters and
# further mutate them.
if random.random() < 0.5:
new_params = checkpoints[replacement_policy]["params"]
else:
new_params = self.pbt_params.mutable_params
new_params = mutate(
new_params,
self.pbt_params.params_to_mutate,
self.pbt_params.mutation_rate,
self.pbt_params.change_min,
self.pbt_params.change_max,
)
experiment_name = checkpoints[self.policy_idx]["experiment_name"]
try:
self._pbt_summaries(new_params, best_objective)
except Exception as exc:
print(f"Policy {self.policy_idx}: Exception {exc} when writing summaries!")
return
try:
restart_checkpoint = os.path.abspath(checkpoints[replacement_policy]["checkpoint"])
# delete previous tempdir to make sure we don't grow too big
checkpoint_tmp_dir = join(project_tmp_dir(), f"{experiment_name}_p{self.policy_idx}")
if os.path.isdir(checkpoint_tmp_dir):
shutil.rmtree(checkpoint_tmp_dir)
checkpoint_tmp_dir = safe_ensure_dir_exists(checkpoint_tmp_dir)
restart_checkpoint_tmp = join(checkpoint_tmp_dir, os.path.basename(restart_checkpoint))
# copy the checkpoint file to the temp dir to make sure it does not get deleted while we're restarting
shutil.copyfile(restart_checkpoint, restart_checkpoint_tmp)
except Exception as exc:
print(f"Policy {self.policy_idx}: Exception {exc} when copying checkpoint file for restart")
# perhaps checkpoint file was deleted before we could make a copy. Abort the restart.
return
# try to load the checkpoint file and if it fails, abandon the restart
try:
self._rewrite_checkpoint(restart_checkpoint_tmp, env_frames)
except Exception as exc:
# this should happen infrequently so should not affect training in any significant way
print(
f"Policy {self.policy_idx}: Exception {exc} when loading checkpoint file for restart."
f"Aborting restart. Continue training with the existing set of weights!"
)
return
print(
f"Policy {self.policy_idx}: Preparing to restart the process with mutated parameters! "
f"Checkpoint {restart_checkpoint_tmp}"
)
_restart_process_with_new_params(
self.policy_idx, new_params, restart_checkpoint_tmp, experiment_name, self.algo, self.with_wandb
)
def _rewrite_checkpoint(self, restart_checkpoint_tmp: str, env_frames: int) -> None:
state = torch.load(restart_checkpoint_tmp)
print(f"Policy {self.policy_idx}: restarting from checkpoint {restart_checkpoint_tmp}, {state['frame']}")
print(f"Replacing {state['frame']} with {env_frames}...")
state["frame"] = env_frames
pbt_history = state.get("pbt_history", [])
print(f"PBT history: {pbt_history}")
pbt_history.append((self.policy_idx, env_frames, self.curr_target_objective_value))
state["pbt_history"] = pbt_history
torch.save(state, restart_checkpoint_tmp)
print(f"Policy {self.policy_idx}: checkpoint rewritten to {restart_checkpoint_tmp}!")
def _save_pbt_checkpoint(self):
"""Save PBT-specific information including iteration number, policy index and hyperparameters."""
checkpoint_file = join(self.curr_policy_workspace_dir, _model_checkpnt_name(self.pbt_iteration))
algo_state = self.algo.get_full_state_weights()
safe_save(algo_state, checkpoint_file)
pbt_checkpoint_file = join(self.curr_policy_workspace_dir, _checkpnt_name(self.pbt_iteration))
pbt_checkpoint = {
"iteration": self.pbt_iteration,
"true_objective": self.curr_target_objective_value,
"frame": self.algo.frame,
"params": self.pbt_params.mutable_params,
"checkpoint": os.path.abspath(checkpoint_file),
"pbt_checkpoint": os.path.abspath(pbt_checkpoint_file),
"experiment_name": self.algo.experiment_name,
}
with open(pbt_checkpoint_file, "w") as fobj:
print(f"Policy {self.policy_idx}: Saving {pbt_checkpoint_file}...")
yaml.dump(pbt_checkpoint, fobj)
def _policy_workspace_dir(self, policy_idx):
return join(self.pbt_workspace_dir, f"{policy_idx:03d}")
def _load_population_checkpoints(self):
"""
Load checkpoints for other policies in the population.
Pick the newest checkpoint, but not newer than our current iteration.
"""
checkpoints = dict()
for policy_idx in range(self.pbt_num_policies):
checkpoints[policy_idx] = None
policy_workspace_dir = self._policy_workspace_dir(policy_idx)
if not os.path.isdir(policy_workspace_dir):
continue
pbt_checkpoint_files = [f for f in os.listdir(policy_workspace_dir) if f.endswith(".yaml")]
pbt_checkpoint_files.sort(reverse=True)
for pbt_checkpoint_file in pbt_checkpoint_files:
iteration_str = pbt_checkpoint_file.split(".")[0]
iteration = int(iteration_str)
if iteration <= self.pbt_iteration:
with open(join(policy_workspace_dir, pbt_checkpoint_file), "r") as fobj:
print(f"Policy {self.policy_idx}: Loading policy-{policy_idx} {pbt_checkpoint_file}")
checkpoints[policy_idx] = safe_filesystem_op(yaml.load, fobj, Loader=yaml.FullLoader)
break
else:
# print(f'Policy {self.policy_idx}: Ignoring {pbt_checkpoint_file} because it is newer than our current iteration')
pass
assert self.policy_idx in checkpoints.keys()
return checkpoints
def _maybe_save_best_policy(self, best_objective, best_policy_idx: int, best_policy_checkpoint):
# make a directory containing the best policy checkpoints using safe_filesystem_op
best_policy_workspace_dir = join(self.pbt_workspace_dir, f"best{self.policy_idx}")
safe_filesystem_op(os.makedirs, best_policy_workspace_dir, exist_ok=True)
best_objective_so_far = _UNINITIALIZED_VALUE
best_policy_checkpoint_files = [f for f in os.listdir(best_policy_workspace_dir) if f.endswith(".yaml")]
best_policy_checkpoint_files.sort(reverse=True)
if best_policy_checkpoint_files:
with open(join(best_policy_workspace_dir, best_policy_checkpoint_files[0]), "r") as fobj:
best_policy_checkpoint_so_far = safe_filesystem_op(yaml.load, fobj, Loader=yaml.FullLoader)
best_objective_so_far = best_policy_checkpoint_so_far["true_objective"]
if best_objective_so_far >= best_objective:
# don't save the checkpoint if it is worse than the best checkpoint so far
return
print(f"Policy {self.policy_idx}: New best objective: {best_objective}!")
# save the best policy checkpoint to this folder
best_policy_checkpoint_name = f"{self.pbt_params.task_name}_best_obj_{best_objective:015.5f}_iter_{self.pbt_iteration:04d}_policy{best_policy_idx:03d}_frame{self.algo.frame}"
# copy the checkpoint file to the best policy directory
try:
shutil.copy(
best_policy_checkpoint["checkpoint"],
join(best_policy_workspace_dir, f"{best_policy_checkpoint_name}.pth"),
)
shutil.copy(
best_policy_checkpoint["pbt_checkpoint"],
join(best_policy_workspace_dir, f"{best_policy_checkpoint_name}.yaml"),
)
# cleanup older best policy checkpoints, we want to keep only N latest files
best_policy_checkpoint_files = [f for f in os.listdir(best_policy_workspace_dir)]
best_policy_checkpoint_files.sort(reverse=True)
n_to_keep = 6
for best_policy_checkpoint_file in best_policy_checkpoint_files[n_to_keep:]:
os.remove(join(best_policy_workspace_dir, best_policy_checkpoint_file))
except Exception as exc:
print(f"Policy {self.policy_idx}: Exception {exc} when copying best checkpoint!")
# no big deal if this fails, hopefully the next time we will succeeed
return
def _pbt_summaries(self, params, best_objective):
for param, value in params.items():
self.algo.writer.add_scalar(f"pbt/{param}", value, self.algo.frame)
self.algo.writer.add_scalar(f"pbt/00_best_objective", best_objective, self.algo.frame)
self.algo.writer.flush()
def _cleanup(self, checkpoints):
iterations = []
for policy_idx, checkpoint in checkpoints.items():
if checkpoint is None:
iterations.append(0)
else:
iterations.append(checkpoint["iteration"])
oldest_iteration = sorted(iterations)[0]
cleanup_threshold = oldest_iteration - 20
print(
f"Policy {self.policy_idx}: Oldest iteration in population is {oldest_iteration}, removing checkpoints older than {cleanup_threshold} iteration"
)
pbt_checkpoint_files = [f for f in os.listdir(self.curr_policy_workspace_dir)]
for f in pbt_checkpoint_files:
if "." in f:
iteration_idx = int(f.split(".")[0])
if iteration_idx <= cleanup_threshold:
print(f"Policy {self.policy_idx}: PBT cleanup: removing checkpoint {f}")
# we catch all exceptions in this function so no need to use safe_filesystem_op
os.remove(join(self.curr_policy_workspace_dir, f))
# Sometimes, one of the PBT processes can get stuck, or crash, or be scheduled significantly later on Slurm
# or a similar cluster management system.
# In that case, we will accumulate a lot of older checkpoints. In order to keep the number of older checkpoints
# under control (to avoid running out of disk space) we implement the following logic:
# when we have more than N checkpoints, we delete half of the oldest checkpoints. This caps the max amount of
# disk space used, and still allows older policies to participate in PBT
max_old_checkpoints = 25
while True:
pbt_checkpoint_files = [f for f in os.listdir(self.curr_policy_workspace_dir) if f.endswith(".yaml")]
if len(pbt_checkpoint_files) <= max_old_checkpoints:
break
if not self._delete_old_checkpoint(pbt_checkpoint_files):
break
def _delete_old_checkpoint(self, pbt_checkpoint_files: List[str]) -> bool:
"""
Delete the checkpoint that results in the smallest max gap between the remaining checkpoints.
Do not delete any of the last N checkpoints.
"""
pbt_checkpoint_files.sort()
n_latest_to_keep = 10
candidates = pbt_checkpoint_files[:-n_latest_to_keep]
num_candidates = len(candidates)
if num_candidates < 3:
return False
def _iter(f):
return int(f.split(".")[0])
best_gap = 1e9
best_candidate = 1
for i in range(1, num_candidates - 1):
prev_iteration = _iter(candidates[i - 1])
next_iteration = _iter(candidates[i + 1])
# gap is we delete the ith candidate
gap = next_iteration - prev_iteration
if gap < best_gap:
best_gap = gap
best_candidate = i
# delete the best candidate
best_candidate_file = candidates[best_candidate]
files_to_remove = [best_candidate_file, _model_checkpnt_name(_iter(best_candidate_file))]
for file_to_remove in files_to_remove:
print(
f"Policy {self.policy_idx}: PBT cleanup old checkpoints, removing checkpoint {file_to_remove} (best gap {best_gap})"
)
os.remove(join(self.curr_policy_workspace_dir, file_to_remove))
return True
| 30,434 | Python | 42.917749 | 182 | 0.638792 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/mutation.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import copy
import random
def mutate_float(x, change_min=1.1, change_max=1.5):
perturb_amount = random.uniform(change_min, change_max)
# mutation direction
new_value = x / perturb_amount if random.random() < 0.5 else x * perturb_amount
return new_value
def mutate_float_min_1(x, **kwargs):
new_value = mutate_float(x, **kwargs)
new_value = max(1.0, new_value)
return new_value
def mutate_eps_clip(x, **kwargs):
new_value = mutate_float(x, **kwargs)
new_value = max(0.01, new_value)
new_value = min(0.3, new_value)
return new_value
def mutate_mini_epochs(x, **kwargs):
change_amount = 1
new_value = x + change_amount if random.random() < 0.5 else x - change_amount
new_value = max(1, new_value)
new_value = min(8, new_value)
return new_value
def mutate_discount(x, **kwargs):
"""Special mutation func for parameters such as gamma (discount factor)."""
inv_x = 1.0 - x
# very conservative, large changes in gamma can lead to very different critic estimates
new_inv_x = mutate_float(inv_x, change_min=1.1, change_max=1.2)
new_value = 1.0 - new_inv_x
return new_value
def get_mutation_func(mutation_func_name):
try:
func = eval(mutation_func_name)
except Exception as exc:
print(f'Exception {exc} while trying to find the mutation func {mutation_func_name}.')
raise Exception(f'Could not find mutation func {mutation_func_name}')
return func
def mutate(params, mutations, mutation_rate, pbt_change_min, pbt_change_max):
mutated_params = copy.deepcopy(params)
for param, param_value in params.items():
# toss a coin whether we perturb the parameter at all
if random.random() > mutation_rate:
continue
mutation_func_name = mutations[param]
mutation_func = get_mutation_func(mutation_func_name)
mutated_value = mutation_func(param_value, change_min=pbt_change_min, change_max=pbt_change_max)
mutated_params[param] = mutated_value
print(f'Param {param} mutated to value {mutated_value}')
return mutated_params
| 3,686 | Python | 36.622449 | 104 | 0.715138 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/ant_pbt.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.run_utils import version
_env = 'ant'
_name = f'{_env}_{version}'
_iterations = 10000
_pbt_num_policies = 3
_params = ParamGrid([
('pbt.policy_idx', list(range(_pbt_num_policies))),
])
_wandb_activate = True
_wandb_group = f'pbt_{_name}'
_wandb_entity = 'your_wandb_entity'
_wandb_project = 'your_wandb_project'
_experiments = [
Experiment(
f'{_name}',
f'python -m isaacgymenvs.train task=Ant headless=True '
f'max_iterations={_iterations} num_envs=2048 seed=-1 train.params.config.save_frequency=2000 '
f'wandb_activate={_wandb_activate} wandb_group={_wandb_group} wandb_entity={_wandb_entity} wandb_project={_wandb_project} '
f'pbt=pbt_default pbt.num_policies={_pbt_num_policies} pbt.workspace=workspace_{_name} '
f'pbt.initial_delay=10000000 pbt.interval_steps=5000000 pbt.start_after=10000000 pbt/mutation=ant_mutation',
_params.generate_params(randomize=False),
),
]
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=_experiments, experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 1,285 | Python | 33.756756 | 131 | 0.701167 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_two_arms_reorientation_lstm.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.run_utils import version, seeds, default_num_frames
kuka_env = 'allegro_kuka_two_arms_reorientation'
_frames = default_num_frames
_name = f'{kuka_env}_{version}'
_params = ParamGrid([
('seed', seeds(8)),
])
_wandb_activate = True
_wandb_group = f'pbt_{_name}'
_wandb_entity = 'your_wandb_entity'
_wandb_project = 'your_wandb_project'
cli = f'python -m isaacgymenvs.train ' \
f'train.params.config.max_frames={_frames} headless=True ' \
f'task=AllegroKukaTwoArmsLSTM task/env=reorientation ' \
f'wandb_project={_wandb_project} wandb_entity={_wandb_entity} wandb_activate={_wandb_activate} wandb_group={_wandb_group}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 1,045 | Python | 33.866666 | 128 | 0.71866 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_throw_lstm.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.run_utils import version, seeds, default_num_frames
kuka_env = 'allegro_kuka_throw'
_frames = default_num_frames
_name = f'{kuka_env}_{version}'
_params = ParamGrid([
('seed', seeds(8)),
])
_wandb_activate = True
_wandb_group = f'pbt_{_name}'
_wandb_entity = 'your_wandb_entity'
_wandb_project = 'your_wandb_project'
cli = f'python -m isaacgymenvs.train seed=-1 ' \
f'train.params.config.max_frames={_frames} headless=True ' \
f'task=AllegroKukaLSTM task/env=throw ' \
f'wandb_project={_wandb_project} wandb_entity={_wandb_entity} wandb_activate={_wandb_activate} wandb_group={_wandb_group}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 1,021 | Python | 33.066666 | 128 | 0.711068 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/run_utils.py | import random
from typing import List
# Versioning -- you can change this number and keep a changelog below to keep track of your experiments as you go.
version = "v1"
def seeds(num_seeds) -> List[int]:
return [random.randrange(1000000, 9999999) for _ in range(num_seeds)]
default_num_frames: int = 10_000_000_000
| 323 | Python | 23.923075 | 114 | 0.73065 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_reorientation_lstm.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.run_utils import version, seeds, default_num_frames
kuka_env = 'allegro_kuka_reorientation'
_frames = default_num_frames
_name = f'{kuka_env}_{version}'
_wandb_activate = True
_wandb_group = f'pbt_{_name}'
_wandb_entity = 'your_wandb_entity'
_wandb_project = 'your_wandb_project'
_params = ParamGrid([
('seed', seeds(8)),
])
cli = f'python -m isaacgymenvs.train seed=-1 ' \
f'train.params.config.max_frames={_frames} headless=True ' \
f'task=AllegroKukaLSTM task/env=reorientation ' \
f'wandb_project={_wandb_project} wandb_entity={_wandb_entity} wandb_activate={_wandb_activate} wandb_group={_wandb_group}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 1,037 | Python | 33.599999 | 128 | 0.715526 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_two_arms_regrasping_pbt_lstm.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.allegro_kuka_pbt_base import kuka_base_cli
from isaacgymenvs.pbt.experiments.run_utils import version
env = 'allegro_kuka_two_arms_regrasp'
_pbt_num_policies = 8
_name = f'{env}_{version}_pbt_{_pbt_num_policies}p'
_wandb_group = f'pbt_{_name}'
_params = ParamGrid([
('pbt.policy_idx', list(range(_pbt_num_policies))),
])
cli = kuka_base_cli + f' task=AllegroKukaTwoArmsLSTM task/env=regrasping task.env.episodeLength=400 wandb_activate=True wandb_group={_wandb_group} pbt.num_policies={_pbt_num_policies}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 916 | Python | 37.208332 | 184 | 0.741266 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_regrasping_pbt_lstm.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.allegro_kuka_pbt_base import kuka_env, kuka_base_cli
from isaacgymenvs.pbt.experiments.run_utils import version
_pbt_num_policies = 8
_name = f'{kuka_env}_regrasp_{version}_pbt_{_pbt_num_policies}p'
_wandb_group = f'pbt_{_name}'
_params = ParamGrid([
('pbt.policy_idx', list(range(_pbt_num_policies))),
])
cli = kuka_base_cli + f' task=AllegroKukaLSTM task/env=regrasping wandb_activate=True wandb_group={_wandb_group} pbt.num_policies={_pbt_num_policies}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 866 | Python | 38.409089 | 150 | 0.737875 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_two_arms_regrasping_lstm.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.run_utils import version, seeds, default_num_frames
kuka_env = 'allegro_kuka_two_arms_regrasp'
_frames = default_num_frames
_name = f'{kuka_env}_{version}'
_params = ParamGrid([
('seed', seeds(8)),
])
_wandb_activate = True
_wandb_group = f'pbt_{_name}'
_wandb_entity = 'your_wandb_entity'
_wandb_project = 'your_wandb_project'
cli = f'python -m isaacgymenvs.train seed=-1 ' \
f'train.params.config.max_frames={_frames} headless=True ' \
f'task=AllegroKukaTwoArmsLSTM task/env=regrasping ' \
f'task.env.episodeLength=400 ' \
f'wandb_project={_wandb_project} wandb_entity={_wandb_entity} wandb_activate={_wandb_activate} wandb_group={_wandb_group}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 1,083 | Python | 33.967741 | 128 | 0.711911 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_reorientation_lstm_8gpu.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.run_utils import version, seeds, default_num_frames
kuka_env = 'allegro_kuka_reorientation'
_num_gpus = 8
_frames = default_num_frames * _num_gpus
_name = f'{kuka_env}_{version}_{_num_gpus}gpu'
_params = ParamGrid([
('seed', seeds(1)),
])
_wandb_activate = True
_wandb_group = f'rlgames_{_name}'
_wandb_entity = 'your_wandb_entity'
_wandb_project = 'your_wandb_project'
cli = f'train.py multi_gpu=True ' \
f'train.params.config.max_frames={_frames} headless=True ' \
f'task=AllegroKukaLSTM task/env=reorientation ' \
f'wandb_project={_wandb_project} wandb_entity={_wandb_entity} wandb_activate={_wandb_activate} wandb_group={_wandb_group}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 1,069 | Python | 33.516128 | 128 | 0.71188 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_regrasping_lstm.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.run_utils import version, seeds, default_num_frames
kuka_env = 'allegro_kuka_regrasp'
_frames = default_num_frames
_name = f'{kuka_env}_{version}'
_params = ParamGrid([
('seed', seeds(8)),
])
_wandb_activate = True
_wandb_group = f'pbt_{_name}'
_wandb_entity = 'your_wandb_entity'
_wandb_project = 'your_wandb_project'
cli = f'python -m isaacgymenvs.train seed=-1 ' \
f'train.params.config.max_frames={_frames} headless=True ' \
f'task=AllegroKukaLSTM task/env=regrasping ' \
f'wandb_project={_wandb_project} wandb_entity={_wandb_entity} wandb_activate={_wandb_activate} wandb_group={_wandb_group}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 1,028 | Python | 33.299999 | 128 | 0.713035 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_reorientation_pbt_lstm.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.allegro_kuka_pbt_base import kuka_env, kuka_base_cli
from isaacgymenvs.pbt.experiments.run_utils import version
_pbt_num_policies = 8
_name = f'{kuka_env}_manip_{version}_pbt_{_pbt_num_policies}p'
_params = ParamGrid([
('pbt.policy_idx', list(range(_pbt_num_policies))),
])
_wandb_activate = True
_wandb_group = f'pbt_{_name}'
_wandb_entity = 'your_wandb_entity'
_wandb_project = 'your_wandb_project'
cli = kuka_base_cli + f' task=AllegroKukaLSTM task/env=reorientation ' \
f'wandb_project={_wandb_project} wandb_entity={_wandb_entity} wandb_activate={_wandb_activate} wandb_group={_wandb_group}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 1,029 | Python | 37.148147 | 144 | 0.718173 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_throw_pbt_lstm.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.allegro_kuka_pbt_base import kuka_env, kuka_base_cli
from isaacgymenvs.pbt.experiments.run_utils import version
_pbt_num_policies = 8
_name = f'{kuka_env}_throw_{version}_pbt_{_pbt_num_policies}p'
_params = ParamGrid([
('pbt.policy_idx', list(range(_pbt_num_policies))),
])
_wandb_activate = True
_wandb_group = f'pbt_{_name}'
_wandb_entity = 'your_wandb_entity'
_wandb_project = 'your_wandb_project'
cli = kuka_base_cli + \
f' task=AllegroKukaLSTM ' \
f'task/env=throw wandb_activate=True pbt.num_policies={_pbt_num_policies} ' \
f'wandb_project={_wandb_project} wandb_entity={_wandb_entity} wandb_activate={_wandb_activate} wandb_group={_wandb_group}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 1,076 | Python | 36.13793 | 126 | 0.722119 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_two_arms_reorientation_pbt_lstm.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.allegro_kuka_pbt_base import kuka_base_cli
from isaacgymenvs.pbt.experiments.run_utils import version
env = 'allegro_kuka_two_arms_reorientation'
_pbt_num_policies = 8
_name = f'{env}_{version}_pbt_{_pbt_num_policies}p'
_wandb_group = f'pbt_{_name}'
_params = ParamGrid([
('pbt.policy_idx', list(range(_pbt_num_policies))),
])
cli = kuka_base_cli + f' task=AllegroKukaTwoArmsLSTM task/env=reorientation wandb_activate=True wandb_group={_wandb_group} pbt.num_policies={_pbt_num_policies}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 898 | Python | 36.458332 | 160 | 0.740535 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/experiments/allegro_kuka_pbt_base.py | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.run_utils import version, default_num_frames
kuka_env = 'allegro_kuka'
_frames = default_num_frames
_pbt_num_policies = 8
_name = f'{kuka_env}_{version}_pbt_{_pbt_num_policies}p'
_wandb_activate = True
_wandb_group = f'pbt_{_name}'
_wandb_entity = 'your_wandb_entity'
_wandb_project = 'your_wandb_project'
kuka_base_cli = (f'python -m isaacgymenvs.train seed=-1 '
f'train.params.config.max_frames={_frames} headless=True '
f'wandb_project={_wandb_project} wandb_entity={_wandb_entity} wandb_activate={_wandb_activate} wandb_group={_wandb_group} '
f'pbt=pbt_default pbt.workspace=workspace_{kuka_env} '
f'pbt.interval_steps=20000000 pbt.start_after=100000000 pbt.initial_delay=200000000 pbt.replace_fraction_worst=0.3 pbt/mutation=allegro_kuka_mutation')
_params = ParamGrid([
('pbt.policy_idx', list(range(_pbt_num_policies))),
])
cli = kuka_base_cli + f' task=AllegroKuka task/env=reorientation pbt.num_policies={_pbt_num_policies}'
RUN_DESCRIPTION = RunDescription(
f'{_name}',
experiments=[Experiment(f'{_name}', cli, _params.generate_params(randomize=False))],
experiment_arg_name='experiment', experiment_dir_arg_name='hydra.run.dir',
param_prefix='', customize_experiment_name=False,
)
| 1,414 | Python | 40.617646 | 168 | 0.704385 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/launcher/run_description.py | import os
import re
from collections import OrderedDict
from os.path import join
import numpy as np
class ParamGenerator:
def __init__(self):
pass
def generate_params(self, randomize=True):
"""Supposed to be a generator (so should yield dicts of parameters)."""
pass
class ParamList(ParamGenerator):
"""The most simple kind of generator, represents just the list of parameter combinations."""
def __init__(self, combinations):
super(ParamList, self).__init__()
self.combinations = combinations
def generate_params(self, randomize=True):
if randomize:
combinations = np.random.permutation(self.combinations)
else:
combinations = self.combinations
for combination in combinations:
yield combination
class ParamGrid(ParamGenerator):
"""Parameter generator for grid search."""
def __init__(self, grid_tuples):
"""Uses OrderedDict, so must be initialized with the list of tuples if you want to preserve order."""
super(ParamGrid, self).__init__()
self.grid = OrderedDict(grid_tuples)
def _generate_combinations(self, param_idx, params):
"""Recursively generate all parameter combinations in a grid."""
if param_idx == len(self.grid) - 1:
# last parameter, just return list of values for this parameter
return [[value] for value in self.grid[params[param_idx]]]
else:
subcombinations = self._generate_combinations(param_idx + 1, params) # returns list of param combinations
result = []
# iterate over all values of current parameter
for value in self.grid[params[param_idx]]:
for subcombination in subcombinations:
result.append([value] + subcombination)
return result
def generate_params(self, randomize=False):
if len(self.grid) == 0:
return dict()
# start with 0th value for every parameter
total_num_combinations = np.prod([len(p_values) for p_values in self.grid.values()])
param_names = tuple(self.grid.keys())
all_combinations = self._generate_combinations(0, param_names)
assert len(all_combinations) == total_num_combinations
if randomize:
all_combinations = np.random.permutation(all_combinations)
for combination in all_combinations:
combination_dict = dict()
for i, param_name in enumerate(param_names):
if isinstance(param_name, (list, tuple)):
for j, param in enumerate(param_name):
combination_dict[param] = combination[i][j]
else:
combination_dict[param_name] = combination[i]
yield combination_dict
class Experiment:
def __init__(self, name, cmd, param_generator=(), env_vars=None):
"""
:param cmd: base command to append the parameters to
:param param_generator: iterable of parameter dicts
"""
self.base_name = name
self.cmd = cmd
self.params = list(param_generator)
self.env_vars = env_vars
def generate_experiments(self, experiment_arg_name, customize_experiment_name, param_prefix):
"""Yields tuples of (cmd, experiment_name)"""
num_experiments = 1 if len(self.params) == 0 else len(self.params)
for experiment_idx in range(num_experiments):
cmd_tokens = [self.cmd]
experiment_name_tokens = [self.base_name]
# abbreviations for parameter names that we've used
param_shorthands = []
if len(self.params) > 0:
params = self.params[experiment_idx]
for param, value in params.items():
param_str = f"{param_prefix}{param}={value}"
cmd_tokens.append(param_str)
param_tokens = re.split("[._-]", param)
shorthand_tokens = [t[0] for t in param_tokens[:-1]]
last_token_l = min(3, len(param_tokens[-1]))
shorthand = ".".join(shorthand_tokens + [param_tokens[-1][:last_token_l]])
while last_token_l <= len(param_tokens[-1]) and shorthand in param_shorthands:
last_token_l += 1
shorthand = ".".join(shorthand_tokens + [param_tokens[-1][:last_token_l]])
param_shorthands.append(shorthand)
experiment_name_token = f"{shorthand}_{value}"
experiment_name_tokens.append(experiment_name_token)
if customize_experiment_name:
experiment_name = f"{experiment_idx:02d}_" + "_".join(experiment_name_tokens)
if len(experiment_name) > 100:
print(f"Experiment name is extra long! ({len(experiment_name)} characters)")
else:
experiment_name = f"{experiment_idx:02d}_{self.base_name}"
cmd_tokens.append(f"{experiment_arg_name}={experiment_name}")
param_str = " ".join(cmd_tokens)
yield param_str, experiment_name
class RunDescription:
def __init__(
self,
run_name,
experiments,
experiment_arg_name="--experiment",
experiment_dir_arg_name="--train_dir",
customize_experiment_name=True,
param_prefix="--",
):
"""
:param run_name: overall name of the experiment and the name of the root folder
:param experiments: a list of Experiment objects to run
:param experiment_arg_name: CLI argument of the underlying experiment that determines it's unique name
to be generated by the launcher. Default: --experiment
:param experiment_dir_arg_name: CLI argument for the root train dir of your experiment. Default: --train_dir
:param customize_experiment_name: whether to add a hyperparameter combination to the experiment name
:param param_prefix: most experiments will use "--" prefix for each parameter, but some apps don't have this
prefix, i.e. with Hydra you should set it to empty string.
"""
self.run_name = run_name
self.experiments = experiments
self.experiment_suffix = ""
self.experiment_arg_name = experiment_arg_name
self.experiment_dir_arg_name = experiment_dir_arg_name
self.customize_experiment_name = customize_experiment_name
self.param_prefix = param_prefix
def generate_experiments(self, train_dir, makedirs=True):
"""Yields tuples (final cmd for experiment, experiment_name, root_dir)."""
for experiment in self.experiments:
root_dir = join(self.run_name, f"{experiment.base_name}_{self.experiment_suffix}")
experiment_cmds = experiment.generate_experiments(
self.experiment_arg_name, self.customize_experiment_name, self.param_prefix
)
for experiment_cmd, experiment_name in experiment_cmds:
experiment_dir = join(train_dir, root_dir)
if makedirs:
os.makedirs(experiment_dir, exist_ok=True)
experiment_cmd += f" {self.experiment_dir_arg_name}={experiment_dir}"
yield experiment_cmd, experiment_name, root_dir, experiment.env_vars
| 7,439 | Python | 39 | 118 | 0.605323 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/launcher/run_ngc.py | """
Run many experiments with NGC: hyperparameter sweeps, etc.
This isn't production code, but feel free to use as an example for your NGC setup.
"""
import time
from multiprocessing.pool import ThreadPool
from subprocess import PIPE, Popen
from isaacgymenvs.pbt.launcher.run_slurm import str2bool
def add_ngc_args(parser):
parser.add_argument(
"--ngc_job_template",
default=None,
type=str,
help="NGC command line template, specifying instance type, docker container, etc.",
)
parser.add_argument(
"--ngc_print_only", default=False, type=str2bool, help="Just print commands to the console without executing"
)
parser.set_defaults(pause_between=0)
return parser
def run_ngc(run_description, args):
pause_between = args.pause_between
experiments = run_description.experiments
print(f"Starting processes with base cmds: {[e.cmd for e in experiments]}")
if args.ngc_job_template is not None:
with open(args.ngc_job_template, "r") as template_file:
ngc_template = template_file.read()
ngc_template = ngc_template.replace("\\", " ")
ngc_template = " ".join(ngc_template.split())
print(f"NGC template: {ngc_template}")
experiments = run_description.generate_experiments(args.train_dir, makedirs=False)
experiments = list(experiments)
print(f"{len(experiments)} experiments to run")
def launch_experiment(experiment_idx, experiment_):
time.sleep(experiment_idx * 0.1)
cmd, name, *_ = experiment_
job_name = name
print(f"Job name: {job_name}")
ngc_job_cmd = ngc_template.replace("{{ name }}", job_name).replace("{{ experiment_cmd }}", cmd)
print(f"Executing {ngc_job_cmd}")
if not args.ngc_print_only:
process = Popen(ngc_job_cmd, stdout=PIPE, shell=True)
output, err = process.communicate()
exit_code = process.wait()
print(f"Output: {output}, err: {err}, exit code: {exit_code}")
time.sleep(pause_between)
pool_size = 1 if pause_between > 0 else min(10, len(experiments))
with ThreadPool(pool_size) as p:
p.starmap(launch_experiment, enumerate(experiments))
print("Done!")
return 0
| 2,260 | Python | 29.972602 | 117 | 0.654425 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/launcher/run_slurm.py | import argparse
import os
import time
from os.path import join
from string import Template
from subprocess import PIPE, Popen
SBATCH_TEMPLATE_DEFAULT = (
"#!/bin/bash\n"
"conda activate conda_env_name\n"
"cd ~/project\n"
)
def str2bool(v):
if isinstance(v, bool):
return v
if isinstance(v, str) and v.lower() in ("true",):
return True
elif isinstance(v, str) and v.lower() in ("false",):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected")
def add_slurm_args(parser):
parser.add_argument("--slurm_gpus_per_job", default=1, type=int, help="GPUs in a single SLURM process")
parser.add_argument(
"--slurm_cpus_per_gpu", default=16, type=int, help="Max allowed number of CPU cores per allocated GPU"
)
parser.add_argument(
"--slurm_print_only", default=False, type=str2bool, help="Just print commands to the console without executing"
)
parser.add_argument(
"--slurm_workdir",
default=None,
type=str,
help="Optional workdir. Used by slurm launcher to store logfiles etc.",
)
parser.add_argument(
"--slurm_partition",
default=None,
type=str,
help='Adds slurm partition, i.e. for "gpu" it will add "-p gpu" to sbatch command line',
)
parser.add_argument(
"--slurm_sbatch_template",
default=None,
type=str,
help="Commands to run before the actual experiment (i.e. activate conda env, etc.)",
)
parser.add_argument(
"--slurm_timeout",
default="0",
type=str,
help="Time to run jobs before timing out job and requeuing the job. Defaults to 0, which does not time out the job",
)
return parser
def run_slurm(run_description, args):
workdir = args.slurm_workdir
pause_between = args.pause_between
experiments = run_description.experiments
print(f"Starting processes with base cmds: {[e.cmd for e in experiments]}")
if not os.path.exists(workdir):
print(f"Creating {workdir}...")
os.makedirs(workdir)
if args.slurm_sbatch_template is not None:
with open(args.slurm_sbatch_template, "r") as template_file:
sbatch_template = template_file.read()
else:
sbatch_template = SBATCH_TEMPLATE_DEFAULT
print(f"Sbatch template: {sbatch_template}")
partition = ""
if args.slurm_partition is not None:
partition = f"-p {args.slurm_partition} "
num_cpus = args.slurm_cpus_per_gpu * args.slurm_gpus_per_job
experiments = run_description.generate_experiments(args.train_dir)
sbatch_files = []
for experiment in experiments:
cmd, name, *_ = experiment
sbatch_fname = f"sbatch_{name}.sh"
sbatch_fname = join(workdir, sbatch_fname)
sbatch_fname = os.path.abspath(sbatch_fname)
file_content = Template(sbatch_template).substitute(
CMD=cmd,
FILENAME=sbatch_fname,
PARTITION=partition,
GPU=args.slurm_gpus_per_job,
CPU=num_cpus,
TIMEOUT=args.slurm_timeout,
)
with open(sbatch_fname, "w") as sbatch_f:
sbatch_f.write(file_content)
sbatch_files.append(sbatch_fname)
job_ids = []
idx = 0
for sbatch_file in sbatch_files:
idx += 1
sbatch_fname = os.path.basename(sbatch_file)
cmd = f"sbatch {partition}--gres=gpu:{args.slurm_gpus_per_job} -c {num_cpus} --parsable --output {workdir}/{sbatch_fname}-slurm-%j.out {sbatch_file}"
print(f"Executing {cmd}")
if args.slurm_print_only:
output = idx
else:
cmd_tokens = cmd.split()
process = Popen(cmd_tokens, stdout=PIPE)
output, err = process.communicate()
exit_code = process.wait()
print(f"{output} {err} {exit_code}")
if exit_code != 0:
print("sbatch process failed!")
time.sleep(5)
job_id = int(output)
job_ids.append(str(job_id))
time.sleep(pause_between)
tail_cmd = f"tail -f {workdir}/*.out"
print(f"Monitor log files using\n\n\t {tail_cmd} \n\n")
scancel_cmd = f'scancel {" ".join(job_ids)}'
print("Jobs queued: %r" % job_ids)
print("Use this command to cancel your jobs: \n\t %s \n" % scancel_cmd)
with open(join(workdir, "scancel.sh"), "w") as fobj:
fobj.write(scancel_cmd)
print("Done!")
return 0
| 4,525 | Python | 28.776316 | 157 | 0.60663 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/launcher/run_processes.py | """Run groups of experiments, hyperparameter sweeps, etc."""
import argparse
import os
import subprocess
import sys
import time
from os.path import join
def add_os_parallelism_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
parser.add_argument("--num_gpus", default=1, type=int, help="How many local GPUs to use")
parser.add_argument("--max_parallel", default=4, type=int, help="Maximum simultaneous experiments")
parser.add_argument(
"--experiments_per_gpu",
default=-1,
type=int,
help="How many experiments can we squeeze on a single GPU. "
"Specify this option if and only if you are using launcher to run several experiments using OS-level"
"parallelism (--backend=processes)."
"In any other case use default value (-1) for not altering CUDA_VISIBLE_DEVICES at all."
"This will allow your experiments to use all GPUs available (as many as --num_gpu allows)"
"Helpful when e.g. you are running a single big PBT experiment.",
)
return parser
def ensure_dir_exists(path) -> str:
if not os.path.exists(path):
os.makedirs(path, exist_ok=True)
return path
def run(run_description, args):
experiments = run_description.experiments
max_parallel = args.max_parallel
print("Starting processes with base cmds: %r", [e.cmd for e in experiments])
print(f"Max parallel processes is {max_parallel}")
print(f"Monitor log files using\n\n\ttail -f train_dir/{run_description.run_name}/**/**/sf_log.txt\n\n")
processes = []
processes_per_gpu = {g: [] for g in range(args.num_gpus)}
experiments = run_description.generate_experiments(args.train_dir)
next_experiment = next(experiments, None)
def find_least_busy_gpu():
least_busy_gpu = None
gpu_available_processes = 0
for gpu_id in range(args.num_gpus):
available_processes = args.experiments_per_gpu - len(processes_per_gpu[gpu_id])
if available_processes > gpu_available_processes:
gpu_available_processes = available_processes
least_busy_gpu = gpu_id
return least_busy_gpu, gpu_available_processes
def can_squeeze_another_process():
if len(processes) >= max_parallel:
return False
if args.experiments_per_gpu > 0:
least_busy_gpu, gpu_available_processes = find_least_busy_gpu()
if gpu_available_processes <= 0:
return False
return True
failed_processes = []
last_log_time = 0
log_interval = 3 # seconds
while len(processes) > 0 or next_experiment is not None:
while can_squeeze_another_process() and next_experiment is not None:
cmd, name, root_dir, exp_env_vars = next_experiment
cmd_tokens = cmd.split(" ")
# workaround to make sure we're running the correct python executable from our virtual env
if cmd_tokens[0].startswith("python"):
cmd_tokens[0] = sys.executable
print(f"Using Python executable {cmd_tokens[0]}")
ensure_dir_exists(join(args.train_dir, root_dir))
envvars = os.environ.copy()
best_gpu = None
if args.experiments_per_gpu > 0:
best_gpu, best_gpu_available_processes = find_least_busy_gpu()
print(
f"The least busy gpu is {best_gpu} where we can run {best_gpu_available_processes} more processes",
)
envvars["CUDA_VISIBLE_DEVICES"] = f"{best_gpu}"
print(f"Starting process {cmd_tokens}")
if exp_env_vars is not None:
for key, value in exp_env_vars.items():
print(f"Adding env variable {key} {value}")
envvars[str(key)] = str(value)
process = subprocess.Popen(cmd_tokens, stdout=None, stderr=None, env=envvars)
process.gpu_id = best_gpu
process.proc_cmd = cmd
processes.append(process)
if process.gpu_id is not None:
processes_per_gpu[process.gpu_id].append(process.proc_cmd)
print(f"Started process {process.proc_cmd} GPU {process.gpu_id}")
print(f"Waiting for {args.pause_between} seconds before starting next process")
time.sleep(args.pause_between)
next_experiment = next(experiments, None)
remaining_processes = []
for process in processes:
if process.poll() is None:
remaining_processes.append(process)
continue
else:
if process.gpu_id is not None:
processes_per_gpu[process.gpu_id].remove(process.proc_cmd)
print(f"Process finished {process.proc_cmd}, {process.returncode}")
if process.returncode != 0:
failed_processes.append((process.proc_cmd, process.pid, process.returncode))
print(f"WARNING: RETURN CODE IS {process.returncode}")
processes = remaining_processes
if time.time() - last_log_time > log_interval:
if failed_processes:
print(f"Failed processes:", ", ".join([f"PID: {p[1]} code: {p[2]}" for p in failed_processes]))
last_log_time = time.time()
time.sleep(0.1)
print("Done!")
return 0
| 5,425 | Python | 36.420689 | 119 | 0.609032 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/pbt/launcher/run.py | import argparse
import importlib
import sys
from isaacgymenvs.pbt.launcher.run_ngc import add_ngc_args, run_ngc
from isaacgymenvs.pbt.launcher.run_processes import add_os_parallelism_args, run
from isaacgymenvs.pbt.launcher.run_slurm import add_slurm_args, run_slurm
def launcher_argparser(args) -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
parser.add_argument("--train_dir", default="./train_dir", type=str, help="Directory for sub-experiments")
parser.add_argument(
"--run",
default=None,
type=str,
help="Name of the python module that describes the run, e.g. sf_examples.vizdoom.experiments.paper_doom_all_basic_envs.py "
"Run module must be importable in your Python environment. It must define a global variable RUN_DESCRIPTION (see existing run modules for examples).",
)
parser.add_argument(
"--backend",
default="processes",
choices=["processes", "slurm", "ngc"],
help="Launcher backend, use OS multiprocessing by default",
)
parser.add_argument("--pause_between", default=1, type=int, help="Pause in seconds between processes")
parser.add_argument(
"--experiment_suffix", default="", type=str, help="Append this to the name of the experiment dir"
)
partial_cfg, _ = parser.parse_known_args(args)
if partial_cfg.backend == "slurm":
parser = add_slurm_args(parser)
elif partial_cfg.backend == "ngc":
parser = add_ngc_args(parser)
elif partial_cfg.backend == "processes":
parser = add_os_parallelism_args(parser)
else:
raise ValueError(f"Unknown backend: {partial_cfg.backend}")
return parser
def parse_args():
args = launcher_argparser(sys.argv[1:]).parse_args(sys.argv[1:])
return args
def main():
launcher_cfg = parse_args()
try:
# assuming we're given the full name of the module
run_module = importlib.import_module(f"{launcher_cfg.run}")
except ImportError as exc:
print(f"Could not import the run module {exc}")
return 1
run_description = run_module.RUN_DESCRIPTION
run_description.experiment_suffix = launcher_cfg.experiment_suffix
if launcher_cfg.backend == "processes":
run(run_description, launcher_cfg)
elif launcher_cfg.backend == "slurm":
run_slurm(run_description, launcher_cfg)
elif launcher_cfg.backend == "ngc":
run_ngc(run_description, launcher_cfg)
return 0
if __name__ == "__main__":
sys.exit(main())
| 2,538 | Python | 32.853333 | 158 | 0.670213 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/utils/wandb_utils.py | from rl_games.common.algo_observer import AlgoObserver
from isaacgymenvs.utils.utils import retry
from isaacgymenvs.utils.reformat import omegaconf_to_dict
class WandbAlgoObserver(AlgoObserver):
"""Need this to propagate the correct experiment name after initialization."""
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
def before_init(self, base_name, config, experiment_name):
"""
Must call initialization of Wandb before RL-games summary writer is initialized, otherwise
sync_tensorboard does not work.
"""
import wandb
wandb_unique_id = f"uid_{experiment_name}"
print(f"Wandb using unique id {wandb_unique_id}")
cfg = self.cfg
# this can fail occasionally, so we try a couple more times
@retry(3, exceptions=(Exception,))
def init_wandb():
wandb.init(
project=cfg.wandb_project,
entity=cfg.wandb_entity,
group=cfg.wandb_group,
tags=cfg.wandb_tags,
sync_tensorboard=True,
id=wandb_unique_id,
name=experiment_name,
resume=True,
settings=wandb.Settings(start_method='fork'),
)
if cfg.wandb_logcode_dir:
wandb.run.log_code(root=cfg.wandb_logcode_dir)
print('wandb running directory........', wandb.run.dir)
print('Initializing WandB...')
try:
init_wandb()
except Exception as exc:
print(f'Could not initialize WandB! {exc}')
if isinstance(self.cfg, dict):
wandb.config.update(self.cfg, allow_val_change=True)
else:
wandb.config.update(omegaconf_to_dict(self.cfg), allow_val_change=True)
| 1,835 | Python | 31.785714 | 98 | 0.584196 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/utils/rlgames_utils.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
from collections import deque
from typing import Callable, Dict, Tuple, Any
import os
import gym
import numpy as np
import torch
from rl_games.common import env_configurations, vecenv
from rl_games.common.algo_observer import AlgoObserver
from isaacgymenvs.tasks import isaacgym_task_map
from isaacgymenvs.utils.utils import set_seed, flatten_dict
def multi_gpu_get_rank(multi_gpu):
if multi_gpu:
rank = int(os.getenv("LOCAL_RANK", "0"))
print("GPU rank: ", rank)
return rank
return 0
def get_rlgames_env_creator(
# used to create the vec task
seed: int,
task_config: dict,
task_name: str,
sim_device: str,
rl_device: str,
graphics_device_id: int,
headless: bool,
# used to handle multi-gpu case
multi_gpu: bool = False,
post_create_hook: Callable = None,
virtual_screen_capture: bool = False,
force_render: bool = False,
):
"""Parses the configuration parameters for the environment task and creates a VecTask
Args:
task_config: environment configuration.
task_name: Name of the task, used to evaluate based on the imported name (eg 'Trifinger')
sim_device: The type of env device, eg 'cuda:0'
rl_device: Device that RL will be done on, eg 'cuda:0'
graphics_device_id: Graphics device ID.
headless: Whether to run in headless mode.
multi_gpu: Whether to use multi gpu
post_create_hook: Hooks to be called after environment creation.
[Needed to setup WandB only for one of the RL Games instances when doing multiple GPUs]
virtual_screen_capture: Set to True to allow the users get captured screen in RGB array via `env.render(mode='rgb_array')`.
force_render: Set to True to always force rendering in the steps (if the `control_freq_inv` is greater than 1 we suggest stting this arg to True)
Returns:
A VecTaskPython object.
"""
def create_rlgpu_env():
"""
Creates the task from configurations and wraps it using RL-games wrappers if required.
"""
if multi_gpu:
local_rank = int(os.getenv("LOCAL_RANK", "0"))
global_rank = int(os.getenv("RANK", "0"))
# local rank of the GPU in a node
local_rank = int(os.getenv("LOCAL_RANK", "0"))
# global rank of the GPU
global_rank = int(os.getenv("RANK", "0"))
# total number of GPUs across all nodes
world_size = int(os.getenv("WORLD_SIZE", "1"))
print(f"global_rank = {global_rank} local_rank = {local_rank} world_size = {world_size}")
_sim_device = f'cuda:{local_rank}'
_rl_device = f'cuda:{local_rank}'
task_config['rank'] = local_rank
task_config['rl_device'] = _rl_device
else:
_sim_device = sim_device
_rl_device = rl_device
# create native task and pass custom config
env = isaacgym_task_map[task_name](
cfg=task_config,
rl_device=_rl_device,
sim_device=_sim_device,
graphics_device_id=graphics_device_id,
headless=headless,
virtual_screen_capture=virtual_screen_capture,
force_render=force_render,
)
if post_create_hook is not None:
post_create_hook()
return env
return create_rlgpu_env
class RLGPUAlgoObserver(AlgoObserver):
"""Allows us to log stats from the env along with the algorithm running stats. """
def __init__(self):
super().__init__()
self.algo = None
self.writer = None
self.ep_infos = []
self.direct_info = {}
self.episode_cumulative = dict()
self.episode_cumulative_avg = dict()
self.new_finished_episodes = False
def after_init(self, algo):
self.algo = algo
self.writer = self.algo.writer
def process_infos(self, infos, done_indices):
assert isinstance(infos, dict), 'RLGPUAlgoObserver expects dict info'
if not isinstance(infos, dict):
return
if 'episode' in infos:
self.ep_infos.append(infos['episode'])
if 'episode_cumulative' in infos:
for key, value in infos['episode_cumulative'].items():
if key not in self.episode_cumulative:
self.episode_cumulative[key] = torch.zeros_like(value)
self.episode_cumulative[key] += value
for done_idx in done_indices:
self.new_finished_episodes = True
done_idx = done_idx.item()
for key, value in infos['episode_cumulative'].items():
if key not in self.episode_cumulative_avg:
self.episode_cumulative_avg[key] = deque([], maxlen=self.algo.games_to_track)
self.episode_cumulative_avg[key].append(self.episode_cumulative[key][done_idx].item())
self.episode_cumulative[key][done_idx] = 0
# turn nested infos into summary keys (i.e. infos['scalars']['lr'] -> infos['scalars/lr']
if len(infos) > 0 and isinstance(infos, dict): # allow direct logging from env
infos_flat = flatten_dict(infos, prefix='', separator='/')
self.direct_info = {}
for k, v in infos_flat.items():
# only log scalars
if isinstance(v, float) or isinstance(v, int) or (isinstance(v, torch.Tensor) and len(v.shape) == 0):
self.direct_info[k] = v
def after_print_stats(self, frame, epoch_num, total_time):
if self.ep_infos:
for key in self.ep_infos[0]:
infotensor = torch.tensor([], device=self.algo.device)
for ep_info in self.ep_infos:
# handle scalar and zero dimensional tensor infos
if not isinstance(ep_info[key], torch.Tensor):
ep_info[key] = torch.Tensor([ep_info[key]])
if len(ep_info[key].shape) == 0:
ep_info[key] = ep_info[key].unsqueeze(0)
infotensor = torch.cat((infotensor, ep_info[key].to(self.algo.device)))
value = torch.mean(infotensor)
self.writer.add_scalar('Episode/' + key, value, epoch_num)
self.ep_infos.clear()
# log these if and only if we have new finished episodes
if self.new_finished_episodes:
for key in self.episode_cumulative_avg:
self.writer.add_scalar(f'episode_cumulative/{key}', np.mean(self.episode_cumulative_avg[key]), frame)
self.writer.add_scalar(f'episode_cumulative_min/{key}_min', np.min(self.episode_cumulative_avg[key]), frame)
self.writer.add_scalar(f'episode_cumulative_max/{key}_max', np.max(self.episode_cumulative_avg[key]), frame)
self.new_finished_episodes = False
for k, v in self.direct_info.items():
self.writer.add_scalar(f'{k}/frame', v, frame)
self.writer.add_scalar(f'{k}/iter', v, epoch_num)
self.writer.add_scalar(f'{k}/time', v, total_time)
class MultiObserver(AlgoObserver):
"""Meta-observer that allows the user to add several observers."""
def __init__(self, observers_):
super().__init__()
self.observers = observers_
def _call_multi(self, method, *args_, **kwargs_):
for o in self.observers:
getattr(o, method)(*args_, **kwargs_)
def before_init(self, base_name, config, experiment_name):
self._call_multi('before_init', base_name, config, experiment_name)
def after_init(self, algo):
self._call_multi('after_init', algo)
def process_infos(self, infos, done_indices):
self._call_multi('process_infos', infos, done_indices)
def after_steps(self):
self._call_multi('after_steps')
def after_clear_stats(self):
self._call_multi('after_clear_stats')
def after_print_stats(self, frame, epoch_num, total_time):
self._call_multi('after_print_stats', frame, epoch_num, total_time)
class RLGPUEnv(vecenv.IVecEnv):
def __init__(self, config_name, num_actors, **kwargs):
self.env = env_configurations.configurations[config_name]['env_creator'](**kwargs)
def step(self, actions):
return self.env.step(actions)
def reset(self):
return self.env.reset()
def reset_done(self):
return self.env.reset_done()
def get_number_of_agents(self):
return self.env.get_number_of_agents()
def get_env_info(self):
info = {}
info['action_space'] = self.env.action_space
info['observation_space'] = self.env.observation_space
if hasattr(self.env, "amp_observation_space"):
info['amp_observation_space'] = self.env.amp_observation_space
if self.env.num_states > 0:
info['state_space'] = self.env.state_space
print(info['action_space'], info['observation_space'], info['state_space'])
else:
print(info['action_space'], info['observation_space'])
return info
def set_train_info(self, env_frames, *args_, **kwargs_):
"""
Send the information in the direction algo->environment.
Most common use case: tell the environment how far along we are in the training process. This is useful
for implementing curriculums and things such as that.
"""
if hasattr(self.env, 'set_train_info'):
self.env.set_train_info(env_frames, *args_, **kwargs_)
def get_env_state(self):
"""
Return serializable environment state to be saved to checkpoint.
Can be used for stateful training sessions, i.e. with adaptive curriculums.
"""
if hasattr(self.env, 'get_env_state'):
return self.env.get_env_state()
else:
return None
def set_env_state(self, env_state):
if hasattr(self.env, 'set_env_state'):
self.env.set_env_state(env_state)
class ComplexObsRLGPUEnv(vecenv.IVecEnv):
def __init__(
self,
config_name,
num_actors,
obs_spec: Dict[str, Dict],
**kwargs,
):
"""RLGPU wrapper for Isaac Gym tasks.
Args:
config_name: Name of rl games env_configurations configuration to use.
obs_spec: Dictinoary listing out specification for observations to use.
eg.
{
'obs': {'names': ['obs_1', 'obs_2'], 'concat': True, space_name: 'observation_space'},},
'states': {'names': ['state_1', 'state_2'], 'concat': False, space_name: 'state_space'},}
}
Within each, if 'concat' is set, concatenates all the given observaitons into a single tensor of dim (num_envs, sum(num_obs)).
Assumes that each indivdual observation is single dimensional (ie (num_envs, k), so image observation isn't supported).
Currently applies to student and teacher both.
"space_name" is given into the env info which RL Games reads to find the space shape
"""
self.env = env_configurations.configurations[config_name]['env_creator'](**kwargs)
self.obs_spec = obs_spec
def _generate_obs(
self, env_obs: Dict[str, torch.Tensor]
) -> Dict[str, Dict[str, torch.Tensor]]:
"""Generate the RL Games observations given the observations from the environment.
Args:
env_obs: environment observations
Returns:
Dict which contains keys with values corresponding to observations.
"""
# rl games expects a dictionary with 'obs' and 'states'
# corresponding to the policy observations and possible asymmetric
# observations respectively
rlgames_obs = {k: self.gen_obs_dict(env_obs, v['names'], v['concat']) for k, v in self.obs_spec.items()}
return rlgames_obs
def step(
self, action: torch.Tensor
) -> Tuple[
Dict[str, Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, Dict[str, Any]
]:
"""Step the Isaac Gym task.
Args:
action: Enivronment action.
Returns:
observations, rewards, dones, infos
Returned obeservations are a dict which contains key 'obs' corresponding to a dictionary of observations,
and possible 'states' key corresponding to dictionary of privileged observations.
"""
env_obs, rewards, dones, infos = self.env.step(action)
rlgames_obs = self._generate_obs(env_obs)
return rlgames_obs, rewards, dones, infos
def reset(self) -> Dict[str, Dict[str, torch.Tensor]]:
env_obs = self.env.reset()
return self._generate_obs(env_obs)
def get_number_of_agents(self) -> int:
return self.env.get_number_of_agents()
def get_env_info(self) -> Dict[str, gym.spaces.Space]:
"""Gets information on the environment's observation, action, and privileged observation (states) spaces."""
info = {}
info["action_space"] = self.env.action_space
for k, v in self.obs_spec.items():
info[v['space_name']] = self.gen_obs_space(v['names'], v['concat'])
return info
def gen_obs_dict(self, obs_dict, obs_names, concat):
"""Generate the RL Games observations given the observations from the environment."""
if concat:
return torch.cat([obs_dict[name] for name in obs_names], dim=1)
else:
return {k: obs_dict[k] for k in obs_names}
def gen_obs_space(self, obs_names, concat):
"""Generate the RL Games observation space given the observations from the environment."""
if concat:
return gym.spaces.Box(
low=-np.Inf,
high=np.Inf,
shape=(sum([self.env.observation_space[s].shape[0] for s in obs_names]),),
dtype=np.float32,
)
else:
return gym.spaces.Dict(
{k: self.env.observation_space[k] for k in obs_names}
)
def set_train_info(self, env_frames, *args_, **kwargs_):
"""
Send the information in the direction algo->environment.
Most common use case: tell the environment how far along we are in the training process. This is useful
for implementing curriculums and things such as that.
"""
if hasattr(self.env, 'set_train_info'):
self.env.set_train_info(env_frames, *args_, **kwargs_)
def get_env_state(self):
"""
Return serializable environment state to be saved to checkpoint.
Can be used for stateful training sessions, i.e. with adaptive curriculums.
"""
if hasattr(self.env, 'get_env_state'):
return self.env.get_env_state()
else:
return None
def set_env_state(self, env_state):
if hasattr(self.env, 'set_env_state'):
self.env.set_env_state(env_state)
| 16,837 | Python | 38.806146 | 153 | 0.612104 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/utils/torch_jit_utils.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import isaacgym
import torch
import torch.nn.functional as F
import numpy as np
def to_torch(x, dtype=torch.float, device='cuda:0', requires_grad=False):
return torch.tensor(x, dtype=dtype, device=device, requires_grad=requires_grad)
@torch.jit.script
def quat_mul(a, b):
assert a.shape == b.shape
shape = a.shape
a = a.reshape(-1, 4)
b = b.reshape(-1, 4)
x1, y1, z1, w1 = a[:, 0], a[:, 1], a[:, 2], a[:, 3]
x2, y2, z2, w2 = b[:, 0], b[:, 1], b[:, 2], b[:, 3]
ww = (z1 + x1) * (x2 + y2)
yy = (w1 - y1) * (w2 + z2)
zz = (w1 + y1) * (w2 - z2)
xx = ww + yy + zz
qq = 0.5 * (xx + (z1 - x1) * (x2 - y2))
w = qq - ww + (z1 - y1) * (y2 - z2)
x = qq - xx + (x1 + w1) * (x2 + w2)
y = qq - yy + (w1 - x1) * (y2 + z2)
z = qq - zz + (z1 + y1) * (w2 - x2)
quat = torch.stack([x, y, z, w], dim=-1).view(shape)
return quat
@torch.jit.script
def normalize(x, eps: float = 1e-9):
return x / x.norm(p=2, dim=-1).clamp(min=eps, max=None).unsqueeze(-1)
@torch.jit.script
def quat_apply(a, b):
shape = b.shape
a = a.reshape(-1, 4)
b = b.reshape(-1, 3)
xyz = a[:, :3]
t = xyz.cross(b, dim=-1) * 2
return (b + a[:, 3:] * t + xyz.cross(t, dim=-1)).view(shape)
@torch.jit.script
def quat_rotate(q, v):
shape = q.shape
q_w = q[:, -1]
q_vec = q[:, :3]
a = v * (2.0 * q_w ** 2 - 1.0).unsqueeze(-1)
b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0
c = q_vec * \
torch.bmm(q_vec.view(shape[0], 1, 3), v.view(
shape[0], 3, 1)).squeeze(-1) * 2.0
return a + b + c
@torch.jit.script
def quat_rotate_inverse(q, v):
shape = q.shape
q_w = q[:, -1]
q_vec = q[:, :3]
a = v * (2.0 * q_w ** 2 - 1.0).unsqueeze(-1)
b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0
c = q_vec * \
torch.bmm(q_vec.view(shape[0], 1, 3), v.view(
shape[0], 3, 1)).squeeze(-1) * 2.0
return a - b + c
@torch.jit.script
def quat_conjugate(a):
shape = a.shape
a = a.reshape(-1, 4)
return torch.cat((-a[:, :3], a[:, -1:]), dim=-1).view(shape)
@torch.jit.script
def quat_unit(a):
return normalize(a)
@torch.jit.script
def quat_from_angle_axis(angle, axis):
theta = (angle / 2).unsqueeze(-1)
xyz = normalize(axis) * theta.sin()
w = theta.cos()
return quat_unit(torch.cat([xyz, w], dim=-1))
@torch.jit.script
def normalize_angle(x):
return torch.atan2(torch.sin(x), torch.cos(x))
@torch.jit.script
def tf_inverse(q, t):
q_inv = quat_conjugate(q)
return q_inv, -quat_apply(q_inv, t)
@torch.jit.script
def tf_apply(q, t, v):
return quat_apply(q, v) + t
@torch.jit.script
def tf_vector(q, v):
return quat_apply(q, v)
@torch.jit.script
def tf_combine(q1, t1, q2, t2):
return quat_mul(q1, q2), quat_apply(q1, t2) + t1
@torch.jit.script
def get_basis_vector(q, v):
return quat_rotate(q, v)
def get_axis_params(value, axis_idx, x_value=0., dtype=float, n_dims=3):
"""construct arguments to `Vec` according to axis index.
"""
zs = np.zeros((n_dims,))
assert axis_idx < n_dims, "the axis dim should be within the vector dimensions"
zs[axis_idx] = 1.
params = np.where(zs == 1., value, zs)
params[0] = x_value
return list(params.astype(dtype))
@torch.jit.script
def copysign(a, b):
# type: (float, Tensor) -> Tensor
a = torch.tensor(a, device=b.device, dtype=torch.float).repeat(b.shape[0])
return torch.abs(a) * torch.sign(b)
@torch.jit.script
def get_euler_xyz(q):
qx, qy, qz, qw = 0, 1, 2, 3
# roll (x-axis rotation)
sinr_cosp = 2.0 * (q[:, qw] * q[:, qx] + q[:, qy] * q[:, qz])
cosr_cosp = q[:, qw] * q[:, qw] - q[:, qx] * \
q[:, qx] - q[:, qy] * q[:, qy] + q[:, qz] * q[:, qz]
roll = torch.atan2(sinr_cosp, cosr_cosp)
# pitch (y-axis rotation)
sinp = 2.0 * (q[:, qw] * q[:, qy] - q[:, qz] * q[:, qx])
pitch = torch.where(torch.abs(sinp) >= 1, copysign(
np.pi / 2.0, sinp), torch.asin(sinp))
# yaw (z-axis rotation)
siny_cosp = 2.0 * (q[:, qw] * q[:, qz] + q[:, qx] * q[:, qy])
cosy_cosp = q[:, qw] * q[:, qw] + q[:, qx] * \
q[:, qx] - q[:, qy] * q[:, qy] - q[:, qz] * q[:, qz]
yaw = torch.atan2(siny_cosp, cosy_cosp)
return roll % (2*np.pi), pitch % (2*np.pi), yaw % (2*np.pi)
@torch.jit.script
def quat_from_euler_xyz(roll, pitch, yaw):
cy = torch.cos(yaw * 0.5)
sy = torch.sin(yaw * 0.5)
cr = torch.cos(roll * 0.5)
sr = torch.sin(roll * 0.5)
cp = torch.cos(pitch * 0.5)
sp = torch.sin(pitch * 0.5)
qw = cy * cr * cp + sy * sr * sp
qx = cy * sr * cp - sy * cr * sp
qy = cy * cr * sp + sy * sr * cp
qz = sy * cr * cp - cy * sr * sp
return torch.stack([qx, qy, qz, qw], dim=-1)
@torch.jit.script
def torch_rand_float(lower, upper, shape, device):
# type: (float, float, Tuple[int, int], str) -> Tensor
return (upper - lower) * torch.rand(*shape, device=device) + lower
@torch.jit.script
def torch_random_dir_2(shape, device):
# type: (Tuple[int, int], str) -> Tensor
angle = torch_rand_float(-np.pi, np.pi, shape, device).squeeze(-1)
return torch.stack([torch.cos(angle), torch.sin(angle)], dim=-1)
@torch.jit.script
def tensor_clamp(t, min_t, max_t):
return torch.max(torch.min(t, max_t), min_t)
@torch.jit.script
def scale(x, lower, upper):
return (0.5 * (x + 1.0) * (upper - lower) + lower)
@torch.jit.script
def unscale(x, lower, upper):
return (2.0 * x - upper - lower) / (upper - lower)
def unscale_np(x, lower, upper):
return (2.0 * x - upper - lower) / (upper - lower)
@torch.jit.script
def compute_heading_and_up(
torso_rotation, inv_start_rot, to_target, vec0, vec1, up_idx
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, int) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]
num_envs = torso_rotation.shape[0]
target_dirs = normalize(to_target)
torso_quat = quat_mul(torso_rotation, inv_start_rot)
up_vec = get_basis_vector(torso_quat, vec1).view(num_envs, 3)
heading_vec = get_basis_vector(torso_quat, vec0).view(num_envs, 3)
up_proj = up_vec[:, up_idx]
heading_proj = torch.bmm(heading_vec.view(
num_envs, 1, 3), target_dirs.view(num_envs, 3, 1)).view(num_envs)
return torso_quat, up_proj, heading_proj, up_vec, heading_vec
@torch.jit.script
def compute_rot(torso_quat, velocity, ang_velocity, targets, torso_positions):
vel_loc = quat_rotate_inverse(torso_quat, velocity)
angvel_loc = quat_rotate_inverse(torso_quat, ang_velocity)
roll, pitch, yaw = get_euler_xyz(torso_quat)
walk_target_angle = torch.atan2(targets[:, 2] - torso_positions[:, 2],
targets[:, 0] - torso_positions[:, 0])
angle_to_target = walk_target_angle - yaw
return vel_loc, angvel_loc, roll, pitch, yaw, angle_to_target
@torch.jit.script
def quat_axis(q, axis=0):
# type: (Tensor, int) -> Tensor
basis_vec = torch.zeros(q.shape[0], 3, device=q.device)
basis_vec[:, axis] = 1
return quat_rotate(q, basis_vec)
"""
Normalization and Denormalization of Tensors
"""
@torch.jit.script
def scale_transform(x: torch.Tensor, lower: torch.Tensor, upper: torch.Tensor) -> torch.Tensor:
"""
Normalizes a given input tensor to a range of [-1, 1].
@note It uses pytorch broadcasting functionality to deal with batched input.
Args:
x: Input tensor of shape (N, dims).
lower: The minimum value of the tensor. Shape (dims,)
upper: The maximum value of the tensor. Shape (dims,)
Returns:
Normalized transform of the tensor. Shape (N, dims)
"""
# default value of center
offset = (lower + upper) * 0.5
# return normalized tensor
return 2 * (x - offset) / (upper - lower)
@torch.jit.script
def unscale_transform(x: torch.Tensor, lower: torch.Tensor, upper: torch.Tensor) -> torch.Tensor:
"""
Denormalizes a given input tensor from range of [-1, 1] to (lower, upper).
@note It uses pytorch broadcasting functionality to deal with batched input.
Args:
x: Input tensor of shape (N, dims).
lower: The minimum value of the tensor. Shape (dims,)
upper: The maximum value of the tensor. Shape (dims,)
Returns:
Denormalized transform of the tensor. Shape (N, dims)
"""
# default value of center
offset = (lower + upper) * 0.5
# return normalized tensor
return x * (upper - lower) * 0.5 + offset
@torch.jit.script
def saturate(x: torch.Tensor, lower: torch.Tensor, upper: torch.Tensor) -> torch.Tensor:
"""
Clamps a given input tensor to (lower, upper).
@note It uses pytorch broadcasting functionality to deal with batched input.
Args:
x: Input tensor of shape (N, dims).
lower: The minimum value of the tensor. Shape (dims,)
upper: The maximum value of the tensor. Shape (dims,)
Returns:
Clamped transform of the tensor. Shape (N, dims)
"""
return torch.max(torch.min(x, upper), lower)
"""
Rotation conversions
"""
@torch.jit.script
def quat_diff_rad(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
"""
Get the difference in radians between two quaternions.
Args:
a: first quaternion, shape (N, 4)
b: second quaternion, shape (N, 4)
Returns:
Difference in radians, shape (N,)
"""
b_conj = quat_conjugate(b)
mul = quat_mul(a, b_conj)
# 2 * torch.acos(torch.abs(mul[:, -1]))
return 2.0 * torch.asin(
torch.clamp(
torch.norm(
mul[:, 0:3],
p=2, dim=-1), max=1.0)
)
@torch.jit.script
def local_to_world_space(pos_offset_local: torch.Tensor, pose_global: torch.Tensor):
""" Convert a point from the local frame to the global frame
Args:
pos_offset_local: Point in local frame. Shape: [N, 3]
pose_global: The spatial pose of this point. Shape: [N, 7]
Returns:
Position in the global frame. Shape: [N, 3]
"""
quat_pos_local = torch.cat(
[pos_offset_local, torch.zeros(pos_offset_local.shape[0], 1, dtype=torch.float32, device=pos_offset_local.device)],
dim=-1
)
quat_global = pose_global[:, 3:7]
quat_global_conj = quat_conjugate(quat_global)
pos_offset_global = quat_mul(quat_global, quat_mul(quat_pos_local, quat_global_conj))[:, 0:3]
result_pos_gloal = pos_offset_global + pose_global[:, 0:3]
return result_pos_gloal
# NB: do not make this function jit, since it is passed around as an argument.
def normalise_quat_in_pose(pose):
"""Takes a pose and normalises the quaternion portion of it.
Args:
pose: shape N, 7
Returns:
Pose with normalised quat. Shape N, 7
"""
pos = pose[:, 0:3]
quat = pose[:, 3:7]
quat /= torch.norm(quat, dim=-1, p=2).reshape(-1, 1)
return torch.cat([pos, quat], dim=-1)
@torch.jit.script
def my_quat_rotate(q, v):
shape = q.shape
q_w = q[:, -1]
q_vec = q[:, :3]
a = v * (2.0 * q_w ** 2 - 1.0).unsqueeze(-1)
b = torch.cross(q_vec, v, dim=-1) * q_w.unsqueeze(-1) * 2.0
c = q_vec * \
torch.bmm(q_vec.view(shape[0], 1, 3), v.view(
shape[0], 3, 1)).squeeze(-1) * 2.0
return a + b + c
@torch.jit.script
def quat_to_angle_axis(q):
# type: (Tensor) -> Tuple[Tensor, Tensor]
# computes axis-angle representation from quaternion q
# q must be normalized
min_theta = 1e-5
qx, qy, qz, qw = 0, 1, 2, 3
sin_theta = torch.sqrt(1 - q[..., qw] * q[..., qw])
angle = 2 * torch.acos(q[..., qw])
angle = normalize_angle(angle)
sin_theta_expand = sin_theta.unsqueeze(-1)
axis = q[..., qx:qw] / sin_theta_expand
mask = sin_theta > min_theta
default_axis = torch.zeros_like(axis)
default_axis[..., -1] = 1
angle = torch.where(mask, angle, torch.zeros_like(angle))
mask_expand = mask.unsqueeze(-1)
axis = torch.where(mask_expand, axis, default_axis)
return angle, axis
@torch.jit.script
def angle_axis_to_exp_map(angle, axis):
# type: (Tensor, Tensor) -> Tensor
# compute exponential map from axis-angle
angle_expand = angle.unsqueeze(-1)
exp_map = angle_expand * axis
return exp_map
@torch.jit.script
def quat_to_exp_map(q):
# type: (Tensor) -> Tensor
# compute exponential map from quaternion
# q must be normalized
angle, axis = quat_to_angle_axis(q)
exp_map = angle_axis_to_exp_map(angle, axis)
return exp_map
def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
"""
Convert rotations given as quaternions to rotation matrices.
Args:
quaternions: quaternions with real part first,
as tensor of shape (..., 4).
Returns:
Rotation matrices as tensor of shape (..., 3, 3).
"""
r, i, j, k = torch.unbind(quaternions, -1)
two_s = 2.0 / (quaternions * quaternions).sum(-1)
mat = torch.stack(
(
1 - two_s * (j * j + k * k),
two_s * (i * j - k * r),
two_s * (i * k + j * r),
two_s * (i * j + k * r),
1 - two_s * (i * i + k * k),
two_s * (j * k - i * r),
two_s * (i * k - j * r),
two_s * (j * k + i * r),
1 - two_s * (i * i + j * j),
),
-1,
)
return mat.reshape(quaternions.shape[:-1] + (3, 3))
def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
"""
Returns torch.sqrt(torch.max(0, x))
subgradient is zero where x is 0.
"""
ret = torch.zeros_like(x)
positive_mask = x > 0
ret[positive_mask] = torch.sqrt(x[positive_mask])
return ret
def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
"""
Convert rotations given as rotation matrices to quaternions.
Args:
matrix: Rotation matrices as tensor of shape (..., 3, 3).
Returns:
quaternions with real part first, as tensor of shape (..., 4).
"""
if matrix.size(-1) != 3 or matrix.size(-2) != 3:
raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")
batch_dim = matrix.shape[:-2]
m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
matrix.reshape(batch_dim + (9,)), dim=-1
)
q_abs = _sqrt_positive_part(
torch.stack(
[
1.0 + m00 + m11 + m22,
1.0 + m00 - m11 - m22,
1.0 - m00 + m11 - m22,
1.0 - m00 - m11 + m22,
],
dim=-1,
)
)
quat_by_rijk = torch.stack(
[
torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
],
dim=-2,
)
flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))
return quat_candidates[
F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :
].reshape(batch_dim + (4,))
@torch.jit.script
def quat_to_tan_norm(q):
# type: (Tensor) -> Tensor
# represents a rotation using the tangent and normal vectors
ref_tan = torch.zeros_like(q[..., 0:3])
ref_tan[..., 0] = 1
tan = my_quat_rotate(q, ref_tan)
ref_norm = torch.zeros_like(q[..., 0:3])
ref_norm[..., -1] = 1
norm = my_quat_rotate(q, ref_norm)
norm_tan = torch.cat([tan, norm], dim=len(tan.shape) - 1)
return norm_tan
@torch.jit.script
def euler_xyz_to_exp_map(roll, pitch, yaw):
# type: (Tensor, Tensor, Tensor) -> Tensor
q = quat_from_euler_xyz(roll, pitch, yaw)
exp_map = quat_to_exp_map(q)
return exp_map
@torch.jit.script
def exp_map_to_angle_axis(exp_map):
min_theta = 1e-5
angle = torch.norm(exp_map, dim=-1)
angle_exp = torch.unsqueeze(angle, dim=-1)
axis = exp_map / angle_exp
angle = normalize_angle(angle)
default_axis = torch.zeros_like(exp_map)
default_axis[..., -1] = 1
mask = angle > min_theta
angle = torch.where(mask, angle, torch.zeros_like(angle))
mask_expand = mask.unsqueeze(-1)
axis = torch.where(mask_expand, axis, default_axis)
return angle, axis
@torch.jit.script
def exp_map_to_quat(exp_map):
angle, axis = exp_map_to_angle_axis(exp_map)
q = quat_from_angle_axis(angle, axis)
return q
@torch.jit.script
def slerp(q0, q1, t):
# type: (Tensor, Tensor, Tensor) -> Tensor
qx, qy, qz, qw = 0, 1, 2, 3
cos_half_theta = q0[..., qw] * q1[..., qw] \
+ q0[..., qx] * q1[..., qx] \
+ q0[..., qy] * q1[..., qy] \
+ q0[..., qz] * q1[..., qz]
neg_mask = cos_half_theta < 0
q1 = q1.clone()
q1[neg_mask] = -q1[neg_mask]
cos_half_theta = torch.abs(cos_half_theta)
cos_half_theta = torch.unsqueeze(cos_half_theta, dim=-1)
half_theta = torch.acos(cos_half_theta);
sin_half_theta = torch.sqrt(1.0 - cos_half_theta * cos_half_theta)
ratioA = torch.sin((1 - t) * half_theta) / sin_half_theta
ratioB = torch.sin(t * half_theta) / sin_half_theta;
new_q_x = ratioA * q0[..., qx:qx+1] + ratioB * q1[..., qx:qx+1]
new_q_y = ratioA * q0[..., qy:qy+1] + ratioB * q1[..., qy:qy+1]
new_q_z = ratioA * q0[..., qz:qz+1] + ratioB * q1[..., qz:qz+1]
new_q_w = ratioA * q0[..., qw:qw+1] + ratioB * q1[..., qw:qw+1]
cat_dim = len(new_q_w.shape) - 1
new_q = torch.cat([new_q_x, new_q_y, new_q_z, new_q_w], dim=cat_dim)
new_q = torch.where(torch.abs(sin_half_theta) < 0.001, 0.5 * q0 + 0.5 * q1, new_q)
new_q = torch.where(torch.abs(cos_half_theta) >= 1, q0, new_q)
return new_q
@torch.jit.script
def calc_heading(q):
# type: (Tensor) -> Tensor
# calculate heading direction from quaternion
# the heading is the direction on the xy plane
# q must be normalized
ref_dir = torch.zeros_like(q[..., 0:3])
ref_dir[..., 0] = 1
rot_dir = my_quat_rotate(q, ref_dir)
heading = torch.atan2(rot_dir[..., 1], rot_dir[..., 0])
return heading
@torch.jit.script
def calc_heading_quat(q):
# type: (Tensor) -> Tensor
# calculate heading rotation from quaternion
# the heading is the direction on the xy plane
# q must be normalized
heading = calc_heading(q)
axis = torch.zeros_like(q[..., 0:3])
axis[..., 2] = 1
heading_q = quat_from_angle_axis(heading, axis)
return heading_q
@torch.jit.script
def calc_heading_quat_inv(q):
# type: (Tensor) -> Tensor
# calculate heading rotation from quaternion
# the heading is the direction on the xy plane
# q must be normalized
heading = calc_heading(q)
axis = torch.zeros_like(q[..., 0:3])
axis[..., 2] = 1
heading_q = quat_from_angle_axis(-heading, axis)
return heading_q
# EOF
| 20,579 | Python | 29.716418 | 123 | 0.588707 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/utils/reformat.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from omegaconf import DictConfig, OmegaConf
from typing import Dict
def omegaconf_to_dict(d: DictConfig)->Dict:
"""Converts an omegaconf DictConfig to a python Dict, respecting variable interpolation."""
ret = {}
for k, v in d.items():
if isinstance(v, DictConfig):
ret[k] = omegaconf_to_dict(v)
else:
ret[k] = v
return ret
def print_dict(val, nesting: int = -4, start: bool = True):
"""Outputs a nested dictionory."""
if type(val) == dict:
if not start:
print('')
nesting += 4
for k in val:
print(nesting * ' ', end='')
print(k, end=': ')
print_dict(val[k], nesting, start=False)
else:
print(val)
# EOF
| 2,314 | Python | 40.339285 | 95 | 0.708729 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/utils/dr_utils.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
from bisect import bisect
from isaacgym import gymapi
def get_property_setter_map(gym):
property_to_setters = {
"dof_properties": gym.set_actor_dof_properties,
"tendon_properties": gym.set_actor_tendon_properties,
"rigid_body_properties": gym.set_actor_rigid_body_properties,
"rigid_shape_properties": gym.set_actor_rigid_shape_properties,
"sim_params": gym.set_sim_params,
}
return property_to_setters
def get_property_getter_map(gym):
property_to_getters = {
"dof_properties": gym.get_actor_dof_properties,
"tendon_properties": gym.get_actor_tendon_properties,
"rigid_body_properties": gym.get_actor_rigid_body_properties,
"rigid_shape_properties": gym.get_actor_rigid_shape_properties,
"sim_params": gym.get_sim_params,
}
return property_to_getters
def get_default_setter_args(gym):
property_to_setter_args = {
"dof_properties": [],
"tendon_properties": [],
"rigid_body_properties": [True],
"rigid_shape_properties": [],
"sim_params": [],
}
return property_to_setter_args
def generate_random_samples(attr_randomization_params, shape, curr_gym_step_count,
extern_sample=None):
rand_range = attr_randomization_params['range']
distribution = attr_randomization_params['distribution']
sched_type = attr_randomization_params['schedule'] if 'schedule' in attr_randomization_params else None
sched_step = attr_randomization_params['schedule_steps'] if 'schedule' in attr_randomization_params else None
operation = attr_randomization_params['operation']
if sched_type == 'linear':
sched_scaling = 1 / sched_step * min(curr_gym_step_count, sched_step)
elif sched_type == 'constant':
sched_scaling = 0 if curr_gym_step_count < sched_step else 1
else:
sched_scaling = 1
if extern_sample is not None:
sample = extern_sample
if operation == 'additive':
sample *= sched_scaling
elif operation == 'scaling':
sample = sample * sched_scaling + 1 * (1 - sched_scaling)
elif distribution == "gaussian":
mu, var = rand_range
if operation == 'additive':
mu *= sched_scaling
var *= sched_scaling
elif operation == 'scaling':
var = var * sched_scaling # scale up var over time
mu = mu * sched_scaling + 1 * (1 - sched_scaling) # linearly interpolate
sample = np.random.normal(mu, var, shape)
elif distribution == "loguniform":
lo, hi = rand_range
if operation == 'additive':
lo *= sched_scaling
hi *= sched_scaling
elif operation == 'scaling':
lo = lo * sched_scaling + 1 * (1 - sched_scaling)
hi = hi * sched_scaling + 1 * (1 - sched_scaling)
sample = np.exp(np.random.uniform(np.log(lo), np.log(hi), shape))
elif distribution == "uniform":
lo, hi = rand_range
if operation == 'additive':
lo *= sched_scaling
hi *= sched_scaling
elif operation == 'scaling':
lo = lo * sched_scaling + 1 * (1 - sched_scaling)
hi = hi * sched_scaling + 1 * (1 - sched_scaling)
sample = np.random.uniform(lo, hi, shape)
return sample
def get_bucketed_val(new_prop_val, attr_randomization_params):
if attr_randomization_params['distribution'] == 'uniform':
# range of buckets defined by uniform distribution
lo, hi = attr_randomization_params['range'][0], attr_randomization_params['range'][1]
else:
# for gaussian, set range of buckets to be 2 stddev away from mean
lo = attr_randomization_params['range'][0] - 2 * np.sqrt(attr_randomization_params['range'][1])
hi = attr_randomization_params['range'][0] + 2 * np.sqrt(attr_randomization_params['range'][1])
num_buckets = attr_randomization_params['num_buckets']
buckets = [(hi - lo) * i / num_buckets + lo for i in range(num_buckets)]
return buckets[bisect(buckets, new_prop_val) - 1]
def apply_random_samples(prop, og_prop, attr, attr_randomization_params,
curr_gym_step_count, extern_sample=None, bucketing_randomization_params=None):
"""
@params:
prop: property we want to randomise
og_prop: the original property and its value
attr: which particular attribute we want to randomise e.g. damping, stiffness
attr_randomization_params: the attribute randomisation meta-data e.g. distr, range, schedule
curr_gym_step_count: gym steps so far
"""
if isinstance(prop, gymapi.SimParams):
if attr == 'gravity':
sample = generate_random_samples(attr_randomization_params, 3, curr_gym_step_count)
if attr_randomization_params['operation'] == 'scaling':
prop.gravity.x = og_prop['gravity'].x * sample[0]
prop.gravity.y = og_prop['gravity'].y * sample[1]
prop.gravity.z = og_prop['gravity'].z * sample[2]
elif attr_randomization_params['operation'] == 'additive':
prop.gravity.x = og_prop['gravity'].x + sample[0]
prop.gravity.y = og_prop['gravity'].y + sample[1]
prop.gravity.z = og_prop['gravity'].z + sample[2]
if attr == 'rest_offset':
sample = generate_random_samples(attr_randomization_params, 1, curr_gym_step_count)
prop.physx.rest_offset = sample
elif isinstance(prop, np.ndarray):
sample = generate_random_samples(attr_randomization_params, prop[attr].shape,
curr_gym_step_count, extern_sample)
if attr_randomization_params['operation'] == 'scaling':
new_prop_val = og_prop[attr] * sample
elif attr_randomization_params['operation'] == 'additive':
new_prop_val = og_prop[attr] + sample
if 'num_buckets' in attr_randomization_params and attr_randomization_params['num_buckets'] > 0:
new_prop_val = get_bucketed_val(new_prop_val, attr_randomization_params)
prop[attr] = new_prop_val
else:
sample = generate_random_samples(attr_randomization_params, 1,
curr_gym_step_count, extern_sample)
cur_attr_val = og_prop[attr]
if attr_randomization_params['operation'] == 'scaling':
new_prop_val = cur_attr_val * sample
elif attr_randomization_params['operation'] == 'additive':
new_prop_val = cur_attr_val + sample
if 'num_buckets' in attr_randomization_params and attr_randomization_params['num_buckets'] > 0:
if bucketing_randomization_params is None:
new_prop_val = get_bucketed_val(new_prop_val, attr_randomization_params)
else:
new_prop_val = get_bucketed_val(new_prop_val, bucketing_randomization_params)
setattr(prop, attr, new_prop_val)
def check_buckets(gym, envs, dr_params):
total_num_buckets = 0
for actor, actor_properties in dr_params["actor_params"].items():
cur_num_buckets = 0
if 'rigid_shape_properties' in actor_properties.keys():
prop_attrs = actor_properties['rigid_shape_properties']
if 'restitution' in prop_attrs and 'num_buckets' in prop_attrs['restitution']:
cur_num_buckets = prop_attrs['restitution']['num_buckets']
if 'friction' in prop_attrs and 'num_buckets' in prop_attrs['friction']:
if cur_num_buckets > 0:
cur_num_buckets *= prop_attrs['friction']['num_buckets']
else:
cur_num_buckets = prop_attrs['friction']['num_buckets']
total_num_buckets += cur_num_buckets
assert total_num_buckets <= 64000, 'Explicit material bucketing has been specified, but the provided total bucket count exceeds 64K: {} specified buckets'.format(
total_num_buckets)
shape_ct = 0
# Separate loop because we should not assume that each actor is present in each env
for env in envs:
for i in range(gym.get_actor_count(env)):
actor_handle = gym.get_actor_handle(env, i)
actor_name = gym.get_actor_name(env, actor_handle)
if actor_name in dr_params["actor_params"] and 'rigid_shape_properties' in dr_params["actor_params"][actor_name]:
shape_ct += gym.get_actor_rigid_shape_count(env, actor_handle)
assert shape_ct <= 64000 or total_num_buckets > 0, 'Explicit material bucketing is not used but the total number of shapes exceeds material limit. Please specify bucketing to limit material count.' | 10,378 | Python | 42.426778 | 201 | 0.64126 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/utils/utils.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# python
#import pwd
import getpass
import tempfile
import time
from collections import OrderedDict
from os.path import join
import numpy as np
import torch
import random
import os
def retry(times, exceptions):
"""
Retry Decorator https://stackoverflow.com/a/64030200/1645784
Retries the wrapped function/method `times` times if the exceptions listed
in ``exceptions`` are thrown
:param times: The number of times to repeat the wrapped function/method
:type times: Int
:param exceptions: Lists of exceptions that trigger a retry attempt
:type exceptions: Tuple of Exceptions
"""
def decorator(func):
def newfn(*args, **kwargs):
attempt = 0
while attempt < times:
try:
return func(*args, **kwargs)
except exceptions:
print(f'Exception thrown when attempting to run {func}, attempt {attempt} out of {times}')
time.sleep(min(2 ** attempt, 30))
attempt += 1
return func(*args, **kwargs)
return newfn
return decorator
def flatten_dict(d, prefix='', separator='.'):
res = dict()
for key, value in d.items():
if isinstance(value, (dict, OrderedDict)):
res.update(flatten_dict(value, prefix + key + separator, separator))
else:
res[prefix + key] = value
return res
def set_np_formatting():
""" formats numpy print """
np.set_printoptions(edgeitems=30, infstr='inf',
linewidth=4000, nanstr='nan', precision=2,
suppress=False, threshold=10000, formatter=None)
def set_seed(seed, torch_deterministic=False, rank=0):
""" set seed across modules """
if seed == -1 and torch_deterministic:
seed = 42 + rank
elif seed == -1:
seed = np.random.randint(0, 10000)
else:
seed = seed + rank
print("Setting seed: {}".format(seed))
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if torch_deterministic:
# refer to https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.use_deterministic_algorithms(True)
else:
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
return seed
def nested_dict_set_attr(d, key, val):
pre, _, post = key.partition('.')
if post:
nested_dict_set_attr(d[pre], post, val)
else:
d[key] = val
def nested_dict_get_attr(d, key):
pre, _, post = key.partition('.')
if post:
return nested_dict_get_attr(d[pre], post)
else:
return d[key]
def ensure_dir_exists(path):
if not os.path.exists(path):
os.makedirs(path)
return path
def safe_ensure_dir_exists(path):
"""Should be safer in multi-treaded environment."""
try:
return ensure_dir_exists(path)
except FileExistsError:
return path
def get_username():
uid = os.getuid()
try:
return getpass.getuser()
except KeyError:
# worst case scenario - let's just use uid
return str(uid)
def project_tmp_dir():
tmp_dir_name = f'ige_{get_username()}'
return safe_ensure_dir_exists(join(tempfile.gettempdir(), tmp_dir_name))
# EOF
| 5,149 | Python | 31.389937 | 110 | 0.666731 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/build/lib/isaacgymenvs/utils/rna_util.py |
# Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
class RandomNetworkAdversary(nn.Module):
def __init__(self, num_envs, in_dims, out_dims, softmax_bins, device):
super(RandomNetworkAdversary, self).__init__()
"""
Class to add random action to the action generated by the policy.
The output is binned to 32 bins per channel and we do softmax over
these bins to figure out the most likely joint angle.
Note: OpenAI et al. 2019 found out that if they used a continuous space
and a tanh non-linearity, actions would always be close to 0.
Section B.3 https://arxiv.org/abs/1910.07113
Q: Why do we need dropouts here?
A: If we were using a CPU-based simulator as in OpenAI et al. 2019, we
will use a different RNA network for different CPU. However,
this is not feasible for a GPU-based simulator as that would mean
creating N_envs RNA networks which will overwhelm the GPU-memory.
Therefore, dropout is a nice approximation of this by re-sampling
weights of the same neural network for each different env on the GPU.
"""
self.in_dims = in_dims
self.out_dims = out_dims
self.softmax_bins = softmax_bins
self.num_envs = num_envs
self.device = device
self.num_feats1 = 512
self.num_feats2 = 1024
# Sampling random probablities for dropout masks
dropout_probs = torch.rand((2, ))
# Setting up the RNA neural network here
# First layer
self.fc1 = nn.Linear(in_dims, self.num_feats1).to(self.device)
self.dropout_masks1 = torch.bernoulli(torch.ones((self.num_envs, \
self.num_feats1)), p=dropout_probs[0]).to(self.device)
self.fc1_1 = nn.Linear(self.num_feats1, self.num_feats1).to(self.device)
# Second layer
self.fc2 = nn.Linear(self.num_feats1, self.num_feats2).to(self.device)
self.dropout_masks2 = torch.bernoulli(torch.ones((self.num_envs, \
self.num_feats2)), p=dropout_probs[1]).to(self.device)
self.fc2_1 = nn.Linear(self.num_feats2, self.num_feats2).to(self.device)
# Last layer
self.fc3 = nn.Linear(self.num_feats2, out_dims*softmax_bins).to(self.device)
# This is needed to reset weights and dropout masks
self._refresh()
def _refresh(self):
self._init_weights()
self.eval()
self.refresh_dropout_masks()
def _init_weights(self):
print('initialising weights for random network')
nn.init.kaiming_uniform_(self.fc1.weight)
nn.init.kaiming_uniform_(self.fc1_1.weight)
nn.init.kaiming_uniform_(self.fc2.weight)
nn.init.kaiming_uniform_(self.fc2_1.weight)
nn.init.kaiming_uniform_(self.fc3.weight)
return
def refresh_dropout_masks(self):
dropout_probs = torch.rand((2, ))
self.dropout_masks1 = torch.bernoulli(torch.ones((self.num_envs, self.num_feats1)), \
p=dropout_probs[0]).to(self.dropout_masks1.device)
self.dropout_masks2 = torch.bernoulli(torch.ones((self.num_envs, self.num_feats2)), \
p=dropout_probs[1]).to(self.dropout_masks2.device)
return
def forward(self, x):
x = self.fc1(x)
x = F.relu(x)
x = self.fc1_1(x)
x = self.dropout_masks1 * x
x = self.fc2(x)
x = F.relu(x)
x = self.fc2_1(x)
x = self.dropout_masks2 * x
x = self.fc3(x)
x = x.view(-1, self.out_dims, self.softmax_bins)
output = F.softmax(x, dim=-1)
# We have discretised the joint angles into bins
# Now we pick up the bin for each joint angle
# corresponding to the highest softmax value / prob.
return output
if __name__ == "__main__":
num_envs = 1024
RNA = RandomNetworkAdversary(num_envs=num_envs, in_dims=16, out_dims=16, softmax_bins=32, device='cuda')
x = torch.tensor(torch.randn(num_envs, 16).to(RNA.device))
y = RNA(x)
import ipdb; ipdb.set_trace()
| 5,780 | Python | 34.25 | 108 | 0.659689 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/__init__.py | import hydra
from hydra import compose, initialize
from hydra.core.hydra_config import HydraConfig
from omegaconf import DictConfig, OmegaConf
from isaacgymenvs.utils.reformat import omegaconf_to_dict
OmegaConf.register_new_resolver('eq', lambda x, y: x.lower()==y.lower())
OmegaConf.register_new_resolver('contains', lambda x, y: x.lower() in y.lower())
OmegaConf.register_new_resolver('if', lambda pred, a, b: a if pred else b)
OmegaConf.register_new_resolver('resolve_default', lambda default, arg: default if arg=='' else arg)
def make(
seed: int,
task: str,
num_envs: int,
sim_device: str,
rl_device: str,
graphics_device_id: int = -1,
headless: bool = False,
multi_gpu: bool = False,
virtual_screen_capture: bool = False,
force_render: bool = True,
cfg: DictConfig = None
):
from isaacgymenvs.utils.rlgames_utils import get_rlgames_env_creator
# create hydra config if no config passed in
if cfg is None:
# reset current hydra config if already parsed (but not passed in here)
if HydraConfig.initialized():
task = HydraConfig.get().runtime.choices['task']
hydra.core.global_hydra.GlobalHydra.instance().clear()
with initialize(config_path="./cfg"):
cfg = compose(config_name="config", overrides=[f"task={task}"])
cfg_dict = omegaconf_to_dict(cfg.task)
cfg_dict['env']['numEnvs'] = num_envs
# reuse existing config
else:
cfg_dict = omegaconf_to_dict(cfg.task)
create_rlgpu_env = get_rlgames_env_creator(
seed=seed,
task_config=cfg_dict,
task_name=cfg_dict["name"],
sim_device=sim_device,
rl_device=rl_device,
graphics_device_id=graphics_device_id,
headless=headless,
multi_gpu=multi_gpu,
virtual_screen_capture=virtual_screen_capture,
force_render=force_render,
)
return create_rlgpu_env()
| 1,953 | Python | 33.892857 | 100 | 0.656938 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/test_onnx.py | import numpy as np
import onnx
import onnxruntime as ort
onnx_model = onnx.load("pendulum.onnx")
# Check that the model is well formed
onnx.checker.check_model(onnx_model)
ort_model = ort.InferenceSession("pendulum.onnx")
outputs = ort_model.run(
None,
{"obs": np.zeros((1, 2)).astype(np.float32)},
)
print(outputs) | 327 | Python | 19.499999 | 49 | 0.718654 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/train.py | # train.py
# Script to train policies in Isaac Gym
#
# Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import hydra
from omegaconf import DictConfig, OmegaConf
from omegaconf import DictConfig, OmegaConf
def preprocess_train_config(cfg, config_dict):
"""
Adding common configuration parameters to the rl_games train config.
An alternative to this is inferring them in task-specific .yaml files, but that requires repeating the same
variable interpolations in each config.
"""
train_cfg = config_dict['params']['config']
train_cfg['device'] = cfg.rl_device
train_cfg['population_based_training'] = cfg.pbt.enabled
train_cfg['pbt_idx'] = cfg.pbt.policy_idx if cfg.pbt.enabled else None
train_cfg['full_experiment_name'] = cfg.get('full_experiment_name')
print(f'Using rl_device: {cfg.rl_device}')
print(f'Using sim_device: {cfg.sim_device}')
print(train_cfg)
try:
model_size_multiplier = config_dict['params']['network']['mlp']['model_size_multiplier']
if model_size_multiplier != 1:
units = config_dict['params']['network']['mlp']['units']
for i, u in enumerate(units):
units[i] = u * model_size_multiplier
print(f'Modified MLP units by x{model_size_multiplier} to {config_dict["params"]["network"]["mlp"]["units"]}')
except KeyError:
pass
return config_dict
@hydra.main(version_base="1.1", config_name="config", config_path="./cfg")
def launch_rlg_hydra(cfg: DictConfig):
import logging
import os
from datetime import datetime
# noinspection PyUnresolvedReferences
import isaacgym
from isaacgymenvs.pbt.pbt import PbtAlgoObserver, initial_pbt_check
from isaacgymenvs.utils.rlgames_utils import multi_gpu_get_rank
from hydra.utils import to_absolute_path
from isaacgymenvs.tasks import isaacgym_task_map
import gym
from isaacgymenvs.utils.reformat import omegaconf_to_dict, print_dict
from isaacgymenvs.utils.utils import set_np_formatting, set_seed
if cfg.pbt.enabled:
initial_pbt_check(cfg)
from isaacgymenvs.utils.rlgames_utils import RLGPUEnv, RLGPUAlgoObserver, MultiObserver, ComplexObsRLGPUEnv
from isaacgymenvs.utils.wandb_utils import WandbAlgoObserver
from rl_games.common import env_configurations, vecenv
from rl_games.torch_runner import Runner
from rl_games.algos_torch import model_builder
from isaacgymenvs.learning import amp_continuous
from isaacgymenvs.learning import amp_players
from isaacgymenvs.learning import amp_models
from isaacgymenvs.learning import amp_network_builder
import isaacgymenvs
time_str = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
run_name = f"{cfg.wandb_name}_{time_str}"
# run_name = f"{cfg.wandb_name}"
# ensure checkpoints can be specified as relative paths
if cfg.checkpoint:
cfg.checkpoint = to_absolute_path(cfg.checkpoint)
cfg_dict = omegaconf_to_dict(cfg)
print_dict(cfg_dict)
# set numpy formatting for printing only
set_np_formatting()
# global rank of the GPU
global_rank = int(os.getenv("RANK", "0"))
# sets seed. if seed is -1 will pick a random one
cfg.seed = set_seed(cfg.seed, torch_deterministic=cfg.torch_deterministic, rank=global_rank)
def create_isaacgym_env(**kwargs):
envs = isaacgymenvs.make(
cfg.seed,
cfg.task_name,
cfg.task.env.numEnvs,
cfg.sim_device,
cfg.rl_device,
cfg.graphics_device_id,
cfg.headless,
cfg.multi_gpu,
cfg.capture_video,
cfg.force_render,
cfg,
**kwargs,
)
if cfg.capture_video:
envs.is_vector_env = True
envs = gym.wrappers.RecordVideo(
envs,
f"videos/{run_name}",
step_trigger=lambda step: step % cfg.capture_video_freq == 0,
video_length=cfg.capture_video_len,
)
return envs
env_configurations.register('rlgpu', {
'vecenv_type': 'RLGPU',
'env_creator': lambda **kwargs: create_isaacgym_env(**kwargs),
})
ige_env_cls = isaacgym_task_map[cfg.task_name]
dict_cls = ige_env_cls.dict_obs_cls if hasattr(ige_env_cls, 'dict_obs_cls') and ige_env_cls.dict_obs_cls else False
if dict_cls:
obs_spec = {}
actor_net_cfg = cfg.train.params.network
obs_spec['obs'] = {'names': list(actor_net_cfg.inputs.keys()), 'concat': not actor_net_cfg.name == "complex_net", 'space_name': 'observation_space'}
if "central_value_config" in cfg.train.params.config:
critic_net_cfg = cfg.train.params.config.central_value_config.network
obs_spec['states'] = {'names': list(critic_net_cfg.inputs.keys()), 'concat': not critic_net_cfg.name == "complex_net", 'space_name': 'state_space'}
vecenv.register('RLGPU', lambda config_name, num_actors, **kwargs: ComplexObsRLGPUEnv(config_name, num_actors, obs_spec, **kwargs))
else:
vecenv.register('RLGPU', lambda config_name, num_actors, **kwargs: RLGPUEnv(config_name, num_actors, **kwargs))
rlg_config_dict = omegaconf_to_dict(cfg.train)
rlg_config_dict = preprocess_train_config(cfg, rlg_config_dict)
observers = [RLGPUAlgoObserver()]
if cfg.pbt.enabled:
pbt_observer = PbtAlgoObserver(cfg)
observers.append(pbt_observer)
if cfg.wandb_activate:
cfg.seed += global_rank
if global_rank == 0:
# initialize wandb only once per multi-gpu run
wandb_observer = WandbAlgoObserver(cfg)
observers.append(wandb_observer)
# register new AMP network builder and agent
def build_runner(algo_observer):
runner = Runner(algo_observer)
runner.algo_factory.register_builder('amp_continuous', lambda **kwargs : amp_continuous.AMPAgent(**kwargs))
runner.player_factory.register_builder('amp_continuous', lambda **kwargs : amp_players.AMPPlayerContinuous(**kwargs))
model_builder.register_model('continuous_amp', lambda network, **kwargs : amp_models.ModelAMPContinuous(network))
model_builder.register_network('amp', lambda **kwargs : amp_network_builder.AMPBuilder())
return runner
# convert CLI arguments into dictionary
# create runner and set the settings
runner = build_runner(MultiObserver(observers))
runner.load(rlg_config_dict)
runner.reset()
# dump config dict
if not cfg.test:
experiment_dir = os.path.join('runs', cfg.train.params.config.name +
'_{date:%d-%H-%M-%S}'.format(date=datetime.now()))
os.makedirs(experiment_dir, exist_ok=True)
with open(os.path.join(experiment_dir, 'config.yaml'), 'w') as f:
f.write(OmegaConf.to_yaml(cfg))
runner.run({
'train': not cfg.test,
'play': cfg.test,
'checkpoint': cfg.checkpoint,
'sigma': cfg.sigma if cfg.sigma != '' else None
})
if __name__ == "__main__":
launch_rlg_hydra()
| 8,657 | Python | 37.48 | 159 | 0.673212 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/learning/amp_models.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import torch.nn as nn
from rl_games.algos_torch.models import ModelA2CContinuousLogStd
class ModelAMPContinuous(ModelA2CContinuousLogStd):
def __init__(self, network):
super().__init__(network)
return
def build(self, config):
net = self.network_builder.build('amp', **config)
for name, _ in net.named_parameters():
print(name)
obs_shape = config['input_shape']
normalize_value = config.get('normalize_value', False)
normalize_input = config.get('normalize_input', False)
value_size = config.get('value_size', 1)
return self.Network(net, obs_shape=obs_shape,
normalize_value=normalize_value, normalize_input=normalize_input, value_size=value_size)
class Network(ModelA2CContinuousLogStd.Network):
def __init__(self, a2c_network, **kwargs):
super().__init__(a2c_network, **kwargs)
return
def forward(self, input_dict):
is_train = input_dict.get('is_train', True)
result = super().forward(input_dict)
if (is_train):
amp_obs = input_dict['amp_obs']
disc_agent_logit = self.a2c_network.eval_disc(amp_obs)
result["disc_agent_logit"] = disc_agent_logit
amp_obs_replay = input_dict['amp_obs_replay']
disc_agent_replay_logit = self.a2c_network.eval_disc(amp_obs_replay)
result["disc_agent_replay_logit"] = disc_agent_replay_logit
amp_demo_obs = input_dict['amp_obs_demo']
disc_demo_logit = self.a2c_network.eval_disc(amp_demo_obs)
result["disc_demo_logit"] = disc_demo_logit
return result | 3,290 | Python | 43.472972 | 100 | 0.685714 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/learning/hrl_models.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import torch.nn as nn
from rl_games.algos_torch.models import ModelA2CContinuousLogStd
class ModelHRLContinuous(ModelA2CContinuousLogStd):
def __init__(self, network):
super().__init__(network)
return
def build(self, config):
net = self.network_builder.build('amp', **config)
for name, _ in net.named_parameters():
print(name)
return ModelHRLContinuous.Network(net)
class Network(ModelA2CContinuousLogStd.Network):
def __init__(self, a2c_network):
super().__init__(a2c_network)
return | 2,142 | Python | 45.586956 | 80 | 0.744631 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/learning/amp_datasets.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import torch
from rl_games.common import datasets
class AMPDataset(datasets.PPODataset):
def __init__(self, batch_size, minibatch_size, is_discrete, is_rnn, device, seq_len):
super().__init__(batch_size, minibatch_size, is_discrete, is_rnn, device, seq_len)
self._idx_buf = torch.randperm(batch_size)
return
def update_mu_sigma(self, mu, sigma):
raise NotImplementedError()
return
def _get_item(self, idx):
start = idx * self.minibatch_size
end = (idx + 1) * self.minibatch_size
sample_idx = self._idx_buf[start:end]
input_dict = {}
for k,v in self.values_dict.items():
if k not in self.special_names and v is not None:
input_dict[k] = v[sample_idx]
if (end >= self.batch_size):
self._shuffle_idx_buf()
return input_dict
def _shuffle_idx_buf(self):
self._idx_buf[:] = torch.randperm(self.batch_size)
return | 2,564 | Python | 41.749999 | 90 | 0.704758 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/learning/replay_buffer.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import torch
class ReplayBuffer():
def __init__(self, buffer_size, device):
self._head = 0
self._total_count = 0
self._buffer_size = buffer_size
self._device = device
self._data_buf = None
self._sample_idx = torch.randperm(buffer_size)
self._sample_head = 0
return
def reset(self):
self._head = 0
self._total_count = 0
self._reset_sample_idx()
return
def get_buffer_size(self):
return self._buffer_size
def get_total_count(self):
return self._total_count
def store(self, data_dict):
if (self._data_buf is None):
self._init_data_buf(data_dict)
n = next(iter(data_dict.values())).shape[0]
buffer_size = self.get_buffer_size()
assert(n < buffer_size)
for key, curr_buf in self._data_buf.items():
curr_n = data_dict[key].shape[0]
assert(n == curr_n)
store_n = min(curr_n, buffer_size - self._head)
curr_buf[self._head:(self._head + store_n)] = data_dict[key][:store_n]
remainder = n - store_n
if (remainder > 0):
curr_buf[0:remainder] = data_dict[key][store_n:]
self._head = (self._head + n) % buffer_size
self._total_count += n
return
def sample(self, n):
total_count = self.get_total_count()
buffer_size = self.get_buffer_size()
idx = torch.arange(self._sample_head, self._sample_head + n)
idx = idx % buffer_size
rand_idx = self._sample_idx[idx]
if (total_count < buffer_size):
rand_idx = rand_idx % self._head
samples = dict()
for k, v in self._data_buf.items():
samples[k] = v[rand_idx]
self._sample_head += n
if (self._sample_head >= buffer_size):
self._reset_sample_idx()
return samples
def _reset_sample_idx(self):
buffer_size = self.get_buffer_size()
self._sample_idx[:] = torch.randperm(buffer_size)
self._sample_head = 0
return
def _init_data_buf(self, data_dict):
buffer_size = self.get_buffer_size()
self._data_buf = dict()
for k, v in data_dict.items():
v_shape = v.shape[1:]
self._data_buf[k] = torch.zeros((buffer_size,) + v_shape, device=self._device)
return | 3,986 | Python | 33.973684 | 90 | 0.632965 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/learning/amp_network_builder.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch import layers
from rl_games.algos_torch import network_builder
import torch
import torch.nn as nn
import numpy as np
DISC_LOGIT_INIT_SCALE = 1.0
class AMPBuilder(network_builder.A2CBuilder):
def __init__(self, **kwargs):
super().__init__(**kwargs)
return
class Network(network_builder.A2CBuilder.Network):
def __init__(self, params, **kwargs):
super().__init__(params, **kwargs)
if self.is_continuous:
if (not self.space_config['learn_sigma']):
actions_num = kwargs.get('actions_num')
sigma_init = self.init_factory.create(**self.space_config['sigma_init'])
self.sigma = nn.Parameter(torch.zeros(actions_num, requires_grad=False, dtype=torch.float32), requires_grad=False)
sigma_init(self.sigma)
amp_input_shape = kwargs.get('amp_input_shape')
self._build_disc(amp_input_shape)
return
def load(self, params):
super().load(params)
self._disc_units = params['disc']['units']
self._disc_activation = params['disc']['activation']
self._disc_initializer = params['disc']['initializer']
return
def eval_critic(self, obs):
c_out = self.critic_cnn(obs)
c_out = c_out.contiguous().view(c_out.size(0), -1)
c_out = self.critic_mlp(c_out)
value = self.value_act(self.value(c_out))
return value
def eval_disc(self, amp_obs):
disc_mlp_out = self._disc_mlp(amp_obs)
disc_logits = self._disc_logits(disc_mlp_out)
return disc_logits
def get_disc_logit_weights(self):
return torch.flatten(self._disc_logits.weight)
def get_disc_weights(self):
weights = []
for m in self._disc_mlp.modules():
if isinstance(m, nn.Linear):
weights.append(torch.flatten(m.weight))
weights.append(torch.flatten(self._disc_logits.weight))
return weights
def _build_disc(self, input_shape):
self._disc_mlp = nn.Sequential()
mlp_args = {
'input_size' : input_shape[0],
'units' : self._disc_units,
'activation' : self._disc_activation,
'dense_func' : torch.nn.Linear
}
self._disc_mlp = self._build_mlp(**mlp_args)
mlp_out_size = self._disc_units[-1]
self._disc_logits = torch.nn.Linear(mlp_out_size, 1)
mlp_init = self.init_factory.create(**self._disc_initializer)
for m in self._disc_mlp.modules():
if isinstance(m, nn.Linear):
mlp_init(m.weight)
if getattr(m, "bias", None) is not None:
torch.nn.init.zeros_(m.bias)
torch.nn.init.uniform_(self._disc_logits.weight, -DISC_LOGIT_INIT_SCALE, DISC_LOGIT_INIT_SCALE)
torch.nn.init.zeros_(self._disc_logits.bias)
return
def build(self, name, **kwargs):
net = AMPBuilder.Network(self.params, **kwargs)
return net | 4,898 | Python | 39.487603 | 134 | 0.620457 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/learning/hrl_continuous.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import copy
from datetime import datetime
from gym import spaces
import numpy as np
import os
import time
import yaml
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch import central_value
from rl_games.algos_torch.running_mean_std import RunningMeanStd
from rl_games.common import a2c_common
from rl_games.common import datasets
from rl_games.common import schedulers
from rl_games.common import vecenv
import torch
from torch import optim
import isaacgymenvs.learning.common_agent as common_agent
import isaacgymenvs.learning.gen_amp as gen_amp
import isaacgymenvs.learning.gen_amp_models as gen_amp_models
import isaacgymenvs.learning.gen_amp_network_builder as gen_amp_network_builder
from tensorboardX import SummaryWriter
class HRLAgent(common_agent.CommonAgent):
def __init__(self, base_name, config):
with open(os.path.join(os.getcwd(), config['llc_config']), 'r') as f:
llc_config = yaml.load(f, Loader=yaml.SafeLoader)
llc_config_params = llc_config['params']
self._latent_dim = llc_config_params['config']['latent_dim']
super().__init__(base_name, config)
self._task_size = self.vec_env.env.get_task_obs_size()
self._llc_steps = config['llc_steps']
llc_checkpoint = config['llc_checkpoint']
assert(llc_checkpoint != "")
self._build_llc(llc_config_params, llc_checkpoint)
return
def env_step(self, actions):
actions = self.preprocess_actions(actions)
obs = self.obs['obs']
rewards = 0.0
done_count = 0.0
for t in range(self._llc_steps):
llc_actions = self._compute_llc_action(obs, actions)
obs, curr_rewards, curr_dones, infos = self.vec_env.step(llc_actions)
rewards += curr_rewards
done_count += curr_dones
rewards /= self._llc_steps
dones = torch.zeros_like(done_count)
dones[done_count > 0] = 1.0
if self.is_tensor_obses:
if self.value_size == 1:
rewards = rewards.unsqueeze(1)
return self.obs_to_tensors(obs), rewards.to(self.ppo_device), dones.to(self.ppo_device), infos
else:
if self.value_size == 1:
rewards = np.expand_dims(rewards, axis=1)
return self.obs_to_tensors(obs), torch.from_numpy(rewards).to(self.ppo_device).float(), torch.from_numpy(dones).to(self.ppo_device), infos
def cast_obs(self, obs):
obs = super().cast_obs(obs)
self._llc_agent.is_tensor_obses = self.is_tensor_obses
return obs
def preprocess_actions(self, actions):
clamped_actions = torch.clamp(actions, -1.0, 1.0)
if not self.is_tensor_obses:
clamped_actions = clamped_actions.cpu().numpy()
return clamped_actions
def _setup_action_space(self):
super()._setup_action_space()
self.actions_num = self._latent_dim
return
def _build_llc(self, config_params, checkpoint_file):
network_params = config_params['network']
network_builder = gen_amp_network_builder.GenAMPBuilder()
network_builder.load(network_params)
network = gen_amp_models.ModelGenAMPContinuous(network_builder)
llc_agent_config = self._build_llc_agent_config(config_params, network)
self._llc_agent = gen_amp.GenAMPAgent('llc', llc_agent_config)
self._llc_agent.restore(checkpoint_file)
print("Loaded LLC checkpoint from {:s}".format(checkpoint_file))
self._llc_agent.set_eval()
return
def _build_llc_agent_config(self, config_params, network):
llc_env_info = copy.deepcopy(self.env_info)
obs_space = llc_env_info['observation_space']
obs_size = obs_space.shape[0]
obs_size -= self._task_size
llc_env_info['observation_space'] = spaces.Box(obs_space.low[:obs_size], obs_space.high[:obs_size])
config = config_params['config']
config['network'] = network
config['num_actors'] = self.num_actors
config['features'] = {'observer' : self.algo_observer}
config['env_info'] = llc_env_info
return config
def _compute_llc_action(self, obs, actions):
llc_obs = self._extract_llc_obs(obs)
processed_obs = self._llc_agent._preproc_obs(llc_obs)
z = torch.nn.functional.normalize(actions, dim=-1)
mu, _ = self._llc_agent.model.a2c_network.eval_actor(obs=processed_obs, amp_latents=z)
llc_action = mu
llc_action = self._llc_agent.preprocess_actions(llc_action)
return llc_action
def _extract_llc_obs(self, obs):
obs_size = obs.shape[-1]
llc_obs = obs[..., :obs_size - self._task_size]
return llc_obs
| 6,339 | Python | 38.625 | 150 | 0.675974 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/learning/amp_continuous.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from rl_games.algos_torch.running_mean_std import RunningMeanStd
from rl_games.algos_torch import torch_ext
from rl_games.common import a2c_common
from rl_games.common import schedulers
from rl_games.common import vecenv
from isaacgymenvs.utils.torch_jit_utils import to_torch
import time
from datetime import datetime
import numpy as np
from torch import optim
import torch
from torch import nn
import isaacgymenvs.learning.replay_buffer as replay_buffer
import isaacgymenvs.learning.common_agent as common_agent
from tensorboardX import SummaryWriter
class AMPAgent(common_agent.CommonAgent):
def __init__(self, base_name, params):
super().__init__(base_name, params)
if self.normalize_value:
self.value_mean_std = self.central_value_net.model.value_mean_std if self.has_central_value else self.model.value_mean_std
if self._normalize_amp_input:
self._amp_input_mean_std = RunningMeanStd(self._amp_observation_space.shape).to(self.ppo_device)
return
def init_tensors(self):
super().init_tensors()
self._build_amp_buffers()
return
def set_eval(self):
super().set_eval()
if self._normalize_amp_input:
self._amp_input_mean_std.eval()
return
def set_train(self):
super().set_train()
if self._normalize_amp_input:
self._amp_input_mean_std.train()
return
def get_stats_weights(self):
state = super().get_stats_weights()
if self._normalize_amp_input:
state['amp_input_mean_std'] = self._amp_input_mean_std.state_dict()
return state
def set_stats_weights(self, weights):
super().set_stats_weights(weights)
if self._normalize_amp_input:
self._amp_input_mean_std.load_state_dict(weights['amp_input_mean_std'])
return
def play_steps(self):
self.set_eval()
epinfos = []
update_list = self.update_list
for n in range(self.horizon_length):
self.obs, done_env_ids = self._env_reset_done()
self.experience_buffer.update_data('obses', n, self.obs['obs'])
if self.use_action_masks:
masks = self.vec_env.get_action_masks()
res_dict = self.get_masked_action_values(self.obs, masks)
else:
res_dict = self.get_action_values(self.obs)
for k in update_list:
self.experience_buffer.update_data(k, n, res_dict[k])
if self.has_central_value:
self.experience_buffer.update_data('states', n, self.obs['states'])
self.obs, rewards, self.dones, infos = self.env_step(res_dict['actions'])
shaped_rewards = self.rewards_shaper(rewards)
self.experience_buffer.update_data('rewards', n, shaped_rewards)
self.experience_buffer.update_data('next_obses', n, self.obs['obs'])
self.experience_buffer.update_data('dones', n, self.dones)
self.experience_buffer.update_data('amp_obs', n, infos['amp_obs'])
terminated = infos['terminate'].float()
terminated = terminated.unsqueeze(-1)
next_vals = self._eval_critic(self.obs)
next_vals *= (1.0 - terminated)
self.experience_buffer.update_data('next_values', n, next_vals)
self.current_rewards += rewards
self.current_lengths += 1
all_done_indices = self.dones.nonzero(as_tuple=False)
done_indices = all_done_indices[::self.num_agents]
self.game_rewards.update(self.current_rewards[done_indices])
self.game_lengths.update(self.current_lengths[done_indices])
self.algo_observer.process_infos(infos, done_indices)
not_dones = 1.0 - self.dones.float()
self.current_rewards = self.current_rewards * not_dones.unsqueeze(1)
self.current_lengths = self.current_lengths * not_dones
if (self.vec_env.env.viewer and (n == (self.horizon_length - 1))):
self._amp_debug(infos)
mb_fdones = self.experience_buffer.tensor_dict['dones'].float()
mb_values = self.experience_buffer.tensor_dict['values']
mb_next_values = self.experience_buffer.tensor_dict['next_values']
mb_rewards = self.experience_buffer.tensor_dict['rewards']
mb_amp_obs = self.experience_buffer.tensor_dict['amp_obs']
amp_rewards = self._calc_amp_rewards(mb_amp_obs)
mb_rewards = self._combine_rewards(mb_rewards, amp_rewards)
mb_advs = self.discount_values(mb_fdones, mb_values, mb_rewards, mb_next_values)
mb_returns = mb_advs + mb_values
batch_dict = self.experience_buffer.get_transformed_list(a2c_common.swap_and_flatten01, self.tensor_list)
batch_dict['returns'] = a2c_common.swap_and_flatten01(mb_returns)
batch_dict['played_frames'] = self.batch_size
for k, v in amp_rewards.items():
batch_dict[k] = a2c_common.swap_and_flatten01(v)
return batch_dict
def prepare_dataset(self, batch_dict):
super().prepare_dataset(batch_dict)
self.dataset.values_dict['amp_obs'] = batch_dict['amp_obs']
self.dataset.values_dict['amp_obs_demo'] = batch_dict['amp_obs_demo']
self.dataset.values_dict['amp_obs_replay'] = batch_dict['amp_obs_replay']
return
def train_epoch(self):
play_time_start = time.time()
with torch.no_grad():
if self.is_rnn:
batch_dict = self.play_steps_rnn()
else:
batch_dict = self.play_steps()
play_time_end = time.time()
update_time_start = time.time()
rnn_masks = batch_dict.get('rnn_masks', None)
self._update_amp_demos()
num_obs_samples = batch_dict['amp_obs'].shape[0]
amp_obs_demo = self._amp_obs_demo_buffer.sample(num_obs_samples)['amp_obs']
batch_dict['amp_obs_demo'] = amp_obs_demo
if (self._amp_replay_buffer.get_total_count() == 0):
batch_dict['amp_obs_replay'] = batch_dict['amp_obs']
else:
batch_dict['amp_obs_replay'] = self._amp_replay_buffer.sample(num_obs_samples)['amp_obs']
self.set_train()
self.curr_frames = batch_dict.pop('played_frames')
self.prepare_dataset(batch_dict)
self.algo_observer.after_steps()
if self.has_central_value:
self.train_central_value()
train_info = None
if self.is_rnn:
frames_mask_ratio = rnn_masks.sum().item() / (rnn_masks.nelement())
print(frames_mask_ratio)
for _ in range(0, self.mini_epochs_num):
ep_kls = []
for i in range(len(self.dataset)):
curr_train_info = self.train_actor_critic(self.dataset[i])
if self.schedule_type == 'legacy':
self.last_lr, self.entropy_coef = self.scheduler.update(self.last_lr, self.entropy_coef, self.epoch_num, 0, curr_train_info['kl'].item())
self.update_lr(self.last_lr)
if (train_info is None):
train_info = dict()
for k, v in curr_train_info.items():
train_info[k] = [v]
else:
for k, v in curr_train_info.items():
train_info[k].append(v)
av_kls = torch_ext.mean_list(train_info['kl'])
if self.schedule_type == 'standard':
self.last_lr, self.entropy_coef = self.scheduler.update(self.last_lr, self.entropy_coef, self.epoch_num, 0, av_kls.item())
self.update_lr(self.last_lr)
if self.schedule_type == 'standard_epoch':
self.last_lr, self.entropy_coef = self.scheduler.update(self.last_lr, self.entropy_coef, self.epoch_num, 0, av_kls.item())
self.update_lr(self.last_lr)
update_time_end = time.time()
play_time = play_time_end - play_time_start
update_time = update_time_end - update_time_start
total_time = update_time_end - play_time_start
self._store_replay_amp_obs(batch_dict['amp_obs'])
train_info['play_time'] = play_time
train_info['update_time'] = update_time
train_info['total_time'] = total_time
self._record_train_batch_info(batch_dict, train_info)
return train_info
def calc_gradients(self, input_dict):
self.set_train()
value_preds_batch = input_dict['old_values']
old_action_log_probs_batch = input_dict['old_logp_actions']
advantage = input_dict['advantages']
old_mu_batch = input_dict['mu']
old_sigma_batch = input_dict['sigma']
return_batch = input_dict['returns']
actions_batch = input_dict['actions']
obs_batch = input_dict['obs']
obs_batch = self._preproc_obs(obs_batch)
amp_obs = input_dict['amp_obs'][0:self._amp_minibatch_size]
amp_obs = self._preproc_amp_obs(amp_obs)
amp_obs_replay = input_dict['amp_obs_replay'][0:self._amp_minibatch_size]
amp_obs_replay = self._preproc_amp_obs(amp_obs_replay)
amp_obs_demo = input_dict['amp_obs_demo'][0:self._amp_minibatch_size]
amp_obs_demo = self._preproc_amp_obs(amp_obs_demo)
amp_obs_demo.requires_grad_(True)
lr = self.last_lr
kl = 1.0
lr_mul = 1.0
curr_e_clip = lr_mul * self.e_clip
batch_dict = {
'is_train': True,
'prev_actions': actions_batch,
'obs' : obs_batch,
'amp_obs' : amp_obs,
'amp_obs_replay' : amp_obs_replay,
'amp_obs_demo' : amp_obs_demo
}
rnn_masks = None
if self.is_rnn:
rnn_masks = input_dict['rnn_masks']
batch_dict['rnn_states'] = input_dict['rnn_states']
batch_dict['seq_length'] = self.seq_len
with torch.cuda.amp.autocast(enabled=self.mixed_precision):
res_dict = self.model(batch_dict)
action_log_probs = res_dict['prev_neglogp']
values = res_dict['values']
entropy = res_dict['entropy']
mu = res_dict['mus']
sigma = res_dict['sigmas']
disc_agent_logit = res_dict['disc_agent_logit']
disc_agent_replay_logit = res_dict['disc_agent_replay_logit']
disc_demo_logit = res_dict['disc_demo_logit']
a_info = self._actor_loss(old_action_log_probs_batch, action_log_probs, advantage, curr_e_clip)
a_loss = a_info['actor_loss']
c_info = self._critic_loss(value_preds_batch, values, curr_e_clip, return_batch, self.clip_value)
c_loss = c_info['critic_loss']
b_loss = self.bound_loss(mu)
losses, sum_mask = torch_ext.apply_masks([a_loss.unsqueeze(1), c_loss, entropy.unsqueeze(1), b_loss.unsqueeze(1)], rnn_masks)
a_loss, c_loss, entropy, b_loss = losses[0], losses[1], losses[2], losses[3]
disc_agent_cat_logit = torch.cat([disc_agent_logit, disc_agent_replay_logit], dim=0)
disc_info = self._disc_loss(disc_agent_cat_logit, disc_demo_logit, amp_obs_demo)
disc_loss = disc_info['disc_loss']
loss = a_loss + self.critic_coef * c_loss - self.entropy_coef * entropy + self.bounds_loss_coef * b_loss \
+ self._disc_coef * disc_loss
if self.multi_gpu:
self.optimizer.zero_grad()
else:
for param in self.model.parameters():
param.grad = None
self.scaler.scale(loss).backward()
#TODO: Refactor this ugliest code of the year
if self.truncate_grads:
if self.multi_gpu:
self.optimizer.synchronize()
self.scaler.unscale_(self.optimizer)
nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_norm)
with self.optimizer.skip_synchronize():
self.scaler.step(self.optimizer)
self.scaler.update()
else:
self.scaler.unscale_(self.optimizer)
nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_norm)
self.scaler.step(self.optimizer)
self.scaler.update()
else:
self.scaler.step(self.optimizer)
self.scaler.update()
with torch.no_grad():
reduce_kl = not self.is_rnn
kl_dist = torch_ext.policy_kl(mu.detach(), sigma.detach(), old_mu_batch, old_sigma_batch, reduce_kl)
if self.is_rnn:
kl_dist = (kl_dist * rnn_masks).sum() / rnn_masks.numel() #/ sum_mask
self.train_result = {
'entropy': entropy,
'kl': kl_dist,
'last_lr': self.last_lr,
'lr_mul': lr_mul,
'b_loss': b_loss
}
self.train_result.update(a_info)
self.train_result.update(c_info)
self.train_result.update(disc_info)
return
def _load_config_params(self, config):
super()._load_config_params(config)
self._task_reward_w = config['task_reward_w']
self._disc_reward_w = config['disc_reward_w']
self._amp_observation_space = self.env_info['amp_observation_space']
self._amp_batch_size = int(config['amp_batch_size'])
self._amp_minibatch_size = int(config['amp_minibatch_size'])
assert(self._amp_minibatch_size <= self.minibatch_size)
self._disc_coef = config['disc_coef']
self._disc_logit_reg = config['disc_logit_reg']
self._disc_grad_penalty = config['disc_grad_penalty']
self._disc_weight_decay = config['disc_weight_decay']
self._disc_reward_scale = config['disc_reward_scale']
self._normalize_amp_input = config.get('normalize_amp_input', True)
return
def _build_net_config(self):
config = super()._build_net_config()
config['amp_input_shape'] = self._amp_observation_space.shape
return config
def _init_train(self):
super()._init_train()
self._init_amp_demo_buf()
return
def _disc_loss(self, disc_agent_logit, disc_demo_logit, obs_demo):
# prediction loss
disc_loss_agent = self._disc_loss_neg(disc_agent_logit)
disc_loss_demo = self._disc_loss_pos(disc_demo_logit)
disc_loss = 0.5 * (disc_loss_agent + disc_loss_demo)
# logit reg
logit_weights = self.model.a2c_network.get_disc_logit_weights()
disc_logit_loss = torch.sum(torch.square(logit_weights))
disc_loss += self._disc_logit_reg * disc_logit_loss
# grad penalty
disc_demo_grad = torch.autograd.grad(disc_demo_logit, obs_demo, grad_outputs=torch.ones_like(disc_demo_logit),
create_graph=True, retain_graph=True, only_inputs=True)
disc_demo_grad = disc_demo_grad[0]
disc_demo_grad = torch.sum(torch.square(disc_demo_grad), dim=-1)
disc_grad_penalty = torch.mean(disc_demo_grad)
disc_loss += self._disc_grad_penalty * disc_grad_penalty
# weight decay
if (self._disc_weight_decay != 0):
disc_weights = self.model.a2c_network.get_disc_weights()
disc_weights = torch.cat(disc_weights, dim=-1)
disc_weight_decay = torch.sum(torch.square(disc_weights))
disc_loss += self._disc_weight_decay * disc_weight_decay
disc_agent_acc, disc_demo_acc = self._compute_disc_acc(disc_agent_logit, disc_demo_logit)
disc_info = {
'disc_loss': disc_loss,
'disc_grad_penalty': disc_grad_penalty,
'disc_logit_loss': disc_logit_loss,
'disc_agent_acc': disc_agent_acc,
'disc_demo_acc': disc_demo_acc,
'disc_agent_logit': disc_agent_logit,
'disc_demo_logit': disc_demo_logit
}
return disc_info
def _disc_loss_neg(self, disc_logits):
bce = torch.nn.BCEWithLogitsLoss()
loss = bce(disc_logits, torch.zeros_like(disc_logits))
return loss
def _disc_loss_pos(self, disc_logits):
bce = torch.nn.BCEWithLogitsLoss()
loss = bce(disc_logits, torch.ones_like(disc_logits))
return loss
def _compute_disc_acc(self, disc_agent_logit, disc_demo_logit):
agent_acc = disc_agent_logit < 0
agent_acc = torch.mean(agent_acc.float())
demo_acc = disc_demo_logit > 0
demo_acc = torch.mean(demo_acc.float())
return agent_acc, demo_acc
def _fetch_amp_obs_demo(self, num_samples):
amp_obs_demo = self.vec_env.env.fetch_amp_obs_demo(num_samples)
return amp_obs_demo
def _build_amp_buffers(self):
batch_shape = self.experience_buffer.obs_base_shape
self.experience_buffer.tensor_dict['amp_obs'] = torch.zeros(batch_shape + self._amp_observation_space.shape,
device=self.ppo_device)
amp_obs_demo_buffer_size = int(self.config['amp_obs_demo_buffer_size'])
self._amp_obs_demo_buffer = replay_buffer.ReplayBuffer(amp_obs_demo_buffer_size, self.ppo_device)
self._amp_replay_keep_prob = self.config['amp_replay_keep_prob']
replay_buffer_size = int(self.config['amp_replay_buffer_size'])
self._amp_replay_buffer = replay_buffer.ReplayBuffer(replay_buffer_size, self.ppo_device)
self.tensor_list += ['amp_obs']
return
def _init_amp_demo_buf(self):
buffer_size = self._amp_obs_demo_buffer.get_buffer_size()
num_batches = int(np.ceil(buffer_size / self._amp_batch_size))
for i in range(num_batches):
curr_samples = self._fetch_amp_obs_demo(self._amp_batch_size)
self._amp_obs_demo_buffer.store({'amp_obs': curr_samples})
return
def _update_amp_demos(self):
new_amp_obs_demo = self._fetch_amp_obs_demo(self._amp_batch_size)
self._amp_obs_demo_buffer.store({'amp_obs': new_amp_obs_demo})
return
def _preproc_amp_obs(self, amp_obs):
if self._normalize_amp_input:
amp_obs = self._amp_input_mean_std(amp_obs)
return amp_obs
def _combine_rewards(self, task_rewards, amp_rewards):
disc_r = amp_rewards['disc_rewards']
combined_rewards = self._task_reward_w * task_rewards + \
+ self._disc_reward_w * disc_r
return combined_rewards
def _eval_disc(self, amp_obs):
proc_amp_obs = self._preproc_amp_obs(amp_obs)
return self.model.a2c_network.eval_disc(proc_amp_obs)
def _calc_amp_rewards(self, amp_obs):
disc_r = self._calc_disc_rewards(amp_obs)
output = {
'disc_rewards': disc_r
}
return output
def _calc_disc_rewards(self, amp_obs):
with torch.no_grad():
disc_logits = self._eval_disc(amp_obs)
prob = 1 / (1 + torch.exp(-disc_logits))
disc_r = -torch.log(torch.maximum(1 - prob, torch.tensor(0.0001, device=self.ppo_device)))
disc_r *= self._disc_reward_scale
return disc_r
def _store_replay_amp_obs(self, amp_obs):
buf_size = self._amp_replay_buffer.get_buffer_size()
buf_total_count = self._amp_replay_buffer.get_total_count()
if (buf_total_count > buf_size):
keep_probs = to_torch(np.array([self._amp_replay_keep_prob] * amp_obs.shape[0]), device=self.ppo_device)
keep_mask = torch.bernoulli(keep_probs) == 1.0
amp_obs = amp_obs[keep_mask]
self._amp_replay_buffer.store({'amp_obs': amp_obs})
return
def _record_train_batch_info(self, batch_dict, train_info):
train_info['disc_rewards'] = batch_dict['disc_rewards']
return
def _log_train_info(self, train_info, frame):
super()._log_train_info(train_info, frame)
self.writer.add_scalar('losses/disc_loss', torch_ext.mean_list(train_info['disc_loss']).item(), frame)
self.writer.add_scalar('info/disc_agent_acc', torch_ext.mean_list(train_info['disc_agent_acc']).item(), frame)
self.writer.add_scalar('info/disc_demo_acc', torch_ext.mean_list(train_info['disc_demo_acc']).item(), frame)
self.writer.add_scalar('info/disc_agent_logit', torch_ext.mean_list(train_info['disc_agent_logit']).item(), frame)
self.writer.add_scalar('info/disc_demo_logit', torch_ext.mean_list(train_info['disc_demo_logit']).item(), frame)
self.writer.add_scalar('info/disc_grad_penalty', torch_ext.mean_list(train_info['disc_grad_penalty']).item(), frame)
self.writer.add_scalar('info/disc_logit_loss', torch_ext.mean_list(train_info['disc_logit_loss']).item(), frame)
disc_reward_std, disc_reward_mean = torch.std_mean(train_info['disc_rewards'])
self.writer.add_scalar('info/disc_reward_mean', disc_reward_mean.item(), frame)
self.writer.add_scalar('info/disc_reward_std', disc_reward_std.item(), frame)
return
def _amp_debug(self, info):
with torch.no_grad():
amp_obs = info['amp_obs']
amp_obs = amp_obs[0:1]
disc_pred = self._eval_disc(amp_obs)
amp_rewards = self._calc_amp_rewards(amp_obs)
disc_reward = amp_rewards['disc_rewards']
disc_pred = disc_pred.detach().cpu().numpy()[0, 0]
disc_reward = disc_reward.cpu().numpy()[0, 0]
print("disc_pred: ", disc_pred, disc_reward)
return | 23,314 | Python | 40.933453 | 157 | 0.6035 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/learning/amp_players.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import torch
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch.running_mean_std import RunningMeanStd
from rl_games.common.player import BasePlayer
import isaacgymenvs.learning.common_player as common_player
class AMPPlayerContinuous(common_player.CommonPlayer):
def __init__(self, params):
config = params['config']
self._normalize_amp_input = config.get('normalize_amp_input', True)
self._disc_reward_scale = config['disc_reward_scale']
self._print_disc_prediction = config.get('print_disc_prediction', False)
super().__init__(params)
return
def restore(self, fn):
super().restore(fn)
if self._normalize_amp_input:
checkpoint = torch_ext.load_checkpoint(fn)
self._amp_input_mean_std.load_state_dict(checkpoint['amp_input_mean_std'])
return
def _build_net(self, config):
super()._build_net(config)
if self._normalize_amp_input:
self._amp_input_mean_std = RunningMeanStd(config['amp_input_shape']).to(self.device)
self._amp_input_mean_std.eval()
return
def _post_step(self, info):
super()._post_step(info)
if self._print_disc_prediction:
self._amp_debug(info)
return
def _build_net_config(self):
config = super()._build_net_config()
if (hasattr(self, 'env')):
config['amp_input_shape'] = self.env.amp_observation_space.shape
else:
config['amp_input_shape'] = self.env_info['amp_observation_space']
return config
def _amp_debug(self, info):
with torch.no_grad():
amp_obs = info['amp_obs']
amp_obs = amp_obs[0:1]
disc_pred = self._eval_disc(amp_obs.to(self.device))
amp_rewards = self._calc_amp_rewards(amp_obs.to(self.device))
disc_reward = amp_rewards['disc_rewards']
disc_pred = disc_pred.detach().cpu().numpy()[0, 0]
disc_reward = disc_reward.cpu().numpy()[0, 0]
print("disc_pred: ", disc_pred, disc_reward)
return
def _preproc_amp_obs(self, amp_obs):
if self._normalize_amp_input:
amp_obs = self._amp_input_mean_std(amp_obs)
return amp_obs
def _eval_disc(self, amp_obs):
proc_amp_obs = self._preproc_amp_obs(amp_obs)
return self.model.a2c_network.eval_disc(proc_amp_obs)
def _calc_amp_rewards(self, amp_obs):
disc_r = self._calc_disc_rewards(amp_obs)
output = {
'disc_rewards': disc_r
}
return output
def _calc_disc_rewards(self, amp_obs):
with torch.no_grad():
disc_logits = self._eval_disc(amp_obs)
prob = 1.0 / (1.0 + torch.exp(-disc_logits))
disc_r = -torch.log(torch.maximum(1 - prob, torch.tensor(0.0001, device=self.device)))
disc_r *= self._disc_reward_scale
return disc_r
| 4,535 | Python | 38.103448 | 98 | 0.657773 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/learning/common_agent.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import copy
from datetime import datetime
from gym import spaces
import numpy as np
import os
import time
import yaml
from rl_games.algos_torch import a2c_continuous
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch import central_value
from rl_games.algos_torch.running_mean_std import RunningMeanStd
from rl_games.common import a2c_common
from rl_games.common import datasets
from rl_games.common import schedulers
from rl_games.common import vecenv
import torch
from torch import optim
from . import amp_datasets as amp_datasets
from tensorboardX import SummaryWriter
class CommonAgent(a2c_continuous.A2CAgent):
def __init__(self, base_name, params):
a2c_common.A2CBase.__init__(self, base_name, params)
config = params['config']
self._load_config_params(config)
self.is_discrete = False
self._setup_action_space()
self.bounds_loss_coef = config.get('bounds_loss_coef', None)
self.clip_actions = config.get('clip_actions', True)
self.network_path = self.nn_dir
net_config = self._build_net_config()
self.model = self.network.build(net_config)
self.model.to(self.ppo_device)
self.states = None
self.init_rnn_from_model(self.model)
self.last_lr = float(self.last_lr)
self.optimizer = optim.Adam(self.model.parameters(), float(self.last_lr), eps=1e-08, weight_decay=self.weight_decay)
if self.has_central_value:
cv_config = {
'state_shape' : torch_ext.shape_whc_to_cwh(self.state_shape),
'value_size' : self.value_size,
'ppo_device' : self.ppo_device,
'num_agents' : self.num_agents,
'num_steps' : self.horizon_length,
'num_actors' : self.num_actors,
'num_actions' : self.actions_num,
'seq_len' : self.seq_len,
'model' : self.central_value_config['network'],
'config' : self.central_value_config,
'writter' : self.writer,
'multi_gpu' : self.multi_gpu
}
self.central_value_net = central_value.CentralValueTrain(**cv_config).to(self.ppo_device)
self.use_experimental_cv = self.config.get('use_experimental_cv', True)
self.dataset = amp_datasets.AMPDataset(self.batch_size, self.minibatch_size, self.is_discrete, self.is_rnn, self.ppo_device, self.seq_len)
self.algo_observer.after_init(self)
return
def init_tensors(self):
super().init_tensors()
self.experience_buffer.tensor_dict['next_obses'] = torch.zeros_like(self.experience_buffer.tensor_dict['obses'])
self.experience_buffer.tensor_dict['next_values'] = torch.zeros_like(self.experience_buffer.tensor_dict['values'])
self.tensor_list += ['next_obses']
return
def train(self):
self.init_tensors()
self.last_mean_rewards = -100500
start_time = time.time()
total_time = 0
rep_count = 0
self.frame = 0
self.obs = self.env_reset()
self.curr_frames = self.batch_size_envs
self.model_output_file = os.path.join(self.network_path,
self.config['name'] + '_{date:%d-%H-%M-%S}'.format(date=datetime.now()))
self._init_train()
# global rank of the GPU
# multi-gpu training is not currently supported for AMP
self.global_rank = int(os.getenv("RANK", "0"))
while True:
epoch_num = self.update_epoch()
train_info = self.train_epoch()
sum_time = train_info['total_time']
total_time += sum_time
frame = self.frame
if self.global_rank == 0:
scaled_time = sum_time
scaled_play_time = train_info['play_time']
curr_frames = self.curr_frames
self.frame += curr_frames
if self.print_stats:
fps_step = curr_frames / scaled_play_time
fps_total = curr_frames / scaled_time
print(f'fps step: {fps_step:.1f} fps total: {fps_total:.1f}')
self.writer.add_scalar('performance/total_fps', curr_frames / scaled_time, frame)
self.writer.add_scalar('performance/step_fps', curr_frames / scaled_play_time, frame)
self.writer.add_scalar('info/epochs', epoch_num, frame)
self._log_train_info(train_info, frame)
self.algo_observer.after_print_stats(frame, epoch_num, total_time)
if self.game_rewards.current_size > 0:
mean_rewards = self.game_rewards.get_mean()
mean_lengths = self.game_lengths.get_mean()
for i in range(self.value_size):
self.writer.add_scalar('rewards/frame'.format(i), mean_rewards[i], frame)
self.writer.add_scalar('rewards/iter'.format(i), mean_rewards[i], epoch_num)
self.writer.add_scalar('rewards/time'.format(i), mean_rewards[i], total_time)
self.writer.add_scalar('episode_lengths/frame', mean_lengths, frame)
self.writer.add_scalar('episode_lengths/iter', mean_lengths, epoch_num)
if self.has_self_play_config:
self.self_play_manager.update(self)
if self.save_freq > 0:
if (epoch_num % self.save_freq == 0):
self.save(self.model_output_file + "_" + str(epoch_num))
if epoch_num > self.max_epochs:
self.save(self.model_output_file)
print('MAX EPOCHS NUM!')
return self.last_mean_rewards, epoch_num
update_time = 0
return
def train_epoch(self):
play_time_start = time.time()
with torch.no_grad():
if self.is_rnn:
batch_dict = self.play_steps_rnn()
else:
batch_dict = self.play_steps()
play_time_end = time.time()
update_time_start = time.time()
rnn_masks = batch_dict.get('rnn_masks', None)
self.set_train()
self.curr_frames = batch_dict.pop('played_frames')
self.prepare_dataset(batch_dict)
self.algo_observer.after_steps()
if self.has_central_value:
self.train_central_value()
train_info = None
if self.is_rnn:
frames_mask_ratio = rnn_masks.sum().item() / (rnn_masks.nelement())
print(frames_mask_ratio)
for _ in range(0, self.mini_epochs_num):
ep_kls = []
for i in range(len(self.dataset)):
curr_train_info = self.train_actor_critic(self.dataset[i])
print(type(curr_train_info))
if self.schedule_type == 'legacy':
self.last_lr, self.entropy_coef = self.scheduler.update(self.last_lr, self.entropy_coef, self.epoch_num, 0, curr_train_info['kl'].item())
self.update_lr(self.last_lr)
if (train_info is None):
train_info = dict()
for k, v in curr_train_info.items():
train_info[k] = [v]
else:
for k, v in curr_train_info.items():
train_info[k].append(v)
av_kls = torch_ext.mean_list(train_info['kl'])
if self.schedule_type == 'standard':
self.last_lr, self.entropy_coef = self.scheduler.update(self.last_lr, self.entropy_coef, self.epoch_num, 0, av_kls.item())
self.update_lr(self.last_lr)
if self.schedule_type == 'standard_epoch':
self.last_lr, self.entropy_coef = self.scheduler.update(self.last_lr, self.entropy_coef, self.epoch_num, 0, av_kls.item())
self.update_lr(self.last_lr)
update_time_end = time.time()
play_time = play_time_end - play_time_start
update_time = update_time_end - update_time_start
total_time = update_time_end - play_time_start
train_info['play_time'] = play_time
train_info['update_time'] = update_time
train_info['total_time'] = total_time
self._record_train_batch_info(batch_dict, train_info)
return train_info
def play_steps(self):
self.set_eval()
epinfos = []
update_list = self.update_list
for n in range(self.horizon_length):
self.obs, done_env_ids = self._env_reset_done()
self.experience_buffer.update_data('obses', n, self.obs['obs'])
if self.use_action_masks:
masks = self.vec_env.get_action_masks()
res_dict = self.get_masked_action_values(self.obs, masks)
else:
res_dict = self.get_action_values(self.obs)
for k in update_list:
self.experience_buffer.update_data(k, n, res_dict[k])
if self.has_central_value:
self.experience_buffer.update_data('states', n, self.obs['states'])
self.obs, rewards, self.dones, infos = self.env_step(res_dict['actions'])
shaped_rewards = self.rewards_shaper(rewards)
self.experience_buffer.update_data('rewards', n, shaped_rewards)
self.experience_buffer.update_data('next_obses', n, self.obs['obs'])
self.experience_buffer.update_data('dones', n, self.dones)
terminated = infos['terminate'].float()
terminated = terminated.unsqueeze(-1)
next_vals = self._eval_critic(self.obs)
next_vals *= (1.0 - terminated)
self.experience_buffer.update_data('next_values', n, next_vals)
self.current_rewards += rewards
self.current_lengths += 1
all_done_indices = self.dones.nonzero(as_tuple=False)
done_indices = all_done_indices[::self.num_agents]
self.game_rewards.update(self.current_rewards[done_indices])
self.game_lengths.update(self.current_lengths[done_indices])
self.algo_observer.process_infos(infos, done_indices)
not_dones = 1.0 - self.dones.float()
self.current_rewards = self.current_rewards * not_dones.unsqueeze(1)
self.current_lengths = self.current_lengths * not_dones
mb_fdones = self.experience_buffer.tensor_dict['dones'].float()
mb_values = self.experience_buffer.tensor_dict['values']
mb_next_values = self.experience_buffer.tensor_dict['next_values']
mb_rewards = self.experience_buffer.tensor_dict['rewards']
mb_advs = self.discount_values(mb_fdones, mb_values, mb_rewards, mb_next_values)
mb_returns = mb_advs + mb_values
batch_dict = self.experience_buffer.get_transformed_list(a2c_common.swap_and_flatten01, self.tensor_list)
batch_dict['returns'] = a2c_common.swap_and_flatten01(mb_returns)
batch_dict['played_frames'] = self.batch_size
return batch_dict
def calc_gradients(self, input_dict):
self.set_train()
value_preds_batch = input_dict['old_values']
old_action_log_probs_batch = input_dict['old_logp_actions']
advantage = input_dict['advantages']
old_mu_batch = input_dict['mu']
old_sigma_batch = input_dict['sigma']
return_batch = input_dict['returns']
actions_batch = input_dict['actions']
obs_batch = input_dict['obs']
obs_batch = self._preproc_obs(obs_batch)
lr = self.last_lr
kl = 1.0
lr_mul = 1.0
curr_e_clip = lr_mul * self.e_clip
batch_dict = {
'is_train': True,
'prev_actions': actions_batch,
'obs' : obs_batch
}
rnn_masks = None
if self.is_rnn:
rnn_masks = input_dict['rnn_masks']
batch_dict['rnn_states'] = input_dict['rnn_states']
batch_dict['seq_length'] = self.seq_len
with torch.cuda.amp.autocast(enabled=self.mixed_precision):
res_dict = self.model(batch_dict)
action_log_probs = res_dict['prev_neglogp']
values = res_dict['value']
entropy = res_dict['entropy']
mu = res_dict['mu']
sigma = res_dict['sigma']
a_info = self._actor_loss(old_action_log_probs_batch, action_log_probs, advantage, curr_e_clip)
a_loss = a_info['actor_loss']
c_info = self._critic_loss(value_preds_batch, values, curr_e_clip, return_batch, self.clip_value)
c_loss = c_info['critic_loss']
b_loss = self.bound_loss(mu)
losses, sum_mask = torch_ext.apply_masks([a_loss.unsqueeze(1), c_loss, entropy.unsqueeze(1), b_loss.unsqueeze(1)], rnn_masks)
a_loss, c_loss, entropy, b_loss = losses[0], losses[1], losses[2], losses[3]
loss = a_loss + self.critic_coef * c_loss - self.entropy_coef * entropy + self.bounds_loss_coef * b_loss
if self.multi_gpu:
self.optimizer.zero_grad()
else:
for param in self.model.parameters():
param.grad = None
self.scaler.scale(loss).backward()
#TODO: Refactor this ugliest code of the year
if self.truncate_grads:
if self.multi_gpu:
self.optimizer.synchronize()
self.scaler.unscale_(self.optimizer)
nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_norm)
with self.optimizer.skip_synchronize():
self.scaler.step(self.optimizer)
self.scaler.update()
else:
self.scaler.unscale_(self.optimizer)
nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_norm)
self.scaler.step(self.optimizer)
self.scaler.update()
else:
self.scaler.step(self.optimizer)
self.scaler.update()
with torch.no_grad():
reduce_kl = not self.is_rnn
kl_dist = torch_ext.policy_kl(mu.detach(), sigma.detach(), old_mu_batch, old_sigma_batch, reduce_kl)
if self.is_rnn:
kl_dist = (kl_dist * rnn_masks).sum() / rnn_masks.numel() #/ sum_mask
self.train_result = {
'entropy': entropy,
'kl': kl_dist,
'last_lr': self.last_lr,
'lr_mul': lr_mul,
'b_loss': b_loss
}
self.train_result.update(a_info)
self.train_result.update(c_info)
return
def discount_values(self, mb_fdones, mb_values, mb_rewards, mb_next_values):
lastgaelam = 0
mb_advs = torch.zeros_like(mb_rewards)
for t in reversed(range(self.horizon_length)):
not_done = 1.0 - mb_fdones[t]
not_done = not_done.unsqueeze(1)
delta = mb_rewards[t] + self.gamma * mb_next_values[t] - mb_values[t]
lastgaelam = delta + self.gamma * self.tau * not_done * lastgaelam
mb_advs[t] = lastgaelam
return mb_advs
def bound_loss(self, mu):
if self.bounds_loss_coef is not None:
soft_bound = 1.0
mu_loss_high = torch.maximum(mu - soft_bound, torch.tensor(0, device=self.ppo_device))**2
mu_loss_low = torch.minimum(mu + soft_bound, torch.tensor(0, device=self.ppo_device))**2
b_loss = (mu_loss_low + mu_loss_high).sum(axis=-1)
else:
b_loss = 0
return b_loss
def _load_config_params(self, config):
self.last_lr = config['learning_rate']
return
def _build_net_config(self):
obs_shape = torch_ext.shape_whc_to_cwh(self.obs_shape)
config = {
'actions_num' : self.actions_num,
'input_shape' : obs_shape,
'num_seqs' : self.num_actors * self.num_agents,
'value_size': self.env_info.get('value_size', 1),
'normalize_value' : self.normalize_value,
'normalize_input': self.normalize_input,
}
return config
def _setup_action_space(self):
action_space = self.env_info['action_space']
self.actions_num = action_space.shape[0]
# todo introduce device instead of cuda()
self.actions_low = torch.from_numpy(action_space.low.copy()).float().to(self.ppo_device)
self.actions_high = torch.from_numpy(action_space.high.copy()).float().to(self.ppo_device)
return
def _init_train(self):
return
def _env_reset_done(self):
obs, done_env_ids = self.vec_env.reset_done()
return self.obs_to_tensors(obs), done_env_ids
def _eval_critic(self, obs_dict):
self.model.eval()
obs = obs_dict['obs']
processed_obs = self._preproc_obs(obs)
if self.normalize_input:
processed_obs = self.model.norm_obs(processed_obs)
value = self.model.a2c_network.eval_critic(processed_obs)
if self.normalize_value:
value = self.value_mean_std(value, True)
return value
def _actor_loss(self, old_action_log_probs_batch, action_log_probs, advantage, curr_e_clip):
clip_frac = None
if (self.ppo):
ratio = torch.exp(old_action_log_probs_batch - action_log_probs)
surr1 = advantage * ratio
surr2 = advantage * torch.clamp(ratio, 1.0 - curr_e_clip,
1.0 + curr_e_clip)
a_loss = torch.max(-surr1, -surr2)
clipped = torch.abs(ratio - 1.0) > curr_e_clip
clip_frac = torch.mean(clipped.float())
clip_frac = clip_frac.detach()
else:
a_loss = (action_log_probs * advantage)
info = {
'actor_loss': a_loss,
'actor_clip_frac': clip_frac
}
return info
def _critic_loss(self, value_preds_batch, values, curr_e_clip, return_batch, clip_value):
if clip_value:
value_pred_clipped = value_preds_batch + \
(values - value_preds_batch).clamp(-curr_e_clip, curr_e_clip)
value_losses = (values - return_batch)**2
value_losses_clipped = (value_pred_clipped - return_batch)**2
c_loss = torch.max(value_losses, value_losses_clipped)
else:
c_loss = (return_batch - values)**2
info = {
'critic_loss': c_loss
}
return info
def _record_train_batch_info(self, batch_dict, train_info):
return
def _log_train_info(self, train_info, frame):
self.writer.add_scalar('performance/update_time', train_info['update_time'], frame)
self.writer.add_scalar('performance/play_time', train_info['play_time'], frame)
self.writer.add_scalar('losses/a_loss', torch_ext.mean_list(train_info['actor_loss']).item(), frame)
self.writer.add_scalar('losses/c_loss', torch_ext.mean_list(train_info['critic_loss']).item(), frame)
self.writer.add_scalar('losses/bounds_loss', torch_ext.mean_list(train_info['b_loss']).item(), frame)
self.writer.add_scalar('losses/entropy', torch_ext.mean_list(train_info['entropy']).item(), frame)
self.writer.add_scalar('info/last_lr', train_info['last_lr'][-1] * train_info['lr_mul'][-1], frame)
self.writer.add_scalar('info/lr_mul', train_info['lr_mul'][-1], frame)
self.writer.add_scalar('info/e_clip', self.e_clip * train_info['lr_mul'][-1], frame)
self.writer.add_scalar('info/clip_frac', torch_ext.mean_list(train_info['actor_clip_frac']).item(), frame)
self.writer.add_scalar('info/kl', torch_ext.mean_list(train_info['kl']).item(), frame)
return
| 21,575 | Python | 39.863636 | 157 | 0.585724 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/learning/common_player.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import torch
from rl_games.algos_torch import players
from rl_games.algos_torch import torch_ext
from rl_games.algos_torch.running_mean_std import RunningMeanStd
from rl_games.common.player import BasePlayer
class CommonPlayer(players.PpoPlayerContinuous):
def __init__(self, params):
BasePlayer.__init__(self, params)
self.network = self.config['network']
self.normalize_input = self.config['normalize_input']
self.normalize_value = self.config['normalize_value']
self._setup_action_space()
self.mask = [False]
net_config = self._build_net_config()
self._build_net(net_config)
return
def run(self):
n_games = self.games_num
render = self.render_env
n_game_life = self.n_game_life
is_determenistic = self.is_deterministic
sum_rewards = 0
sum_steps = 0
sum_game_res = 0
n_games = n_games * n_game_life
games_played = 0
has_masks = False
has_masks_func = getattr(self.env, "has_action_mask", None) is not None
op_agent = getattr(self.env, "create_agent", None)
if op_agent:
agent_inited = True
if has_masks_func:
has_masks = self.env.has_action_mask()
need_init_rnn = self.is_rnn
for _ in range(n_games):
if games_played >= n_games:
break
obs_dict = self.env_reset(self.env)
batch_size = 1
batch_size = self.get_batch_size(obs_dict['obs'], batch_size)
if need_init_rnn:
self.init_rnn()
need_init_rnn = False
cr = torch.zeros(batch_size, dtype=torch.float32)
steps = torch.zeros(batch_size, dtype=torch.float32)
print_game_res = False
for n in range(self.max_steps):
obs_dict, done_env_ids = self._env_reset_done()
if has_masks:
masks = self.env.get_action_mask()
action = self.get_masked_action(obs_dict, masks, is_determenistic)
else:
action = self.get_action(obs_dict, is_determenistic)
obs_dict, r, done, info = self.env_step(self.env, action)
cr += r
steps += 1
self._post_step(info)
if render:
self.env.render(mode = 'human')
time.sleep(self.render_sleep)
all_done_indices = done.nonzero(as_tuple=False)
done_indices = all_done_indices[::self.num_agents]
done_count = len(done_indices)
games_played += done_count
if done_count > 0:
if self.is_rnn:
for s in self.states:
s[:,all_done_indices,:] = s[:,all_done_indices,:] * 0.0
cur_rewards = cr[done_indices].sum().item()
cur_steps = steps[done_indices].sum().item()
cr = cr * (1.0 - done.float())
steps = steps * (1.0 - done.float())
sum_rewards += cur_rewards
sum_steps += cur_steps
game_res = 0.0
if isinstance(info, dict):
if 'battle_won' in info:
print_game_res = True
game_res = info.get('battle_won', 0.5)
if 'scores' in info:
print_game_res = True
game_res = info.get('scores', 0.5)
if self.print_stats:
if print_game_res:
print('reward:', cur_rewards/done_count, 'steps:', cur_steps/done_count, 'w:', game_res)
else:
print('reward:', cur_rewards/done_count, 'steps:', cur_steps/done_count)
sum_game_res += game_res
if batch_size//self.num_agents == 1 or games_played >= n_games:
break
print(sum_rewards)
if print_game_res:
print('av reward:', sum_rewards / games_played * n_game_life, 'av steps:', sum_steps / games_played * n_game_life, 'winrate:', sum_game_res / games_played * n_game_life)
else:
print('av reward:', sum_rewards / games_played * n_game_life, 'av steps:', sum_steps / games_played * n_game_life)
return
def obs_to_torch(self, obs):
obs = super().obs_to_torch(obs)
obs_dict = {
'obs': obs
}
return obs_dict
def get_action(self, obs_dict, is_determenistic = False):
output = super().get_action(obs_dict['obs'], is_determenistic)
return output
def _build_net(self, config):
self.model = self.network.build(config)
self.model.to(self.device)
self.model.eval()
self.is_rnn = self.model.is_rnn()
return
def _env_reset_done(self):
obs, done_env_ids = self.env.reset_done()
return self.obs_to_torch(obs), done_env_ids
def _post_step(self, info):
return
def _build_net_config(self):
obs_shape = torch_ext.shape_whc_to_cwh(self.obs_shape)
config = {
'actions_num' : self.actions_num,
'input_shape' : obs_shape,
'num_seqs' : self.num_agents,
'value_size': self.env_info.get('value_size', 1),
'normalize_value': self.normalize_value,
'normalize_input': self.normalize_input,
}
return config
def _setup_action_space(self):
self.actions_num = self.action_space.shape[0]
self.actions_low = torch.from_numpy(self.action_space.low.copy()).float().to(self.device)
self.actions_high = torch.from_numpy(self.action_space.high.copy()).float().to(self.device)
return | 7,570 | Python | 37.627551 | 181 | 0.571731 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/allegro_hand.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymtorch
from isaacgym import gymapi
from isaacgymenvs.utils.torch_jit_utils import scale, unscale, quat_mul, quat_conjugate, quat_from_angle_axis, \
to_torch, get_axis_params, torch_rand_float, tensor_clamp
from isaacgymenvs.tasks.base.vec_task import VecTask
class AllegroHand(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.aggregate_mode = self.cfg["env"]["aggregateMode"]
self.dist_reward_scale = self.cfg["env"]["distRewardScale"]
self.rot_reward_scale = self.cfg["env"]["rotRewardScale"]
self.action_penalty_scale = self.cfg["env"]["actionPenaltyScale"]
self.success_tolerance = self.cfg["env"]["successTolerance"]
self.reach_goal_bonus = self.cfg["env"]["reachGoalBonus"]
self.fall_dist = self.cfg["env"]["fallDistance"]
self.fall_penalty = self.cfg["env"]["fallPenalty"]
self.rot_eps = self.cfg["env"]["rotEps"]
self.vel_obs_scale = 0.2 # scale factor of velocity based observations
self.force_torque_obs_scale = 10.0 # scale factor of velocity based observations
self.reset_position_noise = self.cfg["env"]["resetPositionNoise"]
self.reset_rotation_noise = self.cfg["env"]["resetRotationNoise"]
self.reset_dof_pos_noise = self.cfg["env"]["resetDofPosRandomInterval"]
self.reset_dof_vel_noise = self.cfg["env"]["resetDofVelRandomInterval"]
self.force_scale = self.cfg["env"].get("forceScale", 0.0)
self.force_prob_range = self.cfg["env"].get("forceProbRange", [0.001, 0.1])
self.force_decay = self.cfg["env"].get("forceDecay", 0.99)
self.force_decay_interval = self.cfg["env"].get("forceDecayInterval", 0.08)
self.shadow_hand_dof_speed_scale = self.cfg["env"]["dofSpeedScale"]
self.use_relative_control = self.cfg["env"]["useRelativeControl"]
self.act_moving_average = self.cfg["env"]["actionsMovingAverage"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.reset_time = self.cfg["env"].get("resetTime", -1.0)
self.print_success_stat = self.cfg["env"]["printNumSuccesses"]
self.max_consecutive_successes = self.cfg["env"]["maxConsecutiveSuccesses"]
self.av_factor = self.cfg["env"].get("averFactor", 0.1)
self.object_type = self.cfg["env"]["objectType"]
assert self.object_type in ["block", "egg", "pen"]
self.ignore_z = (self.object_type == "pen")
self.asset_files_dict = {
"block": "urdf/objects/cube_multicolor.urdf",
"egg": "mjcf/open_ai_assets/hand/egg.xml",
"pen": "mjcf/open_ai_assets/hand/pen.xml"
}
if "asset" in self.cfg["env"]:
self.asset_files_dict["block"] = self.cfg["env"]["asset"].get("assetFileNameBlock", self.asset_files_dict["block"])
self.asset_files_dict["egg"] = self.cfg["env"]["asset"].get("assetFileNameEgg", self.asset_files_dict["egg"])
self.asset_files_dict["pen"] = self.cfg["env"]["asset"].get("assetFileNamePen", self.asset_files_dict["pen"])
# can be "full_no_vel", "full", "full_state"
self.obs_type = self.cfg["env"]["observationType"]
if not (self.obs_type in ["full_no_vel", "full", "full_state"]):
raise Exception(
"Unknown type of observations!\nobservationType should be one of: [openai, full_no_vel, full, full_state]")
print("Obs type:", self.obs_type)
self.num_obs_dict = {
"full_no_vel": 50,
"full": 72,
"full_state": 88
}
self.up_axis = 'z'
self.use_vel_obs = False
self.fingertip_obs = True
self.asymmetric_obs = self.cfg["env"]["asymmetric_observations"]
num_states = 0
if self.asymmetric_obs:
num_states = 88
self.cfg["env"]["numObservations"] = self.num_obs_dict[self.obs_type]
self.cfg["env"]["numStates"] = num_states
self.cfg["env"]["numActions"] = 16
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
self.dt = self.sim_params.dt
control_freq_inv = self.cfg["env"].get("controlFrequencyInv", 1)
if self.reset_time > 0.0:
self.max_episode_length = int(round(self.reset_time/(control_freq_inv * self.dt)))
print("Reset time: ", self.reset_time)
print("New episode length: ", self.max_episode_length)
if self.viewer != None:
cam_pos = gymapi.Vec3(10.0, 5.0, 1.0)
cam_target = gymapi.Vec3(6.0, 5.0, 0.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym GPU state tensors
actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
rigid_body_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
if self.obs_type == "full_state" or self.asymmetric_obs:
# sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
# self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, self.num_fingertips * 6)
dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs, self.num_shadow_hand_dofs)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
# create some wrapper tensors for different slices
self.shadow_hand_default_dof_pos = torch.zeros(self.num_shadow_hand_dofs, dtype=torch.float, device=self.device)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.shadow_hand_dof_state = self.dof_state.view(self.num_envs, -1, 2)[:, :self.num_shadow_hand_dofs]
self.shadow_hand_dof_pos = self.shadow_hand_dof_state[..., 0]
self.shadow_hand_dof_vel = self.shadow_hand_dof_state[..., 1]
self.rigid_body_states = gymtorch.wrap_tensor(rigid_body_tensor).view(self.num_envs, -1, 13)
self.num_bodies = self.rigid_body_states.shape[1]
self.root_state_tensor = gymtorch.wrap_tensor(actor_root_state_tensor).view(-1, 13)
self.num_dofs = self.gym.get_sim_dof_count(self.sim) // self.num_envs
print("Num dofs: ", self.num_dofs)
self.prev_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.cur_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.global_indices = torch.arange(self.num_envs * 3, dtype=torch.int32, device=self.device).view(self.num_envs, -1)
self.x_unit_tensor = to_torch([1, 0, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.y_unit_tensor = to_torch([0, 1, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.z_unit_tensor = to_torch([0, 0, 1], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.reset_goal_buf = self.reset_buf.clone()
self.successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.consecutive_successes = torch.zeros(1, dtype=torch.float, device=self.device)
self.av_factor = to_torch(self.av_factor, dtype=torch.float, device=self.device)
self.total_successes = 0
self.total_resets = 0
# object apply random forces parameters
self.force_decay = to_torch(self.force_decay, dtype=torch.float, device=self.device)
self.force_prob_range = to_torch(self.force_prob_range, dtype=torch.float, device=self.device)
self.random_force_prob = torch.exp((torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(self.num_envs, device=self.device) + torch.log(self.force_prob_range[1]))
self.rb_forces = torch.zeros((self.num_envs, self.num_bodies, 3), dtype=torch.float, device=self.device)
def create_sim(self):
self.dt = self.sim_params.dt
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../assets')
allegro_hand_asset_file = "urdf/kuka_allegro_description/allegro.urdf"
if "asset" in self.cfg["env"]:
asset_root = self.cfg["env"]["asset"].get("assetRoot", asset_root)
allegro_hand_asset_file = self.cfg["env"]["asset"].get("assetFileName", allegro_hand_asset_file)
object_asset_file = self.asset_files_dict[self.object_type]
# load shadow hand_ asset
asset_options = gymapi.AssetOptions()
asset_options.flip_visual_attachments = False
asset_options.fix_base_link = True
asset_options.collapse_fixed_joints = True
asset_options.disable_gravity = True
asset_options.thickness = 0.001
asset_options.angular_damping = 0.01
if self.physics_engine == gymapi.SIM_PHYSX:
asset_options.use_physx_armature = True
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS
allegro_hand_asset = self.gym.load_asset(self.sim, asset_root, allegro_hand_asset_file, asset_options)
self.num_shadow_hand_bodies = self.gym.get_asset_rigid_body_count(allegro_hand_asset)
self.num_shadow_hand_shapes = self.gym.get_asset_rigid_shape_count(allegro_hand_asset)
self.num_shadow_hand_dofs = self.gym.get_asset_dof_count(allegro_hand_asset)
print("Num dofs: ", self.num_shadow_hand_dofs)
self.num_shadow_hand_actuators = self.num_shadow_hand_dofs
self.actuated_dof_indices = [i for i in range(self.num_shadow_hand_dofs)]
# set shadow_hand dof properties
shadow_hand_dof_props = self.gym.get_asset_dof_properties(allegro_hand_asset)
self.shadow_hand_dof_lower_limits = []
self.shadow_hand_dof_upper_limits = []
self.shadow_hand_dof_default_pos = []
self.shadow_hand_dof_default_vel = []
self.sensors = []
sensor_pose = gymapi.Transform()
for i in range(self.num_shadow_hand_dofs):
self.shadow_hand_dof_lower_limits.append(shadow_hand_dof_props['lower'][i])
self.shadow_hand_dof_upper_limits.append(shadow_hand_dof_props['upper'][i])
self.shadow_hand_dof_default_pos.append(0.0)
self.shadow_hand_dof_default_vel.append(0.0)
print("Max effort: ", shadow_hand_dof_props['effort'][i])
shadow_hand_dof_props['effort'][i] = 0.5
shadow_hand_dof_props['stiffness'][i] = 3
shadow_hand_dof_props['damping'][i] = 0.1
shadow_hand_dof_props['friction'][i] = 0.01
shadow_hand_dof_props['armature'][i] = 0.001
self.actuated_dof_indices = to_torch(self.actuated_dof_indices, dtype=torch.long, device=self.device)
self.shadow_hand_dof_lower_limits = to_torch(self.shadow_hand_dof_lower_limits, device=self.device)
self.shadow_hand_dof_upper_limits = to_torch(self.shadow_hand_dof_upper_limits, device=self.device)
self.shadow_hand_dof_default_pos = to_torch(self.shadow_hand_dof_default_pos, device=self.device)
self.shadow_hand_dof_default_vel = to_torch(self.shadow_hand_dof_default_vel, device=self.device)
# load manipulated object and goal assets
object_asset_options = gymapi.AssetOptions()
object_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options)
object_asset_options.disable_gravity = True
goal_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options)
shadow_hand_start_pose = gymapi.Transform()
shadow_hand_start_pose.p = gymapi.Vec3(*get_axis_params(0.5, self.up_axis_idx))
shadow_hand_start_pose.r = gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 1, 0), np.pi) * gymapi.Quat.from_axis_angle(gymapi.Vec3(1, 0, 0), 0.47 * np.pi) * gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 0, 1), 0.25 * np.pi)
object_start_pose = gymapi.Transform()
object_start_pose.p = gymapi.Vec3()
object_start_pose.p.x = shadow_hand_start_pose.p.x
pose_dy, pose_dz = -0.2, 0.06
object_start_pose.p.y = shadow_hand_start_pose.p.y + pose_dy
object_start_pose.p.z = shadow_hand_start_pose.p.z + pose_dz
if self.object_type == "pen":
object_start_pose.p.z = shadow_hand_start_pose.p.z + 0.02
self.goal_displacement = gymapi.Vec3(-0.2, -0.06, 0.12)
self.goal_displacement_tensor = to_torch(
[self.goal_displacement.x, self.goal_displacement.y, self.goal_displacement.z], device=self.device)
goal_start_pose = gymapi.Transform()
goal_start_pose.p = object_start_pose.p + self.goal_displacement
goal_start_pose.p.z -= 0.04
# compute aggregate size
max_agg_bodies = self.num_shadow_hand_bodies + 2
max_agg_shapes = self.num_shadow_hand_shapes + 2
self.allegro_hands = []
self.envs = []
self.object_init_state = []
self.hand_start_states = []
self.hand_indices = []
self.fingertip_indices = []
self.object_indices = []
self.goal_object_indices = []
shadow_hand_rb_count = self.gym.get_asset_rigid_body_count(allegro_hand_asset)
object_rb_count = self.gym.get_asset_rigid_body_count(object_asset)
self.object_rb_handles = list(range(shadow_hand_rb_count, shadow_hand_rb_count + object_rb_count))
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
if self.aggregate_mode >= 1:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
# add hand - collision filter = -1 to use asset collision filters set in mjcf loader
allegro_hand_actor = self.gym.create_actor(env_ptr, allegro_hand_asset, shadow_hand_start_pose, "hand", i, -1, 0)
self.hand_start_states.append([shadow_hand_start_pose.p.x, shadow_hand_start_pose.p.y, shadow_hand_start_pose.p.z,
shadow_hand_start_pose.r.x, shadow_hand_start_pose.r.y, shadow_hand_start_pose.r.z, shadow_hand_start_pose.r.w,
0, 0, 0, 0, 0, 0])
self.gym.set_actor_dof_properties(env_ptr, allegro_hand_actor, shadow_hand_dof_props)
hand_idx = self.gym.get_actor_index(env_ptr, allegro_hand_actor, gymapi.DOMAIN_SIM)
self.hand_indices.append(hand_idx)
# add object
object_handle = self.gym.create_actor(env_ptr, object_asset, object_start_pose, "object", i, 0, 0)
self.object_init_state.append([object_start_pose.p.x, object_start_pose.p.y, object_start_pose.p.z,
object_start_pose.r.x, object_start_pose.r.y, object_start_pose.r.z, object_start_pose.r.w,
0, 0, 0, 0, 0, 0])
object_idx = self.gym.get_actor_index(env_ptr, object_handle, gymapi.DOMAIN_SIM)
self.object_indices.append(object_idx)
# add goal object
goal_handle = self.gym.create_actor(env_ptr, goal_asset, goal_start_pose, "goal_object", i + self.num_envs, 0, 0)
goal_object_idx = self.gym.get_actor_index(env_ptr, goal_handle, gymapi.DOMAIN_SIM)
self.goal_object_indices.append(goal_object_idx)
if self.object_type != "block":
self.gym.set_rigid_body_color(
env_ptr, object_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
self.gym.set_rigid_body_color(
env_ptr, goal_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
if self.aggregate_mode > 0:
self.gym.end_aggregate(env_ptr)
self.envs.append(env_ptr)
self.allegro_hands.append(allegro_hand_actor)
object_rb_props = self.gym.get_actor_rigid_body_properties(env_ptr, object_handle)
self.object_rb_masses = [prop.mass for prop in object_rb_props]
self.object_init_state = to_torch(self.object_init_state, device=self.device, dtype=torch.float).view(self.num_envs, 13)
self.goal_states = self.object_init_state.clone()
self.goal_states[:, self.up_axis_idx] -= 0.04
self.goal_init_state = self.goal_states.clone()
self.hand_start_states = to_torch(self.hand_start_states, device=self.device).view(self.num_envs, 13)
self.object_rb_handles = to_torch(self.object_rb_handles, dtype=torch.long, device=self.device)
self.object_rb_masses = to_torch(self.object_rb_masses, dtype=torch.float, device=self.device)
self.hand_indices = to_torch(self.hand_indices, dtype=torch.long, device=self.device)
self.object_indices = to_torch(self.object_indices, dtype=torch.long, device=self.device)
self.goal_object_indices = to_torch(self.goal_object_indices, dtype=torch.long, device=self.device)
def compute_reward(self, actions):
self.rew_buf[:], self.reset_buf[:], self.reset_goal_buf[:], self.progress_buf[:], self.successes[:], self.consecutive_successes[:] = compute_hand_reward(
self.rew_buf, self.reset_buf, self.reset_goal_buf, self.progress_buf, self.successes, self.consecutive_successes,
self.max_episode_length, self.object_pos, self.object_rot, self.goal_pos, self.goal_rot,
self.dist_reward_scale, self.rot_reward_scale, self.rot_eps, self.actions, self.action_penalty_scale,
self.success_tolerance, self.reach_goal_bonus, self.fall_dist, self.fall_penalty,
self.max_consecutive_successes, self.av_factor, (self.object_type == "pen")
)
self.extras['consecutive_successes'] = self.consecutive_successes.mean()
if self.print_success_stat:
self.total_resets = self.total_resets + self.reset_buf.sum()
direct_average_successes = self.total_successes + self.successes.sum()
self.total_successes = self.total_successes + (self.successes * self.reset_buf).sum()
# The direct average shows the overall result more quickly, but slightly undershoots long term
# policy performance.
print("Direct average consecutive successes = {:.1f}".format(direct_average_successes/(self.total_resets + self.num_envs)))
if self.total_resets > 0:
print("Post-Reset average consecutive successes = {:.1f}".format(self.total_successes/self.total_resets))
def compute_observations(self):
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
if self.obs_type == "full_state" or self.asymmetric_obs:
self.gym.refresh_force_sensor_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.object_pose = self.root_state_tensor[self.object_indices, 0:7]
self.object_pos = self.root_state_tensor[self.object_indices, 0:3]
self.object_rot = self.root_state_tensor[self.object_indices, 3:7]
self.object_linvel = self.root_state_tensor[self.object_indices, 7:10]
self.object_angvel = self.root_state_tensor[self.object_indices, 10:13]
self.goal_pose = self.goal_states[:, 0:7]
self.goal_pos = self.goal_states[:, 0:3]
self.goal_rot = self.goal_states[:, 3:7]
if self.obs_type == "full_no_vel":
self.compute_full_observations(True)
elif self.obs_type == "full":
self.compute_full_observations()
elif self.obs_type == "full_state":
self.compute_full_state()
else:
print("Unknown observations type!")
if self.asymmetric_obs:
self.compute_full_state(True)
def compute_full_observations(self, no_vel=False):
if no_vel:
self.obs_buf[:, 0:self.num_shadow_hand_dofs] = unscale(self.shadow_hand_dof_pos,
self.shadow_hand_dof_lower_limits, self.shadow_hand_dof_upper_limits)
self.obs_buf[:, 16:23] = self.object_pose
self.obs_buf[:, 23:30] = self.goal_pose
self.obs_buf[:, 30:34] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
self.obs_buf[:, 34:50] = self.actions
else:
self.obs_buf[:, 0:self.num_shadow_hand_dofs] = unscale(self.shadow_hand_dof_pos,
self.shadow_hand_dof_lower_limits, self.shadow_hand_dof_upper_limits)
self.obs_buf[:, self.num_shadow_hand_dofs:2*self.num_shadow_hand_dofs] = self.vel_obs_scale * self.shadow_hand_dof_vel
# 2*16 = 32 -16
self.obs_buf[:, 32:39] = self.object_pose
self.obs_buf[:, 39:42] = self.object_linvel
self.obs_buf[:, 42:45] = self.vel_obs_scale * self.object_angvel
self.obs_buf[:, 45:52] = self.goal_pose
self.obs_buf[:, 52:56] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
self.obs_buf[:, 56:72] = self.actions
def compute_full_state(self, asymm_obs=False):
if asymm_obs:
self.states_buf[:, 0:self.num_shadow_hand_dofs] = unscale(self.shadow_hand_dof_pos,
self.shadow_hand_dof_lower_limits, self.shadow_hand_dof_upper_limits)
self.states_buf[:, self.num_shadow_hand_dofs:2*self.num_shadow_hand_dofs] = self.vel_obs_scale * self.shadow_hand_dof_vel
self.states_buf[:, 2*self.num_shadow_hand_dofs:3*self.num_shadow_hand_dofs] = self.force_torque_obs_scale * self.dof_force_tensor
obj_obs_start = 3*self.num_shadow_hand_dofs # 48
self.states_buf[:, obj_obs_start:obj_obs_start + 7] = self.object_pose
self.states_buf[:, obj_obs_start + 7:obj_obs_start + 10] = self.object_linvel
self.states_buf[:, obj_obs_start + 10:obj_obs_start + 13] = self.vel_obs_scale * self.object_angvel
goal_obs_start = obj_obs_start + 13 # 61
self.states_buf[:, goal_obs_start:goal_obs_start + 7] = self.goal_pose
self.states_buf[:, goal_obs_start + 7:goal_obs_start + 11] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
fingertip_obs_start = goal_obs_start + 11 # 72
# obs_end = 96 + 65 + 30 = 191
# obs_total = obs_end + num_actions = 72 + 16 = 88
obs_end = fingertip_obs_start
self.states_buf[:, obs_end:obs_end + self.num_actions] = self.actions
else:
self.obs_buf[:, 0:self.num_shadow_hand_dofs] = unscale(self.shadow_hand_dof_pos,
self.shadow_hand_dof_lower_limits, self.shadow_hand_dof_upper_limits)
self.obs_buf[:, self.num_shadow_hand_dofs:2*self.num_shadow_hand_dofs] = self.vel_obs_scale * self.shadow_hand_dof_vel
self.obs_buf[:, 2*self.num_shadow_hand_dofs:3*self.num_shadow_hand_dofs] = self.force_torque_obs_scale * self.dof_force_tensor
obj_obs_start = 3*self.num_shadow_hand_dofs # 48
self.obs_buf[:, obj_obs_start:obj_obs_start + 7] = self.object_pose
self.obs_buf[:, obj_obs_start + 7:obj_obs_start + 10] = self.object_linvel
self.obs_buf[:, obj_obs_start + 10:obj_obs_start + 13] = self.vel_obs_scale * self.object_angvel
goal_obs_start = obj_obs_start + 13 # 61
self.obs_buf[:, goal_obs_start:goal_obs_start + 7] = self.goal_pose
self.obs_buf[:, goal_obs_start + 7:goal_obs_start + 11] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
fingertip_obs_start = goal_obs_start + 11 # 72
# obs_end = 96 + 65 + 30 = 191
# obs_total = obs_end + num_actions = 72 + 16 = 88
obs_end = fingertip_obs_start #+ num_ft_states + num_ft_force_torques
self.obs_buf[:, obs_end:obs_end + self.num_actions] = self.actions
def reset_target_pose(self, env_ids, apply_reset=False):
rand_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), 4), device=self.device)
new_rot = randomize_rotation(rand_floats[:, 0], rand_floats[:, 1], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids])
self.goal_states[env_ids, 0:3] = self.goal_init_state[env_ids, 0:3]
self.goal_states[env_ids, 3:7] = new_rot
self.root_state_tensor[self.goal_object_indices[env_ids], 0:3] = self.goal_states[env_ids, 0:3] + self.goal_displacement_tensor
self.root_state_tensor[self.goal_object_indices[env_ids], 3:7] = self.goal_states[env_ids, 3:7]
self.root_state_tensor[self.goal_object_indices[env_ids], 7:13] = torch.zeros_like(self.root_state_tensor[self.goal_object_indices[env_ids], 7:13])
if apply_reset:
goal_object_indices = self.goal_object_indices[env_ids].to(torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state_tensor),
gymtorch.unwrap_tensor(goal_object_indices), len(env_ids))
self.reset_goal_buf[env_ids] = 0
def reset_idx(self, env_ids, goal_env_ids):
# generate random values
rand_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), self.num_shadow_hand_dofs * 2 + 5), device=self.device)
# randomize start object poses
self.reset_target_pose(env_ids)
# reset rigid body forces
self.rb_forces[env_ids, :, :] = 0.0
# reset object
self.root_state_tensor[self.object_indices[env_ids]] = self.object_init_state[env_ids].clone()
self.root_state_tensor[self.object_indices[env_ids], 0:2] = self.object_init_state[env_ids, 0:2] + \
self.reset_position_noise * rand_floats[:, 0:2]
self.root_state_tensor[self.object_indices[env_ids], self.up_axis_idx] = self.object_init_state[env_ids, self.up_axis_idx] + \
self.reset_position_noise * rand_floats[:, self.up_axis_idx]
new_object_rot = randomize_rotation(rand_floats[:, 3], rand_floats[:, 4], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids])
if self.object_type == "pen":
rand_angle_y = torch.tensor(0.3)
new_object_rot = randomize_rotation_pen(rand_floats[:, 3], rand_floats[:, 4], rand_angle_y,
self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids], self.z_unit_tensor[env_ids])
self.root_state_tensor[self.object_indices[env_ids], 3:7] = new_object_rot
self.root_state_tensor[self.object_indices[env_ids], 7:13] = torch.zeros_like(self.root_state_tensor[self.object_indices[env_ids], 7:13])
object_indices = torch.unique(torch.cat([self.object_indices[env_ids],
self.goal_object_indices[env_ids],
self.goal_object_indices[goal_env_ids]]).to(torch.int32))
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state_tensor),
gymtorch.unwrap_tensor(object_indices), len(object_indices))
# reset random force probabilities
self.random_force_prob[env_ids] = torch.exp((torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(len(env_ids), device=self.device) + torch.log(self.force_prob_range[1]))
# reset shadow hand
delta_max = self.shadow_hand_dof_upper_limits - self.shadow_hand_dof_default_pos
delta_min = self.shadow_hand_dof_lower_limits - self.shadow_hand_dof_default_pos
rand_delta = delta_min + (delta_max - delta_min) * 0.5 * (rand_floats[:, 5:5+self.num_shadow_hand_dofs] + 1)
pos = self.shadow_hand_default_dof_pos + self.reset_dof_pos_noise * rand_delta
self.shadow_hand_dof_pos[env_ids, :] = pos
self.shadow_hand_dof_vel[env_ids, :] = self.shadow_hand_dof_default_vel + \
self.reset_dof_vel_noise * rand_floats[:, 5+self.num_shadow_hand_dofs:5+self.num_shadow_hand_dofs*2]
self.prev_targets[env_ids, :self.num_shadow_hand_dofs] = pos
self.cur_targets[env_ids, :self.num_shadow_hand_dofs] = pos
hand_indices = self.hand_indices[env_ids].to(torch.int32)
self.gym.set_dof_position_target_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.prev_targets),
gymtorch.unwrap_tensor(hand_indices), len(env_ids))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(hand_indices), len(env_ids))
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
self.successes[env_ids] = 0
def pre_physics_step(self, actions):
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
goal_env_ids = self.reset_goal_buf.nonzero(as_tuple=False).squeeze(-1)
# if only goals need reset, then call set API
if len(goal_env_ids) > 0 and len(env_ids) == 0:
self.reset_target_pose(goal_env_ids, apply_reset=True)
# if goals need reset in addition to other envs, call set API in reset()
elif len(goal_env_ids) > 0:
self.reset_target_pose(goal_env_ids)
if len(env_ids) > 0:
self.reset_idx(env_ids, goal_env_ids)
self.actions = actions.clone().to(self.device)
if self.use_relative_control:
targets = self.prev_targets[:, self.actuated_dof_indices] + self.shadow_hand_dof_speed_scale * self.dt * self.actions
self.cur_targets[:, self.actuated_dof_indices] = tensor_clamp(targets,
self.shadow_hand_dof_lower_limits[self.actuated_dof_indices], self.shadow_hand_dof_upper_limits[self.actuated_dof_indices])
else:
self.cur_targets[:, self.actuated_dof_indices] = scale(self.actions,
self.shadow_hand_dof_lower_limits[self.actuated_dof_indices], self.shadow_hand_dof_upper_limits[self.actuated_dof_indices])
self.cur_targets[:, self.actuated_dof_indices] = self.act_moving_average * self.cur_targets[:,
self.actuated_dof_indices] + (1.0 - self.act_moving_average) * self.prev_targets[:, self.actuated_dof_indices]
self.cur_targets[:, self.actuated_dof_indices] = tensor_clamp(self.cur_targets[:, self.actuated_dof_indices],
self.shadow_hand_dof_lower_limits[self.actuated_dof_indices], self.shadow_hand_dof_upper_limits[self.actuated_dof_indices])
self.prev_targets[:, self.actuated_dof_indices] = self.cur_targets[:, self.actuated_dof_indices]
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(self.cur_targets))
if self.force_scale > 0.0:
self.rb_forces *= torch.pow(self.force_decay, self.dt / self.force_decay_interval)
# apply new forces
force_indices = (torch.rand(self.num_envs, device=self.device) < self.random_force_prob).nonzero()
self.rb_forces[force_indices, self.object_rb_handles, :] = torch.randn(
self.rb_forces[force_indices, self.object_rb_handles, :].shape, device=self.device) * self.object_rb_masses * self.force_scale
self.gym.apply_rigid_body_force_tensors(self.sim, gymtorch.unwrap_tensor(self.rb_forces), None, gymapi.LOCAL_SPACE)
def post_physics_step(self):
self.progress_buf += 1
self.randomize_buf += 1
self.compute_observations()
self.compute_reward(self.actions)
if self.viewer and self.debug_viz:
# draw axes on target object
self.gym.clear_lines(self.viewer)
self.gym.refresh_rigid_body_state_tensor(self.sim)
for i in range(self.num_envs):
targetx = (self.goal_pos[i] + quat_apply(self.goal_rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
targety = (self.goal_pos[i] + quat_apply(self.goal_rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
targetz = (self.goal_pos[i] + quat_apply(self.goal_rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.goal_pos[i].cpu().numpy() + self.goal_displacement_tensor.cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], targetx[0], targetx[1], targetx[2]], [0.85, 0.1, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], targety[0], targety[1], targety[2]], [0.1, 0.85, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], targetz[0], targetz[1], targetz[2]], [0.1, 0.1, 0.85])
objectx = (self.object_pos[i] + quat_apply(self.object_rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
objecty = (self.object_pos[i] + quat_apply(self.object_rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
objectz = (self.object_pos[i] + quat_apply(self.object_rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.object_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], objectx[0], objectx[1], objectx[2]], [0.85, 0.1, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], objecty[0], objecty[1], objecty[2]], [0.1, 0.85, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], objectz[0], objectz[1], objectz[2]], [0.1, 0.1, 0.85])
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_hand_reward(
rew_buf, reset_buf, reset_goal_buf, progress_buf, successes, consecutive_successes,
max_episode_length: float, object_pos, object_rot, target_pos, target_rot,
dist_reward_scale: float, rot_reward_scale: float, rot_eps: float,
actions, action_penalty_scale: float,
success_tolerance: float, reach_goal_bonus: float, fall_dist: float,
fall_penalty: float, max_consecutive_successes: int, av_factor: float, ignore_z_rot: bool
):
# Distance from the hand to the object
goal_dist = torch.norm(object_pos - target_pos, p=2, dim=-1)
if ignore_z_rot:
success_tolerance = 2.0 * success_tolerance
# Orientation alignment for the cube in hand and goal cube
quat_diff = quat_mul(object_rot, quat_conjugate(target_rot))
rot_dist = 2.0 * torch.asin(torch.clamp(torch.norm(quat_diff[:, 0:3], p=2, dim=-1), max=1.0))
dist_rew = goal_dist * dist_reward_scale
rot_rew = 1.0/(torch.abs(rot_dist) + rot_eps) * rot_reward_scale
action_penalty = torch.sum(actions ** 2, dim=-1)
# Total reward is: position distance + orientation alignment + action regularization + success bonus + fall penalty
reward = dist_rew + rot_rew + action_penalty * action_penalty_scale
# Find out which envs hit the goal and update successes count
goal_resets = torch.where(torch.abs(rot_dist) <= success_tolerance, torch.ones_like(reset_goal_buf), reset_goal_buf)
successes = successes + goal_resets
# Success bonus: orientation is within `success_tolerance` of goal orientation
reward = torch.where(goal_resets == 1, reward + reach_goal_bonus, reward)
# Fall penalty: distance to the goal is larger than a threshold
reward = torch.where(goal_dist >= fall_dist, reward + fall_penalty, reward)
# Check env termination conditions, including maximum success number
resets = torch.where(goal_dist >= fall_dist, torch.ones_like(reset_buf), reset_buf)
if max_consecutive_successes > 0:
# Reset progress buffer on goal envs if max_consecutive_successes > 0
progress_buf = torch.where(torch.abs(rot_dist) <= success_tolerance, torch.zeros_like(progress_buf), progress_buf)
resets = torch.where(successes >= max_consecutive_successes, torch.ones_like(resets), resets)
timed_out = progress_buf >= max_episode_length - 1
resets = torch.where(timed_out, torch.ones_like(resets), resets)
# Apply penalty for not reaching the goal
if max_consecutive_successes > 0:
reward = torch.where(timed_out, reward + 0.5 * fall_penalty, reward)
num_resets = torch.sum(resets)
finished_cons_successes = torch.sum(successes * resets.float())
cons_successes = torch.where(num_resets > 0, av_factor*finished_cons_successes/num_resets + (1.0 - av_factor)*consecutive_successes, consecutive_successes)
return reward, resets, goal_resets, progress_buf, successes, cons_successes
@torch.jit.script
def randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor):
return quat_mul(quat_from_angle_axis(rand0 * np.pi, x_unit_tensor),
quat_from_angle_axis(rand1 * np.pi, y_unit_tensor))
@torch.jit.script
def randomize_rotation_pen(rand0, rand1, max_angle, x_unit_tensor, y_unit_tensor, z_unit_tensor):
rot = quat_mul(quat_from_angle_axis(0.5 * np.pi + rand0 * max_angle, x_unit_tensor),
quat_from_angle_axis(rand0 * np.pi, z_unit_tensor))
return rot
| 40,972 | Python | 54.897681 | 223 | 0.622157 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/ball_balance.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import os
import torch
import xml.etree.ElementTree as ET
from isaacgym import gymutil, gymtorch, gymapi
from isaacgymenvs.utils.torch_jit_utils import to_torch, torch_rand_float, tensor_clamp, torch_random_dir_2
from .base.vec_task import VecTask
def _indent_xml(elem, level=0):
i = "\n" + level * " "
if len(elem):
if not elem.text or not elem.text.strip():
elem.text = i + " "
if not elem.tail or not elem.tail.strip():
elem.tail = i
for elem in elem:
_indent_xml(elem, level + 1)
if not elem.tail or not elem.tail.strip():
elem.tail = i
else:
if level and (not elem.tail or not elem.tail.strip()):
elem.tail = i
class BallBalance(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.max_episode_length = self.cfg["env"]["maxEpisodeLength"]
self.action_speed_scale = self.cfg["env"]["actionSpeedScale"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
sensors_per_env = 3
actors_per_env = 2
dofs_per_env = 6
bodies_per_env = 7 + 1
# Observations:
# 0:3 - activated DOF positions
# 3:6 - activated DOF velocities
# 6:9 - ball position
# 9:12 - ball linear velocity
# 12:15 - sensor force (same for each sensor)
# 15:18 - sensor torque 1
# 18:21 - sensor torque 2
# 21:24 - sensor torque 3
self.cfg["env"]["numObservations"] = 24
# Actions: target velocities for the 3 actuated DOFs
self.cfg["env"]["numActions"] = 3
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
self.root_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
self.dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
self.sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
vec_root_tensor = gymtorch.wrap_tensor(self.root_tensor).view(self.num_envs, actors_per_env, 13)
vec_dof_tensor = gymtorch.wrap_tensor(self.dof_state_tensor).view(self.num_envs, dofs_per_env, 2)
vec_sensor_tensor = gymtorch.wrap_tensor(self.sensor_tensor).view(self.num_envs, sensors_per_env, 6)
self.root_states = vec_root_tensor
self.tray_positions = vec_root_tensor[..., 0, 0:3]
self.ball_positions = vec_root_tensor[..., 1, 0:3]
self.ball_orientations = vec_root_tensor[..., 1, 3:7]
self.ball_linvels = vec_root_tensor[..., 1, 7:10]
self.ball_angvels = vec_root_tensor[..., 1, 10:13]
self.dof_states = vec_dof_tensor
self.dof_positions = vec_dof_tensor[..., 0]
self.dof_velocities = vec_dof_tensor[..., 1]
self.sensor_forces = vec_sensor_tensor[..., 0:3]
self.sensor_torques = vec_sensor_tensor[..., 3:6]
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.initial_dof_states = self.dof_states.clone()
self.initial_root_states = vec_root_tensor.clone()
self.dof_position_targets = torch.zeros((self.num_envs, dofs_per_env), dtype=torch.float32, device=self.device, requires_grad=False)
self.all_actor_indices = torch.arange(actors_per_env * self.num_envs, dtype=torch.int32, device=self.device).view(self.num_envs, actors_per_env)
self.all_bbot_indices = actors_per_env * torch.arange(self.num_envs, dtype=torch.int32, device=self.device)
# vis
self.axes_geom = gymutil.AxesGeometry(0.2)
def create_sim(self):
self.dt = self.sim_params.dt
self.sim_params.up_axis = gymapi.UP_AXIS_Z
self.sim_params.gravity.x = 0
self.sim_params.gravity.y = 0
self.sim_params.gravity.z = -9.81
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_balance_bot_asset()
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
def _create_balance_bot_asset(self):
# there is an asset balance_bot.xml, here we override some features.
tray_radius = 0.5
tray_thickness = 0.02
leg_radius = 0.02
leg_outer_offset = tray_radius - 0.1
leg_length = leg_outer_offset - 2 * leg_radius
leg_inner_offset = leg_outer_offset - leg_length / math.sqrt(2)
tray_height = leg_length * math.sqrt(2) + 2 * leg_radius + 0.5 * tray_thickness
root = ET.Element('mujoco')
root.attrib["model"] = "BalanceBot"
compiler = ET.SubElement(root, "compiler")
compiler.attrib["angle"] = "degree"
compiler.attrib["coordinate"] = "local"
compiler.attrib["inertiafromgeom"] = "true"
worldbody = ET.SubElement(root, "worldbody")
tray = ET.SubElement(worldbody, "body")
tray.attrib["name"] = "tray"
tray.attrib["pos"] = "%g %g %g" % (0, 0, tray_height)
tray_joint = ET.SubElement(tray, "joint")
tray_joint.attrib["name"] = "root_joint"
tray_joint.attrib["type"] = "free"
tray_geom = ET.SubElement(tray, "geom")
tray_geom.attrib["type"] = "cylinder"
tray_geom.attrib["size"] = "%g %g" % (tray_radius, 0.5 * tray_thickness)
tray_geom.attrib["pos"] = "0 0 0"
tray_geom.attrib["density"] = "100"
leg_angles = [0.0, 2.0 / 3.0 * math.pi, 4.0 / 3.0 * math.pi]
for i in range(len(leg_angles)):
angle = leg_angles[i]
upper_leg_from = gymapi.Vec3()
upper_leg_from.x = leg_outer_offset * math.cos(angle)
upper_leg_from.y = leg_outer_offset * math.sin(angle)
upper_leg_from.z = -leg_radius - 0.5 * tray_thickness
upper_leg_to = gymapi.Vec3()
upper_leg_to.x = leg_inner_offset * math.cos(angle)
upper_leg_to.y = leg_inner_offset * math.sin(angle)
upper_leg_to.z = upper_leg_from.z - leg_length / math.sqrt(2)
upper_leg_pos = (upper_leg_from + upper_leg_to) * 0.5
upper_leg_quat = gymapi.Quat.from_euler_zyx(0, -0.75 * math.pi, angle)
upper_leg = ET.SubElement(tray, "body")
upper_leg.attrib["name"] = "upper_leg" + str(i)
upper_leg.attrib["pos"] = "%g %g %g" % (upper_leg_pos.x, upper_leg_pos.y, upper_leg_pos.z)
upper_leg.attrib["quat"] = "%g %g %g %g" % (upper_leg_quat.w, upper_leg_quat.x, upper_leg_quat.y, upper_leg_quat.z)
upper_leg_geom = ET.SubElement(upper_leg, "geom")
upper_leg_geom.attrib["type"] = "capsule"
upper_leg_geom.attrib["size"] = "%g %g" % (leg_radius, 0.5 * leg_length)
upper_leg_geom.attrib["density"] = "1000"
upper_leg_joint = ET.SubElement(upper_leg, "joint")
upper_leg_joint.attrib["name"] = "upper_leg_joint" + str(i)
upper_leg_joint.attrib["type"] = "hinge"
upper_leg_joint.attrib["pos"] = "%g %g %g" % (0, 0, -0.5 * leg_length)
upper_leg_joint.attrib["axis"] = "0 1 0"
upper_leg_joint.attrib["limited"] = "true"
upper_leg_joint.attrib["range"] = "-45 45"
lower_leg_pos = gymapi.Vec3(-0.5 * leg_length, 0, 0.5 * leg_length)
lower_leg_quat = gymapi.Quat.from_euler_zyx(0, -0.5 * math.pi, 0)
lower_leg = ET.SubElement(upper_leg, "body")
lower_leg.attrib["name"] = "lower_leg" + str(i)
lower_leg.attrib["pos"] = "%g %g %g" % (lower_leg_pos.x, lower_leg_pos.y, lower_leg_pos.z)
lower_leg.attrib["quat"] = "%g %g %g %g" % (lower_leg_quat.w, lower_leg_quat.x, lower_leg_quat.y, lower_leg_quat.z)
lower_leg_geom = ET.SubElement(lower_leg, "geom")
lower_leg_geom.attrib["type"] = "capsule"
lower_leg_geom.attrib["size"] = "%g %g" % (leg_radius, 0.5 * leg_length)
lower_leg_geom.attrib["density"] = "1000"
lower_leg_joint = ET.SubElement(lower_leg, "joint")
lower_leg_joint.attrib["name"] = "lower_leg_joint" + str(i)
lower_leg_joint.attrib["type"] = "hinge"
lower_leg_joint.attrib["pos"] = "%g %g %g" % (0, 0, -0.5 * leg_length)
lower_leg_joint.attrib["axis"] = "0 1 0"
lower_leg_joint.attrib["limited"] = "true"
lower_leg_joint.attrib["range"] = "-70 90"
_indent_xml(root)
ET.ElementTree(root).write("balance_bot.xml")
# save some useful robot parameters
self.tray_height = tray_height
self.leg_radius = leg_radius
self.leg_length = leg_length
self.leg_outer_offset = leg_outer_offset
self.leg_angles = leg_angles
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = "."
asset_file = "balance_bot.xml"
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
bbot_options = gymapi.AssetOptions()
bbot_options.fix_base_link = False
bbot_options.slices_per_cylinder = 40
bbot_asset = self.gym.load_asset(self.sim, asset_root, asset_file, bbot_options)
# printed view of asset built
# self.gym.debug_print_asset(bbot_asset)
self.num_bbot_dofs = self.gym.get_asset_dof_count(bbot_asset)
bbot_dof_props = self.gym.get_asset_dof_properties(bbot_asset)
self.bbot_dof_lower_limits = []
self.bbot_dof_upper_limits = []
for i in range(self.num_bbot_dofs):
self.bbot_dof_lower_limits.append(bbot_dof_props['lower'][i])
self.bbot_dof_upper_limits.append(bbot_dof_props['upper'][i])
self.bbot_dof_lower_limits = to_torch(self.bbot_dof_lower_limits, device=self.device)
self.bbot_dof_upper_limits = to_torch(self.bbot_dof_upper_limits, device=self.device)
bbot_pose = gymapi.Transform()
bbot_pose.p.z = self.tray_height
# create force sensors attached to the tray body
bbot_tray_idx = self.gym.find_asset_rigid_body_index(bbot_asset, "tray")
for angle in self.leg_angles:
sensor_pose = gymapi.Transform()
sensor_pose.p.x = self.leg_outer_offset * math.cos(angle)
sensor_pose.p.y = self.leg_outer_offset * math.sin(angle)
self.gym.create_asset_force_sensor(bbot_asset, bbot_tray_idx, sensor_pose)
# create ball asset
self.ball_radius = 0.1
ball_options = gymapi.AssetOptions()
ball_options.density = 200
ball_asset = self.gym.create_sphere(self.sim, self.ball_radius, ball_options)
self.envs = []
self.bbot_handles = []
self.obj_handles = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
bbot_handle = self.gym.create_actor(env_ptr, bbot_asset, bbot_pose, "bbot", i, 0, 0)
actuated_dofs = np.array([1, 3, 5])
free_dofs = np.array([0, 2, 4])
dof_props = self.gym.get_actor_dof_properties(env_ptr, bbot_handle)
dof_props['driveMode'][actuated_dofs] = gymapi.DOF_MODE_POS
dof_props['stiffness'][actuated_dofs] = 4000.0
dof_props['damping'][actuated_dofs] = 100.0
dof_props['driveMode'][free_dofs] = gymapi.DOF_MODE_NONE
dof_props['stiffness'][free_dofs] = 0
dof_props['damping'][free_dofs] = 0
self.gym.set_actor_dof_properties(env_ptr, bbot_handle, dof_props)
lower_leg_handles = []
lower_leg_handles.append(self.gym.find_actor_rigid_body_handle(env_ptr, bbot_handle, "lower_leg0"))
lower_leg_handles.append(self.gym.find_actor_rigid_body_handle(env_ptr, bbot_handle, "lower_leg1"))
lower_leg_handles.append(self.gym.find_actor_rigid_body_handle(env_ptr, bbot_handle, "lower_leg2"))
# create attractors to hold the feet in place
attractor_props = gymapi.AttractorProperties()
attractor_props.stiffness = 5e7
attractor_props.damping = 5e3
attractor_props.axes = gymapi.AXIS_TRANSLATION
for j in range(3):
angle = self.leg_angles[j]
attractor_props.rigid_handle = lower_leg_handles[j]
# attractor world pose to keep the feet in place
attractor_props.target.p.x = self.leg_outer_offset * math.cos(angle)
attractor_props.target.p.z = self.leg_radius
attractor_props.target.p.y = self.leg_outer_offset * math.sin(angle)
# attractor local pose in lower leg body
attractor_props.offset.p.z = 0.5 * self.leg_length
self.gym.create_rigid_body_attractor(env_ptr, attractor_props)
ball_pose = gymapi.Transform()
ball_pose.p.x = 0.2
ball_pose.p.z = 2.0
ball_handle = self.gym.create_actor(env_ptr, ball_asset, ball_pose, "ball", i, 0, 0)
self.obj_handles.append(ball_handle)
# pretty colors
self.gym.set_rigid_body_color(env_ptr, ball_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.99, 0.66, 0.25))
self.gym.set_rigid_body_color(env_ptr, bbot_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.48, 0.65, 0.8))
for j in range(1, 7):
self.gym.set_rigid_body_color(env_ptr, bbot_handle, j, gymapi.MESH_VISUAL, gymapi.Vec3(0.15, 0.2, 0.3))
self.envs.append(env_ptr)
self.bbot_handles.append(bbot_handle)
def compute_observations(self):
#print("~!~!~!~! Computing obs")
actuated_dof_indices = torch.tensor([1, 3, 5], device=self.device)
#print(self.dof_states[:, actuated_dof_indices, :])
self.obs_buf[..., 0:3] = self.dof_positions[..., actuated_dof_indices]
self.obs_buf[..., 3:6] = self.dof_velocities[..., actuated_dof_indices]
self.obs_buf[..., 6:9] = self.ball_positions
self.obs_buf[..., 9:12] = self.ball_linvels
self.obs_buf[..., 12:15] = self.sensor_forces[..., 0] / 20 # !!! lousy normalization
self.obs_buf[..., 15:18] = self.sensor_torques[..., 0] / 20 # !!! lousy normalization
self.obs_buf[..., 18:21] = self.sensor_torques[..., 1] / 20 # !!! lousy normalization
self.obs_buf[..., 21:24] = self.sensor_torques[..., 2] / 20 # !!! lousy normalization
return self.obs_buf
def compute_reward(self):
self.rew_buf[:], self.reset_buf[:] = compute_bbot_reward(
self.tray_positions,
self.ball_positions,
self.ball_linvels,
self.ball_radius,
self.reset_buf, self.progress_buf, self.max_episode_length
)
def reset_idx(self, env_ids):
num_resets = len(env_ids)
# reset bbot and ball root states
self.root_states[env_ids] = self.initial_root_states[env_ids]
min_d = 0.001 # min horizontal dist from origin
max_d = 0.5 # max horizontal dist from origin
min_height = 1.0
max_height = 2.0
min_horizontal_speed = 0
max_horizontal_speed = 5
dists = torch_rand_float(min_d, max_d, (num_resets, 1), self.device)
dirs = torch_random_dir_2((num_resets, 1), self.device)
hpos = dists * dirs
speedscales = (dists - min_d) / (max_d - min_d)
hspeeds = torch_rand_float(min_horizontal_speed, max_horizontal_speed, (num_resets, 1), self.device)
hvels = -speedscales * hspeeds * dirs
vspeeds = -torch_rand_float(5.0, 5.0, (num_resets, 1), self.device).squeeze()
self.ball_positions[env_ids, 0] = hpos[..., 0]
self.ball_positions[env_ids, 2] = torch_rand_float(min_height, max_height, (num_resets, 1), self.device).squeeze()
self.ball_positions[env_ids, 1] = hpos[..., 1]
self.ball_orientations[env_ids, 0:3] = 0
self.ball_orientations[env_ids, 3] = 1
self.ball_linvels[env_ids, 0] = hvels[..., 0]
self.ball_linvels[env_ids, 2] = vspeeds
self.ball_linvels[env_ids, 1] = hvels[..., 1]
self.ball_angvels[env_ids] = 0
# reset root state for bbots and balls in selected envs
actor_indices = self.all_actor_indices[env_ids].flatten()
self.gym.set_actor_root_state_tensor_indexed(self.sim, self.root_tensor, gymtorch.unwrap_tensor(actor_indices), len(actor_indices))
# reset DOF states for bbots in selected envs
bbot_indices = self.all_bbot_indices[env_ids].flatten()
self.dof_states[env_ids] = self.initial_dof_states[env_ids]
self.gym.set_dof_state_tensor_indexed(self.sim, self.dof_state_tensor, gymtorch.unwrap_tensor(bbot_indices), len(bbot_indices))
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def pre_physics_step(self, _actions):
# resets
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
actions = _actions.to(self.device)
actuated_indices = torch.LongTensor([1, 3, 5])
# update position targets from actions
self.dof_position_targets[..., actuated_indices] += self.dt * self.action_speed_scale * actions
self.dof_position_targets[:] = tensor_clamp(self.dof_position_targets, self.bbot_dof_lower_limits, self.bbot_dof_upper_limits)
# reset position targets for reset envs
self.dof_position_targets[reset_env_ids] = 0
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(self.dof_position_targets))
def post_physics_step(self):
self.progress_buf += 1
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_force_sensor_tensor(self.sim)
self.compute_observations()
self.compute_reward()
# vis
if self.viewer and self.debug_viz:
self.gym.clear_lines(self.viewer)
for i in range(self.num_envs):
env = self.envs[i]
bbot_handle = self.bbot_handles[i]
body_handles = []
body_handles.append(self.gym.find_actor_rigid_body_handle(env, bbot_handle, "upper_leg0"))
body_handles.append(self.gym.find_actor_rigid_body_handle(env, bbot_handle, "upper_leg1"))
body_handles.append(self.gym.find_actor_rigid_body_handle(env, bbot_handle, "upper_leg2"))
for lhandle in body_handles:
lpose = self.gym.get_rigid_transform(env, lhandle)
gymutil.draw_lines(self.axes_geom, self.gym, self.viewer, env, lpose)
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_bbot_reward(tray_positions, ball_positions, ball_velocities, ball_radius, reset_buf, progress_buf, max_episode_length):
# type: (Tensor, Tensor, Tensor, float, Tensor, Tensor, float) -> Tuple[Tensor, Tensor]
# calculating the norm for ball distance to desired height above the ground plane (i.e. 0.7)
ball_dist = torch.sqrt(ball_positions[..., 0] * ball_positions[..., 0] +
(ball_positions[..., 2] - 0.7) * (ball_positions[..., 2] - 0.7) +
(ball_positions[..., 1]) * ball_positions[..., 1])
ball_speed = torch.sqrt(ball_velocities[..., 0] * ball_velocities[..., 0] +
ball_velocities[..., 1] * ball_velocities[..., 1] +
ball_velocities[..., 2] * ball_velocities[..., 2])
pos_reward = 1.0 / (1.0 + ball_dist)
speed_reward = 1.0 / (1.0 + ball_speed)
reward = pos_reward * speed_reward
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset_buf)
reset = torch.where(ball_positions[..., 2] < ball_radius * 1.5, torch.ones_like(reset_buf), reset)
return reward, reset
| 22,414 | Python | 45.991614 | 217 | 0.605559 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/anymal_terrain.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os, time
from isaacgym import gymtorch
from isaacgym import gymapi
from .base.vec_task import VecTask
import torch
from typing import Tuple, Dict
from isaacgymenvs.utils.torch_jit_utils import to_torch, get_axis_params, torch_rand_float, normalize, quat_apply, quat_rotate_inverse
from isaacgymenvs.tasks.base.vec_task import VecTask
import onnxruntime as ort
class AnymalTerrain(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.height_samples = None
self.custom_origins = False
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.init_done = False
self.ort_model = ort.InferenceSession("AnymalTerrain2.onnx")
# normalization
self.lin_vel_scale = self.cfg["env"]["learn"]["linearVelocityScale"]
self.ang_vel_scale = self.cfg["env"]["learn"]["angularVelocityScale"]
self.dof_pos_scale = self.cfg["env"]["learn"]["dofPositionScale"]
self.dof_vel_scale = self.cfg["env"]["learn"]["dofVelocityScale"]
self.height_meas_scale = self.cfg["env"]["learn"]["heightMeasurementScale"]
self.action_scale = self.cfg["env"]["control"]["actionScale"]
# reward scales
self.rew_scales = {}
self.rew_scales["termination"] = self.cfg["env"]["learn"]["terminalReward"]
self.rew_scales["lin_vel_xy"] = self.cfg["env"]["learn"]["linearVelocityXYRewardScale"]
self.rew_scales["lin_vel_z"] = self.cfg["env"]["learn"]["linearVelocityZRewardScale"]
self.rew_scales["ang_vel_z"] = self.cfg["env"]["learn"]["angularVelocityZRewardScale"]
self.rew_scales["ang_vel_xy"] = self.cfg["env"]["learn"]["angularVelocityXYRewardScale"]
self.rew_scales["orient"] = self.cfg["env"]["learn"]["orientationRewardScale"]
self.rew_scales["torque"] = self.cfg["env"]["learn"]["torqueRewardScale"]
self.rew_scales["joint_acc"] = self.cfg["env"]["learn"]["jointAccRewardScale"]
self.rew_scales["base_height"] = self.cfg["env"]["learn"]["baseHeightRewardScale"]
self.rew_scales["air_time"] = self.cfg["env"]["learn"]["feetAirTimeRewardScale"]
self.rew_scales["collision"] = self.cfg["env"]["learn"]["kneeCollisionRewardScale"]
self.rew_scales["stumble"] = self.cfg["env"]["learn"]["feetStumbleRewardScale"]
self.rew_scales["action_rate"] = self.cfg["env"]["learn"]["actionRateRewardScale"]
self.rew_scales["hip"] = self.cfg["env"]["learn"]["hipRewardScale"]
#command ranges
self.command_x_range = self.cfg["env"]["randomCommandVelocityRanges"]["linear_x"]
self.command_y_range = self.cfg["env"]["randomCommandVelocityRanges"]["linear_y"]
self.command_yaw_range = self.cfg["env"]["randomCommandVelocityRanges"]["yaw"]
# base init state
pos = self.cfg["env"]["baseInitState"]["pos"]
rot = self.cfg["env"]["baseInitState"]["rot"]
v_lin = self.cfg["env"]["baseInitState"]["vLinear"]
v_ang = self.cfg["env"]["baseInitState"]["vAngular"]
self.base_init_state = pos + rot + v_lin + v_ang
# default joint positions
self.named_default_joint_angles = self.cfg["env"]["defaultJointAngles"]
# other
self.decimation = self.cfg["env"]["control"]["decimation"]
self.dt = self.decimation * self.cfg["sim"]["dt"]
self.max_episode_length_s = self.cfg["env"]["learn"]["episodeLength_s"]
self.max_episode_length = int(self.max_episode_length_s/ self.dt + 0.5)
self.push_interval = int(self.cfg["env"]["learn"]["pushInterval_s"] / self.dt + 0.5)
self.allow_knee_contacts = self.cfg["env"]["learn"]["allowKneeContacts"]
self.Kp = self.cfg["env"]["control"]["stiffness"]
self.Kd = self.cfg["env"]["control"]["damping"]
self.curriculum = self.cfg["env"]["terrain"]["curriculum"]
for key in self.rew_scales.keys():
self.rew_scales[key] *= self.dt
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
if self.graphics_device_id != -1:
p = self.cfg["env"]["viewer"]["pos"]
lookat = self.cfg["env"]["viewer"]["lookat"]
cam_pos = gymapi.Vec3(p[0], p[1], p[2])
cam_target = gymapi.Vec3(lookat[0], lookat[1], lookat[2])
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym GPU state tensors
actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
net_contact_forces = self.gym.acquire_net_contact_force_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
# create some wrapper tensors for different slices
self.root_states = gymtorch.wrap_tensor(actor_root_state)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
self.contact_forces = gymtorch.wrap_tensor(net_contact_forces).view(self.num_envs, -1, 3) # shape: num_envs, num_bodies, xyz axis
# initialize some data used later on
self.common_step_counter = 0
self.extras = {}
self.noise_scale_vec = self._get_noise_scale_vec(self.cfg)
self.commands = torch.zeros(self.num_envs, 4, dtype=torch.float, device=self.device, requires_grad=False) # x vel, y vel, yaw vel, heading
self.commands_scale = torch.tensor([self.lin_vel_scale, self.lin_vel_scale, self.ang_vel_scale], device=self.device, requires_grad=False,)
self.gravity_vec = to_torch(get_axis_params(-1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
self.forward_vec = to_torch([1., 0., 0.], device=self.device).repeat((self.num_envs, 1))
self.torques = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.last_actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.feet_air_time = torch.zeros(self.num_envs, 4, dtype=torch.float, device=self.device, requires_grad=False)
self.last_dof_vel = torch.zeros_like(self.dof_vel)
self.height_points = self.init_height_points()
self.measured_heights = None
# joint positions offsets
self.default_dof_pos = torch.zeros_like(self.dof_pos, dtype=torch.float, device=self.device, requires_grad=False)
for i in range(self.num_actions):
name = self.dof_names[i]
angle = self.named_default_joint_angles[name]
self.default_dof_pos[:, i] = angle
# reward episode sums
torch_zeros = lambda : torch.zeros(self.num_envs, dtype=torch.float, device=self.device, requires_grad=False)
self.episode_sums = {"lin_vel_xy": torch_zeros(), "lin_vel_z": torch_zeros(), "ang_vel_z": torch_zeros(), "ang_vel_xy": torch_zeros(),
"orient": torch_zeros(), "torques": torch_zeros(), "joint_acc": torch_zeros(), "base_height": torch_zeros(),
"air_time": torch_zeros(), "collision": torch_zeros(), "stumble": torch_zeros(), "action_rate": torch_zeros(), "hip": torch_zeros()}
self.reset_idx(torch.arange(self.num_envs, device=self.device))
self.init_done = True
def create_sim(self):
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
terrain_type = self.cfg["env"]["terrain"]["terrainType"]
if terrain_type=='plane':
self._create_ground_plane()
elif terrain_type=='trimesh':
self._create_trimesh()
self.custom_origins = True
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
def _get_noise_scale_vec(self, cfg):
noise_vec = torch.zeros_like(self.obs_buf[0])
self.add_noise = self.cfg["env"]["learn"]["addNoise"]
noise_level = self.cfg["env"]["learn"]["noiseLevel"]
noise_vec[:3] = self.cfg["env"]["learn"]["linearVelocityNoise"] * noise_level * self.lin_vel_scale
noise_vec[3:6] = self.cfg["env"]["learn"]["angularVelocityNoise"] * noise_level * self.ang_vel_scale
noise_vec[6:9] = self.cfg["env"]["learn"]["gravityNoise"] * noise_level
noise_vec[9:12] = 0. # commands
noise_vec[12:24] = self.cfg["env"]["learn"]["dofPositionNoise"] * noise_level * self.dof_pos_scale
noise_vec[24:36] = self.cfg["env"]["learn"]["dofVelocityNoise"] * noise_level * self.dof_vel_scale
noise_vec[36:176] = self.cfg["env"]["learn"]["heightMeasurementNoise"] * noise_level * self.height_meas_scale
noise_vec[176:188] = 0. # previous actions
return noise_vec
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
plane_params.static_friction = self.cfg["env"]["terrain"]["staticFriction"]
plane_params.dynamic_friction = self.cfg["env"]["terrain"]["dynamicFriction"]
plane_params.restitution = self.cfg["env"]["terrain"]["restitution"]
self.gym.add_ground(self.sim, plane_params)
def _create_trimesh(self):
self.terrain = Terrain(self.cfg["env"]["terrain"], num_robots=self.num_envs)
tm_params = gymapi.TriangleMeshParams()
tm_params.nb_vertices = self.terrain.vertices.shape[0]
tm_params.nb_triangles = self.terrain.triangles.shape[0]
tm_params.transform.p.x = -self.terrain.border_size
tm_params.transform.p.y = -self.terrain.border_size
tm_params.transform.p.z = 0.0
tm_params.static_friction = self.cfg["env"]["terrain"]["staticFriction"]
tm_params.dynamic_friction = self.cfg["env"]["terrain"]["dynamicFriction"]
tm_params.restitution = self.cfg["env"]["terrain"]["restitution"]
self.gym.add_triangle_mesh(self.sim, self.terrain.vertices.flatten(order='C'), self.terrain.triangles.flatten(order='C'), tm_params)
self.height_samples = torch.tensor(self.terrain.heightsamples).view(self.terrain.tot_rows, self.terrain.tot_cols).to(self.device)
def _create_envs(self, num_envs, spacing, num_per_row):
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../assets')
asset_file = self.cfg["env"]["urdfAsset"]["file"]
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
asset_options = gymapi.AssetOptions()
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_EFFORT
asset_options.collapse_fixed_joints = True
asset_options.replace_cylinder_with_capsule = True
asset_options.flip_visual_attachments = True
asset_options.fix_base_link = self.cfg["env"]["urdfAsset"]["fixBaseLink"]
asset_options.density = 0.001
asset_options.angular_damping = 0.0
asset_options.linear_damping = 0.0
asset_options.armature = 0.0
asset_options.thickness = 0.01
asset_options.disable_gravity = False
anymal_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dof = self.gym.get_asset_dof_count(anymal_asset)
self.num_bodies = self.gym.get_asset_rigid_body_count(anymal_asset)
# prepare friction randomization
rigid_shape_prop = self.gym.get_asset_rigid_shape_properties(anymal_asset)
friction_range = self.cfg["env"]["learn"]["frictionRange"]
num_buckets = 100
friction_buckets = torch_rand_float(friction_range[0], friction_range[1], (num_buckets,1), device=self.device)
self.base_init_state = to_torch(self.base_init_state, device=self.device, requires_grad=False)
start_pose = gymapi.Transform()
start_pose.p = gymapi.Vec3(*self.base_init_state[:3])
body_names = self.gym.get_asset_rigid_body_names(anymal_asset)
self.dof_names = self.gym.get_asset_dof_names(anymal_asset)
foot_name = self.cfg["env"]["urdfAsset"]["footName"]
knee_name = self.cfg["env"]["urdfAsset"]["kneeName"]
feet_names = [s for s in body_names if foot_name in s]
self.feet_indices = torch.zeros(len(feet_names), dtype=torch.long, device=self.device, requires_grad=False)
knee_names = [s for s in body_names if knee_name in s]
self.knee_indices = torch.zeros(len(knee_names), dtype=torch.long, device=self.device, requires_grad=False)
self.base_index = 0
dof_props = self.gym.get_asset_dof_properties(anymal_asset)
# env origins
self.env_origins = torch.zeros(self.num_envs, 3, device=self.device, requires_grad=False)
if not self.curriculum: self.cfg["env"]["terrain"]["maxInitMapLevel"] = self.cfg["env"]["terrain"]["numLevels"] - 1
self.terrain_levels = torch.randint(0, self.cfg["env"]["terrain"]["maxInitMapLevel"]+1, (self.num_envs,), device=self.device)
self.terrain_types = torch.randint(0, self.cfg["env"]["terrain"]["numTerrains"], (self.num_envs,), device=self.device)
if self.custom_origins:
self.terrain_origins = torch.from_numpy(self.terrain.env_origins).to(self.device).to(torch.float)
spacing = 0.
env_lower = gymapi.Vec3(-spacing, -spacing, 0.0)
env_upper = gymapi.Vec3(spacing, spacing, spacing)
self.anymal_handles = []
self.envs = []
for i in range(self.num_envs):
# create env instance
env_handle = self.gym.create_env(self.sim, env_lower, env_upper, num_per_row)
if self.custom_origins:
self.env_origins[i] = self.terrain_origins[self.terrain_levels[i], self.terrain_types[i]]
pos = self.env_origins[i].clone()
pos[:2] += torch_rand_float(-1., 1., (2, 1), device=self.device).squeeze(1)
start_pose.p = gymapi.Vec3(*pos)
for s in range(len(rigid_shape_prop)):
rigid_shape_prop[s].friction = friction_buckets[i % num_buckets]
self.gym.set_asset_rigid_shape_properties(anymal_asset, rigid_shape_prop)
anymal_handle = self.gym.create_actor(env_handle, anymal_asset, start_pose, "anymal", i, 0, 0)
self.gym.set_actor_dof_properties(env_handle, anymal_handle, dof_props)
self.envs.append(env_handle)
self.anymal_handles.append(anymal_handle)
for i in range(len(feet_names)):
self.feet_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.anymal_handles[0], feet_names[i])
for i in range(len(knee_names)):
self.knee_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.anymal_handles[0], knee_names[i])
self.base_index = self.gym.find_actor_rigid_body_handle(self.envs[0], self.anymal_handles[0], "base")
def check_termination(self):
self.reset_buf = torch.norm(self.contact_forces[:, self.base_index, :], dim=1) > 1.
if not self.allow_knee_contacts:
knee_contact = torch.norm(self.contact_forces[:, self.knee_indices, :], dim=2) > 1.
self.reset_buf |= torch.any(knee_contact, dim=1)
self.reset_buf = torch.where(self.progress_buf >= self.max_episode_length - 1, torch.ones_like(self.reset_buf), self.reset_buf)
def compute_observations(self):
self.measured_heights = self.get_heights()
heights = torch.clip(self.root_states[:, 2].unsqueeze(1) - 0.5 - self.measured_heights, -1, 1.) * self.height_meas_scale
self.obs_buf = torch.cat(( self.base_lin_vel * self.lin_vel_scale,
self.base_ang_vel * self.ang_vel_scale,
self.projected_gravity,
self.commands[:, :3] * self.commands_scale,
self.dof_pos * self.dof_pos_scale,
self.dof_vel * self.dof_vel_scale,
heights,
self.actions
), dim=-1)
def compute_reward(self):
# velocity tracking reward
lin_vel_error = torch.sum(torch.square(self.commands[:, :2] - self.base_lin_vel[:, :2]), dim=1)
ang_vel_error = torch.square(self.commands[:, 2] - self.base_ang_vel[:, 2])
rew_lin_vel_xy = torch.exp(-lin_vel_error/0.25) * self.rew_scales["lin_vel_xy"]
rew_ang_vel_z = torch.exp(-ang_vel_error/0.25) * self.rew_scales["ang_vel_z"]
# other base velocity penalties
rew_lin_vel_z = torch.square(self.base_lin_vel[:, 2]) * self.rew_scales["lin_vel_z"]
rew_ang_vel_xy = torch.sum(torch.square(self.base_ang_vel[:, :2]), dim=1) * self.rew_scales["ang_vel_xy"]
# orientation penalty
rew_orient = torch.sum(torch.square(self.projected_gravity[:, :2]), dim=1) * self.rew_scales["orient"]
# base height penalty
rew_base_height = torch.square(self.root_states[:, 2] - 0.52) * self.rew_scales["base_height"] # TODO add target base height to cfg
# torque penalty
rew_torque = torch.sum(torch.square(self.torques), dim=1) * self.rew_scales["torque"]
# joint acc penalty
rew_joint_acc = torch.sum(torch.square(self.last_dof_vel - self.dof_vel), dim=1) * self.rew_scales["joint_acc"]
# collision penalty
knee_contact = torch.norm(self.contact_forces[:, self.knee_indices, :], dim=2) > 1.
rew_collision = torch.sum(knee_contact, dim=1) * self.rew_scales["collision"] # sum vs any ?
# stumbling penalty
stumble = (torch.norm(self.contact_forces[:, self.feet_indices, :2], dim=2) > 5.) * (torch.abs(self.contact_forces[:, self.feet_indices, 2]) < 1.)
rew_stumble = torch.sum(stumble, dim=1) * self.rew_scales["stumble"]
# action rate penalty
rew_action_rate = torch.sum(torch.square(self.last_actions - self.actions), dim=1) * self.rew_scales["action_rate"]
# air time reward
# contact = torch.norm(contact_forces[:, feet_indices, :], dim=2) > 1.
contact = self.contact_forces[:, self.feet_indices, 2] > 1.
first_contact = (self.feet_air_time > 0.) * contact
self.feet_air_time += self.dt
rew_airTime = torch.sum((self.feet_air_time - 0.5) * first_contact, dim=1) * self.rew_scales["air_time"] # reward only on first contact with the ground
rew_airTime *= torch.norm(self.commands[:, :2], dim=1) > 0.1 #no reward for zero command
self.feet_air_time *= ~contact
# cosmetic penalty for hip motion
rew_hip = torch.sum(torch.abs(self.dof_pos[:, [0, 3, 6, 9]] - self.default_dof_pos[:, [0, 3, 6, 9]]), dim=1)* self.rew_scales["hip"]
# total reward
self.rew_buf = rew_lin_vel_xy + rew_ang_vel_z + rew_lin_vel_z + rew_ang_vel_xy + rew_orient + rew_base_height +\
rew_torque + rew_joint_acc + rew_collision + rew_action_rate + rew_airTime + rew_hip + rew_stumble
self.rew_buf = torch.clip(self.rew_buf, min=0., max=None)
# add termination reward
self.rew_buf += self.rew_scales["termination"] * self.reset_buf * ~self.timeout_buf
# log episode reward sums
self.episode_sums["lin_vel_xy"] += rew_lin_vel_xy
self.episode_sums["ang_vel_z"] += rew_ang_vel_z
self.episode_sums["lin_vel_z"] += rew_lin_vel_z
self.episode_sums["ang_vel_xy"] += rew_ang_vel_xy
self.episode_sums["orient"] += rew_orient
self.episode_sums["torques"] += rew_torque
self.episode_sums["joint_acc"] += rew_joint_acc
self.episode_sums["collision"] += rew_collision
self.episode_sums["stumble"] += rew_stumble
self.episode_sums["action_rate"] += rew_action_rate
self.episode_sums["air_time"] += rew_airTime
self.episode_sums["base_height"] += rew_base_height
self.episode_sums["hip"] += rew_hip
def reset_idx(self, env_ids):
positions_offset = torch_rand_float(0.5, 1.5, (len(env_ids), self.num_dof), device=self.device)
velocities = torch_rand_float(-0.1, 0.1, (len(env_ids), self.num_dof), device=self.device)
self.dof_pos[env_ids] = self.default_dof_pos[env_ids] * positions_offset
self.dof_vel[env_ids] = velocities
env_ids_int32 = env_ids.to(dtype=torch.int32)
if self.custom_origins:
self.update_terrain_level(env_ids)
self.root_states[env_ids] = self.base_init_state
self.root_states[env_ids, :3] += self.env_origins[env_ids]
self.root_states[env_ids, :2] += torch_rand_float(-0.5, 0.5, (len(env_ids), 2), device=self.device)
else:
self.root_states[env_ids] = self.base_init_state
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.commands[env_ids, 0] = torch_rand_float(self.command_x_range[0], self.command_x_range[1], (len(env_ids), 1), device=self.device).squeeze()
self.commands[env_ids, 1] = torch_rand_float(self.command_y_range[0], self.command_y_range[1], (len(env_ids), 1), device=self.device).squeeze()
self.commands[env_ids, 3] = torch_rand_float(self.command_yaw_range[0], self.command_yaw_range[1], (len(env_ids), 1), device=self.device).squeeze()
self.commands[env_ids] *= (torch.norm(self.commands[env_ids, :2], dim=1) > 0.25).unsqueeze(1) # set small commands to zero
self.last_actions[env_ids] = 0.
self.last_dof_vel[env_ids] = 0.
self.feet_air_time[env_ids] = 0.
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 1
# fill extras
self.extras["episode"] = {}
for key in self.episode_sums.keys():
self.extras["episode"]['rew_' + key] = torch.mean(self.episode_sums[key][env_ids]) / self.max_episode_length_s
self.episode_sums[key][env_ids] = 0.
self.extras["episode"]["terrain_level"] = torch.mean(self.terrain_levels.float())
def update_terrain_level(self, env_ids):
if not self.init_done or not self.curriculum:
# don't change on initial reset
return
distance = torch.norm(self.root_states[env_ids, :2] - self.env_origins[env_ids, :2], dim=1)
self.terrain_levels[env_ids] -= 1 * (distance < torch.norm(self.commands[env_ids, :2])*self.max_episode_length_s*0.25)
self.terrain_levels[env_ids] += 1 * (distance > self.terrain.env_length / 2)
self.terrain_levels[env_ids] = torch.clip(self.terrain_levels[env_ids], 0) % self.terrain.env_rows
self.env_origins[env_ids] = self.terrain_origins[self.terrain_levels[env_ids], self.terrain_types[env_ids]]
def push_robots(self):
self.root_states[:, 7:9] = torch_rand_float(-1., 1., (self.num_envs, 2), device=self.device) # lin vel x/y
self.gym.set_actor_root_state_tensor(self.sim, gymtorch.unwrap_tensor(self.root_states))
def pre_physics_step(self, actions):
self.actions = actions.clone().to(self.device)
# Replace Env0 Actions with the ort model actions
output = self.ort_model.run(
None,
{"obs": self.obs_buf[0, ...].reshape([1,-1]).detach().cpu().numpy().astype(np.float32)},
)
# print(outputs[0])
self.actions[0, :] = torch.tensor(output[0], device=self.device)
for i in range(self.decimation):
torques = torch.clip(self.Kp*(self.action_scale*self.actions + self.default_dof_pos - self.dof_pos) - self.Kd*self.dof_vel,
-80., 80.)
self.gym.set_dof_actuation_force_tensor(self.sim, gymtorch.unwrap_tensor(torques))
self.torques = torques.view(self.torques.shape)
self.gym.simulate(self.sim)
if self.device == 'cpu':
self.gym.fetch_results(self.sim, True)
self.gym.refresh_dof_state_tensor(self.sim)
def post_physics_step(self):
# self.gym.refresh_dof_state_tensor(self.sim) # done in step
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.progress_buf += 1
self.randomize_buf += 1
self.common_step_counter += 1
if self.common_step_counter % self.push_interval == 0:
self.push_robots()
# prepare quantities
self.base_quat = self.root_states[:, 3:7]
self.base_lin_vel = quat_rotate_inverse(self.base_quat, self.root_states[:, 7:10])
self.base_ang_vel = quat_rotate_inverse(self.base_quat, self.root_states[:, 10:13])
self.projected_gravity = quat_rotate_inverse(self.base_quat, self.gravity_vec)
forward = quat_apply(self.base_quat, self.forward_vec)
heading = torch.atan2(forward[:, 1], forward[:, 0])
self.commands[:, 2] = torch.clip(0.5*wrap_to_pi(self.commands[:, 3] - heading), -1., 1.)
# compute observations, rewards, resets, ...
self.check_termination()
self.compute_reward()
env_ids = self.reset_buf.nonzero(as_tuple=False).flatten()
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
if self.add_noise:
self.obs_buf += (2 * torch.rand_like(self.obs_buf) - 1) * self.noise_scale_vec
self.last_actions[:] = self.actions[:]
self.last_dof_vel[:] = self.dof_vel[:]
if self.viewer and self.enable_viewer_sync and self.debug_viz:
# draw height lines
self.gym.clear_lines(self.viewer)
self.gym.refresh_rigid_body_state_tensor(self.sim)
sphere_geom = gymutil.WireframeSphereGeometry(0.02, 4, 4, None, color=(1, 1, 0))
for i in range(self.num_envs):
base_pos = (self.root_states[i, :3]).cpu().numpy()
heights = self.measured_heights[i].cpu().numpy()
height_points = quat_apply_yaw(self.base_quat[i].repeat(heights.shape[0]), self.height_points[i]).cpu().numpy()
for j in range(heights.shape[0]):
x = height_points[j, 0] + base_pos[0]
y = height_points[j, 1] + base_pos[1]
z = heights[j]
sphere_pose = gymapi.Transform(gymapi.Vec3(x, y, z), r=None)
gymutil.draw_lines(sphere_geom, self.gym, self.viewer, self.envs[i], sphere_pose)
def init_height_points(self):
# 1mx1.6m rectangle (without center line)
y = 0.1 * torch.tensor([-5, -4, -3, -2, -1, 1, 2, 3, 4, 5], device=self.device, requires_grad=False) # 10-50cm on each side
x = 0.1 * torch.tensor([-8, -7, -6, -5, -4, -3, -2, 2, 3, 4, 5, 6, 7, 8], device=self.device, requires_grad=False) # 20-80cm on each side
grid_x, grid_y = torch.meshgrid(x, y)
self.num_height_points = grid_x.numel()
points = torch.zeros(self.num_envs, self.num_height_points, 3, device=self.device, requires_grad=False)
points[:, :, 0] = grid_x.flatten()
points[:, :, 1] = grid_y.flatten()
return points
def get_heights(self, env_ids=None):
if self.cfg["env"]["terrain"]["terrainType"] == 'plane':
return torch.zeros(self.num_envs, self.num_height_points, device=self.device, requires_grad=False)
elif self.cfg["env"]["terrain"]["terrainType"] == 'none':
raise NameError("Can't measure height with terrain type 'none'")
if env_ids:
points = quat_apply_yaw(self.base_quat[env_ids].repeat(1, self.num_height_points), self.height_points[env_ids]) + (self.root_states[env_ids, :3]).unsqueeze(1)
else:
points = quat_apply_yaw(self.base_quat.repeat(1, self.num_height_points), self.height_points) + (self.root_states[:, :3]).unsqueeze(1)
points += self.terrain.border_size
points = (points/self.terrain.horizontal_scale).long()
px = points[:, :, 0].view(-1)
py = points[:, :, 1].view(-1)
px = torch.clip(px, 0, self.height_samples.shape[0]-2)
py = torch.clip(py, 0, self.height_samples.shape[1]-2)
heights1 = self.height_samples[px, py]
heights2 = self.height_samples[px+1, py+1]
heights = torch.min(heights1, heights2)
return heights.view(self.num_envs, -1) * self.terrain.vertical_scale
# terrain generator
from isaacgym.terrain_utils import *
class Terrain:
def __init__(self, cfg, num_robots) -> None:
self.type = cfg["terrainType"]
if self.type in ["none", 'plane']:
return
self.horizontal_scale = 0.1
self.vertical_scale = 0.005
self.border_size = 20
self.num_per_env = 2
self.env_length = cfg["mapLength"]
self.env_width = cfg["mapWidth"]
self.proportions = [np.sum(cfg["terrainProportions"][:i+1]) for i in range(len(cfg["terrainProportions"]))]
self.env_rows = cfg["numLevels"]
self.env_cols = cfg["numTerrains"]
self.num_maps = self.env_rows * self.env_cols
self.num_per_env = int(num_robots / self.num_maps)
self.env_origins = np.zeros((self.env_rows, self.env_cols, 3))
self.width_per_env_pixels = int(self.env_width / self.horizontal_scale)
self.length_per_env_pixels = int(self.env_length / self.horizontal_scale)
self.border = int(self.border_size/self.horizontal_scale)
self.tot_cols = int(self.env_cols * self.width_per_env_pixels) + 2 * self.border
self.tot_rows = int(self.env_rows * self.length_per_env_pixels) + 2 * self.border
self.height_field_raw = np.zeros((self.tot_rows , self.tot_cols), dtype=np.int16)
if cfg["curriculum"]:
self.curiculum(num_robots, num_terrains=self.env_cols, num_levels=self.env_rows)
else:
self.randomized_terrain()
self.heightsamples = self.height_field_raw
self.vertices, self.triangles = convert_heightfield_to_trimesh(self.height_field_raw, self.horizontal_scale, self.vertical_scale, cfg["slopeTreshold"])
def randomized_terrain(self):
for k in range(self.num_maps):
# Env coordinates in the world
(i, j) = np.unravel_index(k, (self.env_rows, self.env_cols))
# Heightfield coordinate system from now on
start_x = self.border + i * self.length_per_env_pixels
end_x = self.border + (i + 1) * self.length_per_env_pixels
start_y = self.border + j * self.width_per_env_pixels
end_y = self.border + (j + 1) * self.width_per_env_pixels
terrain = SubTerrain("terrain",
width=self.width_per_env_pixels,
length=self.width_per_env_pixels,
vertical_scale=self.vertical_scale,
horizontal_scale=self.horizontal_scale)
choice = np.random.uniform(0, 1)
if choice < 0.1:
if np.random.choice([0, 1]):
pyramid_sloped_terrain(terrain, np.random.choice([-0.3, -0.2, 0, 0.2, 0.3]))
random_uniform_terrain(terrain, min_height=-0.1, max_height=0.1, step=0.05, downsampled_scale=0.2)
else:
pyramid_sloped_terrain(terrain, np.random.choice([-0.3, -0.2, 0, 0.2, 0.3]))
elif choice < 0.6:
# step_height = np.random.choice([-0.18, -0.15, -0.1, -0.05, 0.05, 0.1, 0.15, 0.18])
step_height = np.random.choice([-0.15, 0.15])
pyramid_stairs_terrain(terrain, step_width=0.31, step_height=step_height, platform_size=3.)
elif choice < 1.:
discrete_obstacles_terrain(terrain, 0.15, 1., 2., 40, platform_size=3.)
self.height_field_raw[start_x: end_x, start_y:end_y] = terrain.height_field_raw
env_origin_x = (i + 0.5) * self.env_length
env_origin_y = (j + 0.5) * self.env_width
x1 = int((self.env_length/2. - 1) / self.horizontal_scale)
x2 = int((self.env_length/2. + 1) / self.horizontal_scale)
y1 = int((self.env_width/2. - 1) / self.horizontal_scale)
y2 = int((self.env_width/2. + 1) / self.horizontal_scale)
env_origin_z = np.max(terrain.height_field_raw[x1:x2, y1:y2])*self.vertical_scale
self.env_origins[i, j] = [env_origin_x, env_origin_y, env_origin_z]
def curiculum(self, num_robots, num_terrains, num_levels):
num_robots_per_map = int(num_robots / num_terrains)
left_over = num_robots % num_terrains
idx = 0
for j in range(num_terrains):
for i in range(num_levels):
terrain = SubTerrain("terrain",
width=self.width_per_env_pixels,
length=self.width_per_env_pixels,
vertical_scale=self.vertical_scale,
horizontal_scale=self.horizontal_scale)
difficulty = i / num_levels
choice = j / num_terrains
slope = difficulty * 0.4
step_height = 0.05 + 0.175 * difficulty
discrete_obstacles_height = 0.025 + difficulty * 0.15
stepping_stones_size = 2 - 1.8 * difficulty
if choice < self.proportions[0]:
if choice < 0.05:
slope *= -1
pyramid_sloped_terrain(terrain, slope=slope, platform_size=3.)
elif choice < self.proportions[1]:
if choice < 0.15:
slope *= -1
pyramid_sloped_terrain(terrain, slope=slope, platform_size=3.)
random_uniform_terrain(terrain, min_height=-0.1, max_height=0.1, step=0.025, downsampled_scale=0.2)
elif choice < self.proportions[3]:
if choice<self.proportions[2]:
step_height *= -1
pyramid_stairs_terrain(terrain, step_width=0.31, step_height=step_height, platform_size=3.)
elif choice < self.proportions[4]:
discrete_obstacles_terrain(terrain, discrete_obstacles_height, 1., 2., 40, platform_size=3.)
else:
stepping_stones_terrain(terrain, stone_size=stepping_stones_size, stone_distance=0.1, max_height=0., platform_size=3.)
# Heightfield coordinate system
start_x = self.border + i * self.length_per_env_pixels
end_x = self.border + (i + 1) * self.length_per_env_pixels
start_y = self.border + j * self.width_per_env_pixels
end_y = self.border + (j + 1) * self.width_per_env_pixels
self.height_field_raw[start_x: end_x, start_y:end_y] = terrain.height_field_raw
robots_in_map = num_robots_per_map
if j < left_over:
robots_in_map +=1
env_origin_x = (i + 0.5) * self.env_length
env_origin_y = (j + 0.5) * self.env_width
x1 = int((self.env_length/2. - 1) / self.horizontal_scale)
x2 = int((self.env_length/2. + 1) / self.horizontal_scale)
y1 = int((self.env_width/2. - 1) / self.horizontal_scale)
y2 = int((self.env_width/2. + 1) / self.horizontal_scale)
env_origin_z = np.max(terrain.height_field_raw[x1:x2, y1:y2])*self.vertical_scale
self.env_origins[i, j] = [env_origin_x, env_origin_y, env_origin_z]
@torch.jit.script
def quat_apply_yaw(quat, vec):
quat_yaw = quat.clone().view(-1, 4)
quat_yaw[:, :2] = 0.
quat_yaw = normalize(quat_yaw)
return quat_apply(quat_yaw, vec)
@torch.jit.script
def wrap_to_pi(angles):
angles %= 2*np.pi
angles -= 2*np.pi * (angles > np.pi)
return angles
| 38,697 | Python | 54.125356 | 217 | 0.61059 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/trifinger.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymtorch
from isaacgym import gymapi
from isaacgymenvs.utils.torch_jit_utils import quat_mul
from collections import OrderedDict
project_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
from isaacgymenvs.utils.torch_jit_utils import *
from isaacgymenvs.tasks.base.vec_task import VecTask
from types import SimpleNamespace
from collections import deque
from typing import Deque, Dict, Tuple, Union
# python
import enum
import numpy as np
# ################### #
# Dimensions of robot #
# ################### #
class TrifingerDimensions(enum.Enum):
"""
Dimensions of the tri-finger robot.
Note: While it may not seem necessary for tri-finger robot since it is fixed base, for floating
base systems having this dimensions class is useful.
"""
# general state
# cartesian position + quaternion orientation
PoseDim = 7,
# linear velocity + angular velcoity
VelocityDim = 6
# state: pose + velocity
StateDim = 13
# force + torque
WrenchDim = 6
# for robot
# number of fingers
NumFingers = 3
# for three fingers
JointPositionDim = 9
JointVelocityDim = 9
JointTorqueDim = 9
# generalized coordinates
GeneralizedCoordinatesDim = JointPositionDim
GeneralizedVelocityDim = JointVelocityDim
# for objects
ObjectPoseDim = 7
ObjectVelocityDim = 6
# ################# #
# Different objects #
# ################# #
# radius of the area
ARENA_RADIUS = 0.195
class CuboidalObject:
"""
Fields for a cuboidal object.
@note Motivation for this class is that if domain randomization is performed over the
size of the cuboid, then its attributes are automatically updated as well.
"""
# 3D radius of the cuboid
radius_3d: float
# distance from wall to the center
max_com_distance_to_center: float
# minimum and mximum height for spawning the object
min_height: float
max_height = 0.1
NumKeypoints = 8
ObjectPositionDim = 3
KeypointsCoordsDim = NumKeypoints * ObjectPositionDim
def __init__(self, size: Union[float, Tuple[float, float, float]]):
"""Initialize the cuboidal object.
Args:
size: The size of the object along x, y, z in meters. If a single float is provided, then it is assumed that
object is a cube.
"""
# decide the size depedning on input type
if isinstance(size, float):
self._size = (size, size, size)
else:
self._size = size
# compute remaining attributes
self.__compute()
"""
Properties
"""
@property
def size(self) -> Tuple[float, float, float]:
"""
Returns the dimensions of the cuboid object (x, y, z) in meters.
"""
return self._size
"""
Configurations
"""
@size.setter
def size(self, size: Union[float, Tuple[float, float, float]]):
""" Set size of the object.
Args:
size: The size of the object along x, y, z in meters. If a single float is provided, then it is assumed
that object is a cube.
"""
# decide the size depedning on input type
if isinstance(size, float):
self._size = (size, size, size)
else:
self._size = size
# compute attributes
self.__compute()
"""
Private members
"""
def __compute(self):
"""Compute the attributes for the object.
"""
# compute 3D radius of the cuboid
max_len = max(self._size)
self.radius_3d = max_len * np.sqrt(3) / 2
# compute distance from wall to the center
self.max_com_distance_to_center = ARENA_RADIUS - self.radius_3d
# minimum height for spawning the object
self.min_height = self._size[2] / 2
class Trifinger(VecTask):
# constants
# directory where assets for the simulator are present
_trifinger_assets_dir = os.path.join(project_dir, "../", "assets", "trifinger")
# robot urdf (path relative to `_trifinger_assets_dir`)
_robot_urdf_file = "robot_properties_fingers/urdf/pro/trifingerpro.urdf"
# stage urdf (path relative to `_trifinger_assets_dir`)
# _stage_urdf_file = "robot_properties_fingers/urdf/trifinger_stage.urdf"
_table_urdf_file = "robot_properties_fingers/urdf/table_without_border.urdf"
_boundary_urdf_file = "robot_properties_fingers/urdf/high_table_boundary.urdf"
# object urdf (path relative to `_trifinger_assets_dir`)
# TODO: Make object URDF configurable.
_object_urdf_file = "objects/urdf/cube_multicolor_rrc.urdf"
# physical dimensions of the object
# TODO: Make object dimensions configurable.
_object_dims = CuboidalObject(0.065)
# dimensions of the system
_dims = TrifingerDimensions
# Constants for limits
# Ref: https://github.com/rr-learning/rrc_simulation/blob/master/python/rrc_simulation/trifinger_platform.py#L68
# maximum joint torque (in N-m) applicable on each actuator
_max_torque_Nm = 0.36
# maximum joint velocity (in rad/s) on each actuator
_max_velocity_radps = 10
# History of state: Number of timesteps to save history for
# Note: Currently used only to manage history of object and frame states.
# This can be extended to other observations (as done in ANYmal).
_state_history_len = 2
# buffers to store the simulation data
# goal poses for the object [num. of instances, 7] where 7: (x, y, z, quat)
_object_goal_poses_buf: torch.Tensor
# DOF state of the system [num. of instances, num. of dof, 2] where last index: pos, vel
_dof_state: torch.Tensor
# Rigid body state of the system [num. of instances, num. of bodies, 13] where 13: (x, y, z, quat, v, omega)
_rigid_body_state: torch.Tensor
# Root prim states [num. of actors, 13] where 13: (x, y, z, quat, v, omega)
_actors_root_state: torch.Tensor
# Force-torque sensor array [num. of instances, num. of bodies * wrench]
_ft_sensors_values: torch.Tensor
# DOF position of the system [num. of instances, num. of dof]
_dof_position: torch.Tensor
# DOF velocity of the system [num. of instances, num. of dof]
_dof_velocity: torch.Tensor
# DOF torque of the system [num. of instances, num. of dof]
_dof_torque: torch.Tensor
# Fingertip links state list([num. of instances, num. of fingers, 13]) where 13: (x, y, z, quat, v, omega)
# The length of list is the history of the state: 0: t, 1: t-1, 2: t-2, ... step.
_fingertips_frames_state_history: Deque[torch.Tensor] = deque(maxlen=_state_history_len)
# Object prim state [num. of instances, 13] where 13: (x, y, z, quat, v, omega)
# The length of list is the history of the state: 0: t, 1: t-1, 2: t-2, ... step.
_object_state_history: Deque[torch.Tensor] = deque(maxlen=_state_history_len)
# stores the last action output
_last_action: torch.Tensor
# keeps track of the number of goal resets
_successes: torch.Tensor
# keeps track of number of consecutive successes
_consecutive_successes: float
_robot_limits: dict = {
"joint_position": SimpleNamespace(
# matches those on the real robot
low=np.array([-0.33, 0.0, -2.7] * _dims.NumFingers.value, dtype=np.float32),
high=np.array([1.0, 1.57, 0.0] * _dims.NumFingers.value, dtype=np.float32),
default=np.array([0.0, 0.9, -2.0] * _dims.NumFingers.value, dtype=np.float32),
),
"joint_velocity": SimpleNamespace(
low=np.full(_dims.JointVelocityDim.value, -_max_velocity_radps, dtype=np.float32),
high=np.full(_dims.JointVelocityDim.value, _max_velocity_radps, dtype=np.float32),
default=np.zeros(_dims.JointVelocityDim.value, dtype=np.float32),
),
"joint_torque": SimpleNamespace(
low=np.full(_dims.JointTorqueDim.value, -_max_torque_Nm, dtype=np.float32),
high=np.full(_dims.JointTorqueDim.value, _max_torque_Nm, dtype=np.float32),
default=np.zeros(_dims.JointTorqueDim.value, dtype=np.float32),
),
"fingertip_position": SimpleNamespace(
low=np.array([-0.4, -0.4, 0], dtype=np.float32),
high=np.array([0.4, 0.4, 0.5], dtype=np.float32),
),
"fingertip_orientation": SimpleNamespace(
low=-np.ones(4, dtype=np.float32),
high=np.ones(4, dtype=np.float32),
),
"fingertip_velocity": SimpleNamespace(
low=np.full(_dims.VelocityDim.value, -0.2, dtype=np.float32),
high=np.full(_dims.VelocityDim.value, 0.2, dtype=np.float32),
),
"fingertip_wrench": SimpleNamespace(
low=np.full(_dims.WrenchDim.value, -1.0, dtype=np.float32),
high=np.full(_dims.WrenchDim.value, 1.0, dtype=np.float32),
),
# used if we want to have joint stiffness/damping as parameters`
"joint_stiffness": SimpleNamespace(
low=np.array([1.0, 1.0, 1.0] * _dims.NumFingers.value, dtype=np.float32),
high=np.array([50.0, 50.0, 50.0] * _dims.NumFingers.value, dtype=np.float32),
),
"joint_damping": SimpleNamespace(
low=np.array([0.01, 0.03, 0.0001] * _dims.NumFingers.value, dtype=np.float32),
high=np.array([1.0, 3.0, 0.01] * _dims.NumFingers.value, dtype=np.float32),
),
}
# limits of the object (mapped later: str -> torch.tensor)
_object_limits: dict = {
"position": SimpleNamespace(
low=np.array([-0.3, -0.3, 0], dtype=np.float32),
high=np.array([0.3, 0.3, 0.3], dtype=np.float32),
default=np.array([0, 0, _object_dims.min_height], dtype=np.float32)
),
# difference between two positions
"position_delta": SimpleNamespace(
low=np.array([-0.6, -0.6, 0], dtype=np.float32),
high=np.array([0.6, 0.6, 0.3], dtype=np.float32),
default=np.array([0, 0, 0], dtype=np.float32)
),
"orientation": SimpleNamespace(
low=-np.ones(4, dtype=np.float32),
high=np.ones(4, dtype=np.float32),
default=np.array([0.0, 0.0, 0.0, 1.0], dtype=np.float32),
),
"velocity": SimpleNamespace(
low=np.full(_dims.VelocityDim.value, -0.5, dtype=np.float32),
high=np.full(_dims.VelocityDim.value, 0.5, dtype=np.float32),
default=np.zeros(_dims.VelocityDim.value, dtype=np.float32)
),
"scale": SimpleNamespace(
low=np.full(1, 0.0, dtype=np.float32),
high=np.full(1, 1.0, dtype=np.float32),
),
}
# PD gains for the robot (mapped later: str -> torch.tensor)
# Ref: https://github.com/rr-learning/rrc_simulation/blob/master/python/rrc_simulation/sim_finger.py#L49-L65
_robot_dof_gains = {
# The kp and kd gains of the PD control of the fingers.
# Note: This depends on simulation step size and is set for a rate of 250 Hz.
"stiffness": [10.0, 10.0, 10.0] * _dims.NumFingers.value,
"damping": [0.1, 0.3, 0.001] * _dims.NumFingers.value,
# The kd gains used for damping the joint motor velocities during the
# safety torque check on the joint motors.
"safety_damping": [0.08, 0.08, 0.04] * _dims.NumFingers.value
}
action_dim = _dims.JointTorqueDim.value
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.obs_spec = {
"robot_q": self._dims.GeneralizedCoordinatesDim.value,
"robot_u": self._dims.GeneralizedVelocityDim.value,
"object_q": self._dims.ObjectPoseDim.value,
"object_q_des": self._dims.ObjectPoseDim.value,
"command": self.action_dim
}
if self.cfg["env"]["asymmetric_obs"]:
self.state_spec = {
# observations spec
**self.obs_spec,
# extra observations (added separately to make computations simpler)
"object_u": self._dims.ObjectVelocityDim.value,
"fingertip_state": self._dims.NumFingers.value * self._dims.StateDim.value,
"robot_a": self._dims.GeneralizedVelocityDim.value,
"fingertip_wrench": self._dims.NumFingers.value * self._dims.WrenchDim.value,
}
else:
self.state_spec = self.obs_spec
self.action_spec = {
"command": self.action_dim
}
self.cfg["env"]["numObservations"] = sum(self.obs_spec.values())
self.cfg["env"]["numStates"] = sum(self.state_spec.values())
self.cfg["env"]["numActions"] = sum(self.action_spec.values())
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.randomize = self.cfg["task"]["randomize"]
self.randomization_params = self.cfg["task"]["randomization_params"]
# define prims present in the scene
prim_names = ["robot", "table", "boundary", "object", "goal_object"]
# mapping from name to asset instance
self.gym_assets = dict.fromkeys(prim_names)
# mapping from name to gym indices
self.gym_indices = dict.fromkeys(prim_names)
# mapping from name to gym rigid body handles
# name of finger tips links i.e. end-effector frames
fingertips_frames = ["finger_tip_link_0", "finger_tip_link_120", "finger_tip_link_240"]
self._fingertips_handles = OrderedDict.fromkeys(fingertips_frames, None)
# mapping from name to gym dof index
robot_dof_names = list()
for finger_pos in ['0', '120', '240']:
robot_dof_names += [f'finger_base_to_upper_joint_{finger_pos}',
f'finger_upper_to_middle_joint_{finger_pos}',
f'finger_middle_to_lower_joint_{finger_pos}']
self._robot_dof_indices = OrderedDict.fromkeys(robot_dof_names, None)
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
if self.viewer != None:
cam_pos = gymapi.Vec3(0.7, 0.0, 0.7)
cam_target = gymapi.Vec3(0.0, 0.0, 0.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# change constant buffers from numpy/lists into torch tensors
# limits for robot
for limit_name in self._robot_limits:
# extract limit simple-namespace
limit_dict = self._robot_limits[limit_name].__dict__
# iterate over namespace attributes
for prop, value in limit_dict.items():
limit_dict[prop] = torch.tensor(value, dtype=torch.float, device=self.device)
# limits for the object
for limit_name in self._object_limits:
# extract limit simple-namespace
limit_dict = self._object_limits[limit_name].__dict__
# iterate over namespace attributes
for prop, value in limit_dict.items():
limit_dict[prop] = torch.tensor(value, dtype=torch.float, device=self.device)
# PD gains for actuation
for gain_name, value in self._robot_dof_gains.items():
self._robot_dof_gains[gain_name] = torch.tensor(value, dtype=torch.float, device=self.device)
# store the sampled goal poses for the object: [num. of instances, 7]
self._object_goal_poses_buf = torch.zeros((self.num_envs, 7), device=self.device, dtype=torch.float)
# get force torque sensor if enabled
if self.cfg["env"]["enable_ft_sensors"] or self.cfg["env"]["asymmetric_obs"]:
# # joint torques
# dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
# self._dof_torque = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs,
# self._dims.JointTorqueDim.value)
# # force-torque sensor
num_ft_dims = self._dims.NumFingers.value * self._dims.WrenchDim.value
# sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
# self._ft_sensors_values = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, num_ft_dims)
sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
self._ft_sensors_values = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, num_ft_dims)
dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
self._dof_torque = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs, self._dims.JointTorqueDim.value)
# get gym GPU state tensors
actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
rigid_body_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
# refresh the buffer (to copy memory?)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
# create wrapper tensors for reference (consider everything as pointer to actual memory)
# DOF
self._dof_state = gymtorch.wrap_tensor(dof_state_tensor).view(self.num_envs, -1, 2)
self._dof_position = self._dof_state[..., 0]
self._dof_velocity = self._dof_state[..., 1]
# rigid body
self._rigid_body_state = gymtorch.wrap_tensor(rigid_body_tensor).view(self.num_envs, -1, 13)
# root actors
self._actors_root_state = gymtorch.wrap_tensor(actor_root_state_tensor).view(-1, 13)
# frames history
action_dim = sum(self.action_spec.values())
self._last_action = torch.zeros(self.num_envs, action_dim, dtype=torch.float, device=self.device)
fingertip_handles_indices = list(self._fingertips_handles.values())
object_indices = self.gym_indices["object"]
# timestep 0 is current tensor
curr_history_length = 0
while curr_history_length < self._state_history_len:
# add tensors to history list
print(self._rigid_body_state.shape)
self._fingertips_frames_state_history.append(self._rigid_body_state[:, fingertip_handles_indices])
self._object_state_history.append(self._actors_root_state[object_indices])
# update current history length
curr_history_length += 1
self._observations_scale = SimpleNamespace(low=None, high=None)
self._states_scale = SimpleNamespace(low=None, high=None)
self._action_scale = SimpleNamespace(low=None, high=None)
self._successes = torch.zeros(self.num_envs, device=self.device, dtype=torch.long)
self._successes_pos = torch.zeros(self.num_envs, device=self.device, dtype=torch.long)
self._successes_quat = torch.zeros(self.num_envs, device=self.device, dtype=torch.long)
self.__configure_mdp_spaces()
def create_sim(self):
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_scene_assets()
self._create_envs(self.num_envs, self.cfg["env"]["envSpacing"], int(np.sqrt(self.num_envs)))
# If randomizing, apply once immediately on startup before the fist sim step
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
plane_params.distance = 0.013
plane_params.static_friction = 1.0
plane_params.dynamic_friction = 1.0
self.gym.add_ground(self.sim, plane_params)
def _create_scene_assets(self):
""" Define Gym assets for stage, robot and object.
"""
# define assets
self.gym_assets["robot"] = self.__define_robot_asset()
self.gym_assets["table"] = self.__define_table_asset()
self.gym_assets["boundary"] = self.__define_boundary_asset()
self.gym_assets["object"] = self.__define_object_asset()
self.gym_assets["goal_object"] = self.__define_goal_object_asset()
# display the properties (only for debugging)
# robot
print("Trifinger Robot Asset: ")
print(f'\t Number of bodies: {self.gym.get_asset_rigid_body_count(self.gym_assets["robot"])}')
print(f'\t Number of shapes: {self.gym.get_asset_rigid_shape_count(self.gym_assets["robot"])}')
print(f'\t Number of dofs: {self.gym.get_asset_dof_count(self.gym_assets["robot"])}')
print(f'\t Number of actuated dofs: {self._dims.JointTorqueDim.value}')
# stage
print("Trifinger Table Asset: ")
print(f'\t Number of bodies: {self.gym.get_asset_rigid_body_count(self.gym_assets["table"])}')
print(f'\t Number of shapes: {self.gym.get_asset_rigid_shape_count(self.gym_assets["table"])}')
print("Trifinger Boundary Asset: ")
print(f'\t Number of bodies: {self.gym.get_asset_rigid_body_count(self.gym_assets["boundary"])}')
print(f'\t Number of shapes: {self.gym.get_asset_rigid_shape_count(self.gym_assets["boundary"])}')
def _create_envs(self, num_envs, spacing, num_per_row):
# define the dof properties for the robot
robot_dof_props = self.gym.get_asset_dof_properties(self.gym_assets["robot"])
# set dof properites based on the control mode
for k, dof_index in enumerate(self._robot_dof_indices.values()):
# note: since safety checks are employed, the simulator PD controller is not
# used. Instead the torque is computed manually and applied, even if the
# command mode is 'position'.
robot_dof_props['driveMode'][dof_index] = gymapi.DOF_MODE_EFFORT
robot_dof_props['stiffness'][dof_index] = 0.0
robot_dof_props['damping'][dof_index] = 0.0
# set dof limits
robot_dof_props['effort'][dof_index] = self._max_torque_Nm
robot_dof_props['velocity'][dof_index] = self._max_velocity_radps
robot_dof_props['lower'][dof_index] = float(self._robot_limits["joint_position"].low[k])
robot_dof_props['upper'][dof_index] = float(self._robot_limits["joint_position"].high[k])
self.envs = []
# define lower and upper region bound for each environment
env_lower_bound = gymapi.Vec3(-self.cfg["env"]["envSpacing"], -self.cfg["env"]["envSpacing"], 0.0)
env_upper_bound = gymapi.Vec3(self.cfg["env"]["envSpacing"], self.cfg["env"]["envSpacing"], self.cfg["env"]["envSpacing"])
num_envs_per_row = int(np.sqrt(self.num_envs))
# initialize gym indices buffer as a list
# note: later the list is converted to torch tensor for ease in interfacing with IsaacGym.
for asset_name in self.gym_indices.keys():
self.gym_indices[asset_name] = list()
# count number of shapes and bodies
max_agg_bodies = 0
max_agg_shapes = 0
for asset in self.gym_assets.values():
max_agg_bodies += self.gym.get_asset_rigid_body_count(asset)
max_agg_shapes += self.gym.get_asset_rigid_shape_count(asset)
# iterate and create environment instances
for env_index in range(self.num_envs):
# create environment
env_ptr = self.gym.create_env(self.sim, env_lower_bound, env_upper_bound, num_envs_per_row)
# begin aggregration mode if enabled - this can improve simulation performance
if self.cfg["env"]["aggregate_mode"]:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
# add trifinger robot to environment
trifinger_actor = self.gym.create_actor(env_ptr, self.gym_assets["robot"], gymapi.Transform(),
"robot", env_index, 0, 0)
trifinger_idx = self.gym.get_actor_index(env_ptr, trifinger_actor, gymapi.DOMAIN_SIM)
# add table to environment
table_handle = self.gym.create_actor(env_ptr, self.gym_assets["table"], gymapi.Transform(),
"table", env_index, 1, 0)
table_idx = self.gym.get_actor_index(env_ptr, table_handle, gymapi.DOMAIN_SIM)
# add stage to environment
boundary_handle = self.gym.create_actor(env_ptr, self.gym_assets["boundary"], gymapi.Transform(),
"boundary", env_index, 1, 0)
boundary_idx = self.gym.get_actor_index(env_ptr, boundary_handle, gymapi.DOMAIN_SIM)
# add object to environment
object_handle = self.gym.create_actor(env_ptr, self.gym_assets["object"], gymapi.Transform(),
"object", env_index, 0, 0)
object_idx = self.gym.get_actor_index(env_ptr, object_handle, gymapi.DOMAIN_SIM)
# add goal object to environment
goal_handle = self.gym.create_actor(env_ptr, self.gym_assets["goal_object"], gymapi.Transform(),
"goal_object", env_index + self.num_envs, 0, 0)
goal_object_idx = self.gym.get_actor_index(env_ptr, goal_handle, gymapi.DOMAIN_SIM)
# change settings of DOF
self.gym.set_actor_dof_properties(env_ptr, trifinger_actor, robot_dof_props)
# add color to instances
stage_color = gymapi.Vec3(0.73, 0.68, 0.72)
self.gym.set_rigid_body_color(env_ptr, table_handle, 0, gymapi.MESH_VISUAL_AND_COLLISION, stage_color)
self.gym.set_rigid_body_color(env_ptr, boundary_handle, 0, gymapi.MESH_VISUAL_AND_COLLISION, stage_color)
# end aggregation mode if enabled
if self.cfg["env"]["aggregate_mode"]:
self.gym.end_aggregate(env_ptr)
# add instances to list
self.envs.append(env_ptr)
self.gym_indices["robot"].append(trifinger_idx)
self.gym_indices["table"].append(table_idx)
self.gym_indices["boundary"].append(boundary_idx)
self.gym_indices["object"].append(object_idx)
self.gym_indices["goal_object"].append(goal_object_idx)
# convert gym indices from list to tensor
for asset_name, asset_indices in self.gym_indices.items():
self.gym_indices[asset_name] = torch.tensor(asset_indices, dtype=torch.long, device=self.device)
def __configure_mdp_spaces(self):
"""
Configures the observations, state and action spaces.
"""
# Action scale for the MDP
# Note: This is order sensitive.
if self.cfg["env"]["command_mode"] == "position":
# action space is joint positions
self._action_scale.low = self._robot_limits["joint_position"].low
self._action_scale.high = self._robot_limits["joint_position"].high
elif self.cfg["env"]["command_mode"] == "torque":
# action space is joint torques
self._action_scale.low = self._robot_limits["joint_torque"].low
self._action_scale.high = self._robot_limits["joint_torque"].high
else:
msg = f"Invalid command mode. Input: {self.cfg['env']['command_mode']} not in ['torque', 'position']."
raise ValueError(msg)
# Observations scale for the MDP
# check if policy outputs normalized action [-1, 1] or not.
if self.cfg["env"]["normalize_action"]:
obs_action_scale = SimpleNamespace(
low=torch.full((self.action_dim,), -1, dtype=torch.float, device=self.device),
high=torch.full((self.action_dim,), 1, dtype=torch.float, device=self.device)
)
else:
obs_action_scale = self._action_scale
object_obs_low = torch.cat([
self._object_limits["position"].low,
self._object_limits["orientation"].low,
]*2)
object_obs_high = torch.cat([
self._object_limits["position"].high,
self._object_limits["orientation"].high,
]*2)
# Note: This is order sensitive.
self._observations_scale.low = torch.cat([
self._robot_limits["joint_position"].low,
self._robot_limits["joint_velocity"].low,
object_obs_low,
obs_action_scale.low
])
self._observations_scale.high = torch.cat([
self._robot_limits["joint_position"].high,
self._robot_limits["joint_velocity"].high,
object_obs_high,
obs_action_scale.high
])
# State scale for the MDP
if self.cfg["env"]["asymmetric_obs"]:
# finger tip scaling
fingertip_state_scale = SimpleNamespace(
low=torch.cat([
self._robot_limits["fingertip_position"].low,
self._robot_limits["fingertip_orientation"].low,
self._robot_limits["fingertip_velocity"].low,
]),
high=torch.cat([
self._robot_limits["fingertip_position"].high,
self._robot_limits["fingertip_orientation"].high,
self._robot_limits["fingertip_velocity"].high,
])
)
states_low = [
self._observations_scale.low,
self._object_limits["velocity"].low,
fingertip_state_scale.low.repeat(self._dims.NumFingers.value),
self._robot_limits["joint_torque"].low,
self._robot_limits["fingertip_wrench"].low.repeat(self._dims.NumFingers.value),
]
states_high = [
self._observations_scale.high,
self._object_limits["velocity"].high,
fingertip_state_scale.high.repeat(self._dims.NumFingers.value),
self._robot_limits["joint_torque"].high,
self._robot_limits["fingertip_wrench"].high.repeat(self._dims.NumFingers.value),
]
# Note: This is order sensitive.
self._states_scale.low = torch.cat(states_low)
self._states_scale.high = torch.cat(states_high)
# check that dimensions of scalings are correct
# count number of dimensions
state_dim = sum(self.state_spec.values())
obs_dim = sum(self.obs_spec.values())
action_dim = sum(self.action_spec.values())
# check that dimensions match
# observations
if self._observations_scale.low.shape[0] != obs_dim or self._observations_scale.high.shape[0] != obs_dim:
msg = f"Observation scaling dimensions mismatch. " \
f"\tLow: {self._observations_scale.low.shape[0]}, " \
f"\tHigh: {self._observations_scale.high.shape[0]}, " \
f"\tExpected: {obs_dim}."
raise AssertionError(msg)
# state
if self.cfg["env"]["asymmetric_obs"] \
and (self._states_scale.low.shape[0] != state_dim or self._states_scale.high.shape[0] != state_dim):
msg = f"States scaling dimensions mismatch. " \
f"\tLow: {self._states_scale.low.shape[0]}, " \
f"\tHigh: {self._states_scale.high.shape[0]}, " \
f"\tExpected: {state_dim}."
raise AssertionError(msg)
# actions
if self._action_scale.low.shape[0] != action_dim or self._action_scale.high.shape[0] != action_dim:
msg = f"Actions scaling dimensions mismatch. " \
f"\tLow: {self._action_scale.low.shape[0]}, " \
f"\tHigh: {self._action_scale.high.shape[0]}, " \
f"\tExpected: {action_dim}."
raise AssertionError(msg)
# print the scaling
print(f'MDP Raw observation bounds\n'
f'\tLow: {self._observations_scale.low}\n'
f'\tHigh: {self._observations_scale.high}')
print(f'MDP Raw state bounds\n'
f'\tLow: {self._states_scale.low}\n'
f'\tHigh: {self._states_scale.high}')
print(f'MDP Raw action bounds\n'
f'\tLow: {self._action_scale.low}\n'
f'\tHigh: {self._action_scale.high}')
def compute_reward(self, actions):
self.rew_buf[:] = 0.
self.reset_buf[:] = 0.
self.rew_buf[:], self.reset_buf[:], log_dict = compute_trifinger_reward(
self.obs_buf,
self.reset_buf,
self.progress_buf,
self.max_episode_length,
self.cfg["sim"]["dt"],
self.cfg["env"]["reward_terms"]["finger_move_penalty"]["weight"],
self.cfg["env"]["reward_terms"]["finger_reach_object_rate"]["weight"],
self.cfg["env"]["reward_terms"]["object_dist"]["weight"],
self.cfg["env"]["reward_terms"]["object_rot"]["weight"],
self.env_steps_count,
self._object_goal_poses_buf,
self._object_state_history[0],
self._object_state_history[1],
self._fingertips_frames_state_history[0],
self._fingertips_frames_state_history[1],
self.cfg["env"]["reward_terms"]["keypoints_dist"]["activate"]
)
self.extras.update({"env/rewards/"+k: v.mean() for k, v in log_dict.items()})
def compute_observations(self):
# refresh memory buffers
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
if self.cfg["env"]["enable_ft_sensors"] or self.cfg["env"]["asymmetric_obs"]:
self.gym.refresh_dof_force_tensor(self.sim)
self.gym.refresh_force_sensor_tensor(self.sim)
joint_torques = self._dof_torque
tip_wrenches = self._ft_sensors_values
else:
joint_torques = torch.zeros(self.num_envs, self._dims.JointTorqueDim.value, dtype=torch.float32, device=self.device)
tip_wrenches = torch.zeros(self.num_envs, self._dims.NumFingers.value * self._dims.WrenchDim.value, dtype=torch.float32, device=self.device)
# extract frame handles
fingertip_handles_indices = list(self._fingertips_handles.values())
object_indices = self.gym_indices["object"]
# update state histories
self._fingertips_frames_state_history.appendleft(self._rigid_body_state[:, fingertip_handles_indices])
self._object_state_history.appendleft(self._actors_root_state[object_indices])
# fill the observations and states buffer
self.obs_buf[:], self.states_buf[:] = compute_trifinger_observations_states(
self.cfg["env"]["asymmetric_obs"],
self._dof_position,
self._dof_velocity,
self._object_state_history[0],
self._object_goal_poses_buf,
self.actions,
self._fingertips_frames_state_history[0],
joint_torques,
tip_wrenches,
)
# normalize observations if flag is enabled
if self.cfg["env"]["normalize_obs"]:
# for normal obs
self.obs_buf = scale_transform(
self.obs_buf,
lower=self._observations_scale.low,
upper=self._observations_scale.high
)
def reset_idx(self, env_ids):
# randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
# A) Reset episode stats buffers
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
self._successes[env_ids] = 0
self._successes_pos[env_ids] = 0
self._successes_quat[env_ids] = 0
# B) Various randomizations at the start of the episode:
# -- Robot base position.
# -- Stage position.
# -- Coefficient of restituion and friction for robot, object, stage.
# -- Mass and size of the object
# -- Mass of robot links
# -- Robot joint state
robot_initial_state_config = self.cfg["env"]["reset_distribution"]["robot_initial_state"]
self._sample_robot_state(
env_ids,
distribution=robot_initial_state_config["type"],
dof_pos_stddev=robot_initial_state_config["dof_pos_stddev"],
dof_vel_stddev=robot_initial_state_config["dof_vel_stddev"]
)
# -- Sampling of initial pose of the object
object_initial_state_config = self.cfg["env"]["reset_distribution"]["object_initial_state"]
self._sample_object_poses(
env_ids,
distribution=object_initial_state_config["type"],
)
# -- Sampling of goal pose of the object
self._sample_object_goal_poses(
env_ids,
difficulty=self.cfg["env"]["task_difficulty"]
)
# C) Extract trifinger indices to reset
robot_indices = self.gym_indices["robot"][env_ids].to(torch.int32)
object_indices = self.gym_indices["object"][env_ids].to(torch.int32)
goal_object_indices = self.gym_indices["goal_object"][env_ids].to(torch.int32)
all_indices = torch.unique(torch.cat([robot_indices, object_indices, goal_object_indices]))
# D) Set values into simulator
# -- DOF
self.gym.set_dof_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._dof_state),
gymtorch.unwrap_tensor(robot_indices), len(robot_indices))
# -- actor root states
self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._actors_root_state),
gymtorch.unwrap_tensor(all_indices), len(all_indices))
def _sample_robot_state(self, instances: torch.Tensor, distribution: str = 'default',
dof_pos_stddev: float = 0.0, dof_vel_stddev: float = 0.0):
"""Samples the robot DOF state based on the settings.
Type of robot initial state distribution: ["default", "random"]
- "default" means that robot is in default configuration.
- "random" means that noise is added to default configuration
- "none" means that robot is configuration is not reset between episodes.
Args:
instances: A tensor constraining indices of environment instances to reset.
distribution: Name of distribution to sample initial state from: ['default', 'random']
dof_pos_stddev: Noise scale to DOF position (used if 'type' is 'random')
dof_vel_stddev: Noise scale to DOF velocity (used if 'type' is 'random')
"""
# number of samples to generate
num_samples = instances.size()[0]
# sample dof state based on distribution type
if distribution == "none":
return
elif distribution == "default":
# set to default configuration
self._dof_position[instances] = self._robot_limits["joint_position"].default
self._dof_velocity[instances] = self._robot_limits["joint_velocity"].default
elif distribution == "random":
# sample uniform random from (-1, 1)
dof_state_dim = self._dims.JointPositionDim.value + self._dims.JointVelocityDim.value
dof_state_noise = 2 * torch.rand((num_samples, dof_state_dim,), dtype=torch.float,
device=self.device) - 1
# set to default configuration
self._dof_position[instances] = self._robot_limits["joint_position"].default
self._dof_velocity[instances] = self._robot_limits["joint_velocity"].default
# add noise
# DOF position
start_offset = 0
end_offset = self._dims.JointPositionDim.value
self._dof_position[instances] += dof_pos_stddev * dof_state_noise[:, start_offset:end_offset]
# DOF velocity
start_offset = end_offset
end_offset += self._dims.JointVelocityDim.value
self._dof_velocity[instances] += dof_vel_stddev * dof_state_noise[:, start_offset:end_offset]
else:
msg = f"Invalid robot initial state distribution. Input: {distribution} not in [`default`, `random`]."
raise ValueError(msg)
# reset robot fingertips state history
for idx in range(1, self._state_history_len):
self._fingertips_frames_state_history[idx][instances] = 0.0
def _sample_object_poses(self, instances: torch.Tensor, distribution: str):
"""Sample poses for the cube.
Type of distribution: ["default", "random", "none"]
- "default" means that pose is default configuration.
- "random" means that pose is randomly sampled on the table.
- "none" means no resetting of object pose between episodes.
Args:
instances: A tensor constraining indices of environment instances to reset.
distribution: Name of distribution to sample initial state from: ['default', 'random']
"""
# number of samples to generate
num_samples = instances.size()[0]
# sample poses based on distribution type
if distribution == "none":
return
elif distribution == "default":
pos_x, pos_y, pos_z = self._object_limits["position"].default
orientation = self._object_limits["orientation"].default
elif distribution == "random":
# For initialization
pos_x, pos_y = random_xy(num_samples, self._object_dims.max_com_distance_to_center, self.device)
# add a small offset to the height to account for scale randomisation (prevent ground intersection)
pos_z = self._object_dims.size[2] / 2 + 0.0015
orientation = random_yaw_orientation(num_samples, self.device)
else:
msg = f"Invalid object initial state distribution. Input: {distribution} " \
"not in [`default`, `random`, `none`]."
raise ValueError(msg)
# set buffers into simulator
# extract indices for goal object
object_indices = self.gym_indices["object"][instances]
# set values into buffer
# object buffer
self._object_state_history[0][instances, 0] = pos_x
self._object_state_history[0][instances, 1] = pos_y
self._object_state_history[0][instances, 2] = pos_z
self._object_state_history[0][instances, 3:7] = orientation
self._object_state_history[0][instances, 7:13] = 0
# reset object state history
for idx in range(1, self._state_history_len):
self._object_state_history[idx][instances] = 0.0
# root actor buffer
self._actors_root_state[object_indices] = self._object_state_history[0][instances]
def _sample_object_goal_poses(self, instances: torch.Tensor, difficulty: int):
"""Sample goal poses for the cube and sets them into the desired goal pose buffer.
Args:
instances: A tensor constraining indices of environment instances to reset.
difficulty: Difficulty level. The higher, the more difficult is the goal.
Possible levels are:
- -1: Random goal position on the table, including yaw orientation.
- 1: Random goal position on the table, no orientation.
- 2: Fixed goal position in the air with x,y = 0. No orientation.
- 3: Random goal position in the air, no orientation.
- 4: Random goal pose in the air, including orientation.
"""
# number of samples to generate
num_samples = instances.size()[0]
# sample poses based on task difficulty
if difficulty == -1:
# For initialization
pos_x, pos_y = random_xy(num_samples, self._object_dims.max_com_distance_to_center, self.device)
pos_z = self._object_dims.size[2] / 2
orientation = random_yaw_orientation(num_samples, self.device)
elif difficulty == 1:
# Random goal position on the table, no orientation.
pos_x, pos_y = random_xy(num_samples, self._object_dims.max_com_distance_to_center, self.device)
pos_z = self._object_dims.size[2] / 2
orientation = default_orientation(num_samples, self.device)
elif difficulty == 2:
# Fixed goal position in the air with x,y = 0. No orientation.
pos_x, pos_y = 0.0, 0.0
pos_z = self._object_dims.min_height + 0.05
orientation = default_orientation(num_samples, self.device)
elif difficulty == 3:
# Random goal position in the air, no orientation.
pos_x, pos_y = random_xy(num_samples, self._object_dims.max_com_distance_to_center, self.device)
pos_z = random_z(num_samples, self._object_dims.min_height, self._object_dims.max_height, self.device)
orientation = default_orientation(num_samples, self.device)
elif difficulty == 4:
# Random goal pose in the air, including orientation.
# Note: Set minimum height such that the cube does not intersect with the
# ground in any orientation
max_goal_radius = self._object_dims.max_com_distance_to_center
max_height = self._object_dims.max_height
orientation = random_orientation(num_samples, self.device)
# pick x, y, z according to the maximum height / radius at the current point
# in the cirriculum
pos_x, pos_y = random_xy(num_samples, max_goal_radius, self.device)
pos_z = random_z(num_samples, self._object_dims.radius_3d, max_height, self.device)
else:
msg = f"Invalid difficulty index for task: {difficulty}."
raise ValueError(msg)
# extract indices for goal object
goal_object_indices = self.gym_indices["goal_object"][instances]
# set values into buffer
# object goal buffer
self._object_goal_poses_buf[instances, 0] = pos_x
self._object_goal_poses_buf[instances, 1] = pos_y
self._object_goal_poses_buf[instances, 2] = pos_z
self._object_goal_poses_buf[instances, 3:7] = orientation
# root actor buffer
self._actors_root_state[goal_object_indices, 0:7] = self._object_goal_poses_buf[instances]
# self._actors_root_state[goal_object_indices, 2] = -10
def pre_physics_step(self, actions):
env_ids = self.reset_buf.nonzero(as_tuple=False).flatten()
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.gym.simulate(self.sim)
self.actions = actions.clone().to(self.device)
# if normalized_action is true, then denormalize them.
if self.cfg["env"]["normalize_action"]:
# TODO: Default action should correspond to normalized value of 0.
action_transformed = unscale_transform(
self.actions,
lower=self._action_scale.low,
upper=self._action_scale.high
)
else:
action_transformed = self.actions
# compute command on the basis of mode selected
if self.cfg["env"]["command_mode"] == 'torque':
# command is the desired joint torque
computed_torque = action_transformed
elif self.cfg["env"]["command_mode"] == 'position':
# command is the desired joint positions
desired_dof_position = action_transformed
# compute torque to apply
computed_torque = self._robot_dof_gains["stiffness"] * (desired_dof_position - self._dof_position)
computed_torque -= self._robot_dof_gains["damping"] * self._dof_velocity
else:
msg = f"Invalid command mode. Input: {self.cfg['env']['command_mode']} not in ['torque', 'position']."
raise ValueError(msg)
# apply clamping of computed torque to actuator limits
applied_torque = saturate(
computed_torque,
lower=self._robot_limits["joint_torque"].low,
upper=self._robot_limits["joint_torque"].high
)
# apply safety damping and clamping of the action torque if enabled
if self.cfg["env"]["apply_safety_damping"]:
# apply damping by joint velocity
applied_torque -= self._robot_dof_gains["safety_damping"] * self._dof_velocity
# clamp input
applied_torque = saturate(
applied_torque,
lower=self._robot_limits["joint_torque"].low,
upper=self._robot_limits["joint_torque"].high
)
# set computed torques to simulator buffer.
self.gym.set_dof_actuation_force_tensor(self.sim, gymtorch.unwrap_tensor(applied_torque))
def post_physics_step(self):
self._step_info = {}
self.progress_buf += 1
self.randomize_buf += 1
self.compute_observations()
self.compute_reward(self.actions)
# check termination conditions (success only)
self._check_termination()
if torch.sum(self.reset_buf) > 0:
self._step_info['consecutive_successes'] = np.mean(self._successes.float().cpu().numpy())
self._step_info['consecutive_successes_pos'] = np.mean(self._successes_pos.float().cpu().numpy())
self._step_info['consecutive_successes_quat'] = np.mean(self._successes_quat.float().cpu().numpy())
def _check_termination(self):
"""Check whether the episode is done per environment.
"""
# Extract configuration for termination conditions
termination_config = self.cfg["env"]["termination_conditions"]
# Termination condition - successful completion
# Calculate distance between current object and goal
object_goal_position_dist = torch.norm(
self._object_goal_poses_buf[:, 0:3] - self._object_state_history[0][:, 0:3],
p=2, dim=-1
)
# log theoretical number of r eseats
goal_position_reset = torch.le(object_goal_position_dist,
termination_config["success"]["position_tolerance"])
self._step_info['env/current_position_goal/per_env'] = np.mean(goal_position_reset.float().cpu().numpy())
# For task with difficulty 4, we need to check if orientation matches as well.
# Compute the difference in orientation between object and goal pose
object_goal_orientation_dist = quat_diff_rad(self._object_state_history[0][:, 3:7],
self._object_goal_poses_buf[:, 3:7])
# Check for distance within tolerance
goal_orientation_reset = torch.le(object_goal_orientation_dist,
termination_config["success"]["orientation_tolerance"])
self._step_info['env/current_orientation_goal/per_env'] = np.mean(goal_orientation_reset.float().cpu().numpy())
if self.cfg["env"]['task_difficulty'] < 4:
# Check for task completion if position goal is within a threshold
task_completion_reset = goal_position_reset
elif self.cfg["env"]['task_difficulty'] == 4:
# Check for task completion if both position + orientation goal is within a threshold
task_completion_reset = torch.logical_and(goal_position_reset, goal_orientation_reset)
else:
# Check for task completion if both orientation goal is within a threshold
task_completion_reset = goal_orientation_reset
self._successes = task_completion_reset
self._successes_pos = goal_position_reset
self._successes_quat = goal_orientation_reset
"""
Helper functions - define assets
"""
def __define_robot_asset(self):
""" Define Gym asset for robot.
"""
# define tri-finger asset
robot_asset_options = gymapi.AssetOptions()
robot_asset_options.flip_visual_attachments = False
robot_asset_options.fix_base_link = True
robot_asset_options.collapse_fixed_joints = False
robot_asset_options.disable_gravity = False
robot_asset_options.default_dof_drive_mode = gymapi.DOF_MODE_EFFORT
robot_asset_options.thickness = 0.001
robot_asset_options.angular_damping = 0.01
robot_asset_options.vhacd_enabled = True
robot_asset_options.vhacd_params = gymapi.VhacdParams()
robot_asset_options.vhacd_params.resolution = 100000
robot_asset_options.vhacd_params.concavity = 0.0025
robot_asset_options.vhacd_params.alpha = 0.04
robot_asset_options.vhacd_params.beta = 1.0
robot_asset_options.vhacd_params.convex_hull_downsampling = 4
robot_asset_options.vhacd_params.max_num_vertices_per_ch = 256
if self.physics_engine == gymapi.SIM_PHYSX:
robot_asset_options.use_physx_armature = True
# load tri-finger asset
trifinger_asset = self.gym.load_asset(self.sim, self._trifinger_assets_dir,
self._robot_urdf_file, robot_asset_options)
# set the link properties for the robot
# Ref: https://github.com/rr-learning/rrc_simulation/blob/master/python/rrc_simulation/sim_finger.py#L563
trifinger_props = self.gym.get_asset_rigid_shape_properties(trifinger_asset)
for p in trifinger_props:
p.friction = 1.0
p.torsion_friction = 1.0
p.restitution = 0.8
self.gym.set_asset_rigid_shape_properties(trifinger_asset, trifinger_props)
# extract the frame handles
for frame_name in self._fingertips_handles.keys():
self._fingertips_handles[frame_name] = self.gym.find_asset_rigid_body_index(trifinger_asset,
frame_name)
# check valid handle
if self._fingertips_handles[frame_name] == gymapi.INVALID_HANDLE:
msg = f"Invalid handle received for frame: `{frame_name}`."
print(msg)
if self.cfg["env"]["enable_ft_sensors"] or self.cfg["env"]["asymmetric_obs"]:
sensor_pose = gymapi.Transform()
for fingertip_handle in self._fingertips_handles.values():
self.gym.create_asset_force_sensor(trifinger_asset, fingertip_handle, sensor_pose)
# extract the dof indices
# Note: need to write actuated dofs manually since the system contains fixed joints as well which show up.
for dof_name in self._robot_dof_indices.keys():
self._robot_dof_indices[dof_name] = self.gym.find_asset_dof_index(trifinger_asset, dof_name)
# check valid handle
if self._robot_dof_indices[dof_name] == gymapi.INVALID_HANDLE:
msg = f"Invalid index received for DOF: `{dof_name}`."
print(msg)
# return the asset
return trifinger_asset
def __define_table_asset(self):
""" Define Gym asset for stage.
"""
# define stage asset
table_asset_options = gymapi.AssetOptions()
table_asset_options.disable_gravity = True
table_asset_options.fix_base_link = True
table_asset_options.thickness = 0.001
# load stage asset
table_asset = self.gym.load_asset(self.sim, self._trifinger_assets_dir,
self._table_urdf_file, table_asset_options)
# set stage properties
table_props = self.gym.get_asset_rigid_shape_properties(table_asset)
# iterate over each mesh
for p in table_props:
p.friction = 0.1
p.torsion_friction = 0.1
self.gym.set_asset_rigid_shape_properties(table_asset, table_props)
# return the asset
return table_asset
def __define_boundary_asset(self):
""" Define Gym asset for stage.
"""
# define stage asset
boundary_asset_options = gymapi.AssetOptions()
boundary_asset_options.disable_gravity = True
boundary_asset_options.fix_base_link = True
boundary_asset_options.thickness = 0.001
boundary_asset_options.vhacd_enabled = True
boundary_asset_options.vhacd_params = gymapi.VhacdParams()
boundary_asset_options.vhacd_params.resolution = 100000
boundary_asset_options.vhacd_params.concavity = 0.0
boundary_asset_options.vhacd_params.alpha = 0.04
boundary_asset_options.vhacd_params.beta = 1.0
boundary_asset_options.vhacd_params.max_num_vertices_per_ch = 1024
# load stage asset
boundary_asset = self.gym.load_asset(self.sim, self._trifinger_assets_dir,
self._boundary_urdf_file, boundary_asset_options)
# set stage properties
boundary_props = self.gym.get_asset_rigid_shape_properties(boundary_asset)
self.gym.set_asset_rigid_shape_properties(boundary_asset, boundary_props)
# return the asset
return boundary_asset
def __define_object_asset(self):
""" Define Gym asset for object.
"""
# define object asset
object_asset_options = gymapi.AssetOptions()
object_asset_options.disable_gravity = False
object_asset_options.thickness = 0.001
object_asset_options.flip_visual_attachments = True
# load object asset
object_asset = self.gym.load_asset(self.sim, self._trifinger_assets_dir,
self._object_urdf_file, object_asset_options)
# set object properties
# Ref: https://github.com/rr-learning/rrc_simulation/blob/master/python/rrc_simulation/collision_objects.py#L96
object_props = self.gym.get_asset_rigid_shape_properties(object_asset)
for p in object_props:
p.friction = 1.0
p.torsion_friction = 0.001
p.restitution = 0.0
self.gym.set_asset_rigid_shape_properties(object_asset, object_props)
# return the asset
return object_asset
def __define_goal_object_asset(self):
""" Define Gym asset for goal object.
"""
# define object asset
object_asset_options = gymapi.AssetOptions()
object_asset_options.disable_gravity = True
object_asset_options.fix_base_link = True
object_asset_options.thickness = 0.001
object_asset_options.flip_visual_attachments = True
# load object asset
goal_object_asset = self.gym.load_asset(self.sim, self._trifinger_assets_dir,
self._object_urdf_file, object_asset_options)
# return the asset
return goal_object_asset
@property
def env_steps_count(self) -> int:
"""Returns the total number of environment steps aggregated across parallel environments."""
return self.gym.get_frame_count(self.sim) * self.num_envs
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def lgsk_kernel(x: torch.Tensor, scale: float = 50.0, eps:float=2) -> torch.Tensor:
"""Defines logistic kernel function to bound input to [-0.25, 0)
Ref: https://arxiv.org/abs/1901.08652 (page 15)
Args:
x: Input tensor.
scale: Scaling of the kernel function (controls how wide the 'bell' shape is')
eps: Controls how 'tall' the 'bell' shape is.
Returns:
Output tensor computed using kernel.
"""
scaled = x * scale
return 1.0 / (scaled.exp() + eps + (-scaled).exp())
@torch.jit.script
def gen_keypoints(pose: torch.Tensor, num_keypoints: int = 8, size: Tuple[float, float, float] = (0.065, 0.065, 0.065)):
num_envs = pose.shape[0]
keypoints_buf = torch.ones(num_envs, num_keypoints, 3, dtype=torch.float32, device=pose.device)
for i in range(num_keypoints):
# which dimensions to negate
n = [((i >> k) & 1) == 0 for k in range(3)]
corner_loc = [(1 if n[k] else -1) * s / 2 for k, s in enumerate(size)],
corner = torch.tensor(corner_loc, dtype=torch.float32, device=pose.device) * keypoints_buf[:, i, :]
keypoints_buf[:, i, :] = local_to_world_space(corner, pose)
return keypoints_buf
@torch.jit.script
def compute_trifinger_reward(
obs_buf: torch.Tensor,
reset_buf: torch.Tensor,
progress_buf: torch.Tensor,
episode_length: int,
dt: float,
finger_move_penalty_weight: float,
finger_reach_object_weight: float,
object_dist_weight: float,
object_rot_weight: float,
env_steps_count: int,
object_goal_poses_buf: torch.Tensor,
object_state: torch.Tensor,
last_object_state: torch.Tensor,
fingertip_state: torch.Tensor,
last_fingertip_state: torch.Tensor,
use_keypoints: bool
) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, torch.Tensor]]:
ft_sched_start = 0
ft_sched_end = 5e7
# Reward penalising finger movement
fingertip_vel = (fingertip_state[:, :, 0:3] - last_fingertip_state[:, :, 0:3]) / dt
finger_movement_penalty = finger_move_penalty_weight * fingertip_vel.pow(2).view(-1, 9).sum(dim=-1)
# Reward for finger reaching the object
# distance from each finger to the centroid of the object, shape (N, 3).
curr_norms = torch.stack([
torch.norm(fingertip_state[:, i, 0:3] - object_state[:, 0:3], p=2, dim=-1)
for i in range(3)
], dim=-1)
# distance from each finger to the centroid of the object in the last timestep, shape (N, 3).
prev_norms = torch.stack([
torch.norm(last_fingertip_state[:, i, 0:3] - last_object_state[:, 0:3], p=2, dim=-1)
for i in range(3)
], dim=-1)
ft_sched_val = 1.0 if ft_sched_start <= env_steps_count <= ft_sched_end else 0.0
finger_reach_object_reward = finger_reach_object_weight * ft_sched_val * (curr_norms - prev_norms).sum(dim=-1)
if use_keypoints:
object_keypoints = gen_keypoints(object_state[:, 0:7])
goal_keypoints = gen_keypoints(object_goal_poses_buf[:, 0:7])
delta = object_keypoints - goal_keypoints
dist_l2 = torch.norm(delta, p=2, dim=-1)
keypoints_kernel_sum = lgsk_kernel(dist_l2, scale=30., eps=2.).mean(dim=-1)
pose_reward = object_dist_weight * dt * keypoints_kernel_sum
else:
# Reward for object distance
object_dist = torch.norm(object_state[:, 0:3] - object_goal_poses_buf[:, 0:3], p=2, dim=-1)
object_dist_reward = object_dist_weight * dt * lgsk_kernel(object_dist, scale=50., eps=2.)
# Reward for object rotation
# extract quaternion orientation
quat_a = object_state[:, 3:7]
quat_b = object_goal_poses_buf[:, 3:7]
angles = quat_diff_rad(quat_a, quat_b)
object_rot_reward = object_rot_weight * dt / (3. * torch.abs(angles) + 0.01)
pose_reward = object_dist_reward + object_rot_reward
total_reward = (
finger_movement_penalty
+ finger_reach_object_reward
+ pose_reward
)
# reset agents
reset = torch.zeros_like(reset_buf)
reset = torch.where(progress_buf >= episode_length - 1, torch.ones_like(reset_buf), reset)
info: Dict[str, torch.Tensor] = {
'finger_movement_penalty': finger_movement_penalty,
'finger_reach_object_reward': finger_reach_object_reward,
'pose_reward': finger_reach_object_reward,
'reward': total_reward,
}
return total_reward, reset, info
@torch.jit.script
def compute_trifinger_observations_states(
asymmetric_obs: bool,
dof_position: torch.Tensor,
dof_velocity: torch.Tensor,
object_state: torch.Tensor,
object_goal_poses: torch.Tensor,
actions: torch.Tensor,
fingertip_state: torch.Tensor,
joint_torques: torch.Tensor,
tip_wrenches: torch.Tensor
):
num_envs = dof_position.shape[0]
obs_buf = torch.cat([
dof_position,
dof_velocity,
object_state[:, 0:7], # pose
object_goal_poses,
actions
], dim=-1)
if asymmetric_obs:
states_buf = torch.cat([
obs_buf,
object_state[:, 7:13], # linear / angular velocity
fingertip_state.reshape(num_envs, -1),
joint_torques,
tip_wrenches
], dim=-1)
else:
states_buf = obs_buf
return obs_buf, states_buf
"""
Sampling of cuboidal object
"""
@torch.jit.script
def random_xy(num: int, max_com_distance_to_center: float, device: str) -> Tuple[torch.Tensor, torch.Tensor]:
"""Returns sampled uniform positions in circle (https://stackoverflow.com/a/50746409)"""
# sample radius of circle
radius = torch.sqrt(torch.rand(num, dtype=torch.float, device=device))
radius *= max_com_distance_to_center
# sample theta of point
theta = 2 * np.pi * torch.rand(num, dtype=torch.float, device=device)
# x,y-position of the cube
x = radius * torch.cos(theta)
y = radius * torch.sin(theta)
return x, y
@torch.jit.script
def random_z(num: int, min_height: float, max_height: float, device: str) -> torch.Tensor:
"""Returns sampled height of the goal object."""
z = torch.rand(num, dtype=torch.float, device=device)
z = (max_height - min_height) * z + min_height
return z
@torch.jit.script
def default_orientation(num: int, device: str) -> torch.Tensor:
"""Returns identity rotation transform."""
quat = torch.zeros((num, 4,), dtype=torch.float, device=device)
quat[..., -1] = 1.0
return quat
@torch.jit.script
def random_orientation(num: int, device: str) -> torch.Tensor:
"""Returns sampled rotation in 3D as quaternion.
Ref: https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.random.html
"""
# sample random orientation from normal distribution
quat = torch.randn((num, 4,), dtype=torch.float, device=device)
# normalize the quaternion
quat = torch.nn.functional.normalize(quat, p=2., dim=-1, eps=1e-12)
return quat
@torch.jit.script
def random_orientation_within_angle(num: int, device:str, base: torch.Tensor, max_angle: float):
""" Generates random quaternions within max_angle of base
Ref: https://math.stackexchange.com/a/3448434
"""
quat = torch.zeros((num, 4,), dtype=torch.float, device=device)
rand = torch.rand((num, 3), dtype=torch.float, device=device)
c = torch.cos(rand[:, 0]*max_angle)
n = torch.sqrt((1.-c)/2.)
quat[:, 3] = torch.sqrt((1+c)/2.)
quat[:, 2] = (rand[:, 1]*2.-1.) * n
quat[:, 0] = (torch.sqrt(1-quat[:, 2]**2.) * torch.cos(2*np.pi*rand[:, 2])) * n
quat[:, 1] = (torch.sqrt(1-quat[:, 2]**2.) * torch.sin(2*np.pi*rand[:, 2])) * n
# floating point errors can cause it to be slightly off, re-normalise
quat = torch.nn.functional.normalize(quat, p=2., dim=-1, eps=1e-12)
return quat_mul(quat, base)
@torch.jit.script
def random_angular_vel(num: int, device: str, magnitude_stdev: float) -> torch.Tensor:
"""Samples a random angular velocity with standard deviation `magnitude_stdev`"""
axis = torch.randn((num, 3,), dtype=torch.float, device=device)
axis /= torch.norm(axis, p=2, dim=-1).view(-1, 1)
magnitude = torch.randn((num, 1,), dtype=torch.float, device=device)
magnitude *= magnitude_stdev
return magnitude * axis
@torch.jit.script
def random_yaw_orientation(num: int, device: str) -> torch.Tensor:
"""Returns sampled rotation around z-axis."""
roll = torch.zeros(num, dtype=torch.float, device=device)
pitch = torch.zeros(num, dtype=torch.float, device=device)
yaw = 2 * np.pi * torch.rand(num, dtype=torch.float, device=device)
return quat_from_euler_xyz(roll, pitch, yaw)
| 70,571 | Python | 45.643754 | 217 | 0.611568 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/shadow_hand.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymtorch
from isaacgym import gymapi
from isaacgymenvs.utils.torch_jit_utils import scale, unscale, quat_mul, quat_conjugate, quat_from_angle_axis, \
to_torch, get_axis_params, torch_rand_float, tensor_clamp
from isaacgymenvs.tasks.base.vec_task import VecTask
class ShadowHand(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.randomize = self.cfg["task"]["randomize"]
self.randomization_params = self.cfg["task"]["randomization_params"]
self.aggregate_mode = self.cfg["env"]["aggregateMode"]
self.dist_reward_scale = self.cfg["env"]["distRewardScale"]
self.rot_reward_scale = self.cfg["env"]["rotRewardScale"]
self.action_penalty_scale = self.cfg["env"]["actionPenaltyScale"]
self.success_tolerance = self.cfg["env"]["successTolerance"]
self.reach_goal_bonus = self.cfg["env"]["reachGoalBonus"]
self.fall_dist = self.cfg["env"]["fallDistance"]
self.fall_penalty = self.cfg["env"]["fallPenalty"]
self.rot_eps = self.cfg["env"]["rotEps"]
self.vel_obs_scale = 0.2 # scale factor of velocity based observations
self.force_torque_obs_scale = 10.0 # scale factor of velocity based observations
self.reset_position_noise = self.cfg["env"]["resetPositionNoise"]
self.reset_rotation_noise = self.cfg["env"]["resetRotationNoise"]
self.reset_dof_pos_noise = self.cfg["env"]["resetDofPosRandomInterval"]
self.reset_dof_vel_noise = self.cfg["env"]["resetDofVelRandomInterval"]
self.force_scale = self.cfg["env"].get("forceScale", 0.0)
self.force_prob_range = self.cfg["env"].get("forceProbRange", [0.001, 0.1])
self.force_decay = self.cfg["env"].get("forceDecay", 0.99)
self.force_decay_interval = self.cfg["env"].get("forceDecayInterval", 0.08)
self.shadow_hand_dof_speed_scale = self.cfg["env"]["dofSpeedScale"]
self.use_relative_control = self.cfg["env"]["useRelativeControl"]
self.act_moving_average = self.cfg["env"]["actionsMovingAverage"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.reset_time = self.cfg["env"].get("resetTime", -1.0)
self.print_success_stat = self.cfg["env"]["printNumSuccesses"]
self.max_consecutive_successes = self.cfg["env"]["maxConsecutiveSuccesses"]
self.av_factor = self.cfg["env"].get("averFactor", 0.1)
self.object_type = self.cfg["env"]["objectType"]
assert self.object_type in ["block", "egg", "pen"]
self.ignore_z = (self.object_type == "pen")
self.asset_files_dict = {
"block": "urdf/objects/cube_multicolor.urdf",
"egg": "mjcf/open_ai_assets/hand/egg.xml",
"pen": "mjcf/open_ai_assets/hand/pen.xml"
}
if "asset" in self.cfg["env"]:
self.asset_files_dict["block"] = self.cfg["env"]["asset"].get("assetFileNameBlock", self.asset_files_dict["block"])
self.asset_files_dict["egg"] = self.cfg["env"]["asset"].get("assetFileNameEgg", self.asset_files_dict["egg"])
self.asset_files_dict["pen"] = self.cfg["env"]["asset"].get("assetFileNamePen", self.asset_files_dict["pen"])
# can be "openai", "full_no_vel", "full", "full_state"
self.obs_type = self.cfg["env"]["observationType"]
if not (self.obs_type in ["openai", "full_no_vel", "full", "full_state"]):
raise Exception(
"Unknown type of observations!\nobservationType should be one of: [openai, full_no_vel, full, full_state]")
print("Obs type:", self.obs_type)
self.num_obs_dict = {
"openai": 42,
"full_no_vel": 77,
"full": 157,
"full_state": 211
}
self.up_axis = 'z'
self.fingertips = ["robot0:ffdistal", "robot0:mfdistal", "robot0:rfdistal", "robot0:lfdistal", "robot0:thdistal"]
self.num_fingertips = len(self.fingertips)
self.use_vel_obs = False
self.fingertip_obs = True
self.asymmetric_obs = self.cfg["env"]["asymmetric_observations"]
num_states = 0
if self.asymmetric_obs:
num_states = 211
self.cfg["env"]["numObservations"] = self.num_obs_dict[self.obs_type]
self.cfg["env"]["numStates"] = num_states
self.cfg["env"]["numActions"] = 20
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
self.dt = self.sim_params.dt
control_freq_inv = self.cfg["env"].get("controlFrequencyInv", 1)
if self.reset_time > 0.0:
self.max_episode_length = int(round(self.reset_time/(control_freq_inv * self.dt)))
print("Reset time: ", self.reset_time)
print("New episode length: ", self.max_episode_length)
if self.viewer != None:
cam_pos = gymapi.Vec3(10.0, 5.0, 1.0)
cam_target = gymapi.Vec3(6.0, 5.0, 0.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym GPU state tensors
actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
rigid_body_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
if self.obs_type == "full_state" or self.asymmetric_obs:
sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, self.num_fingertips * 6)
dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs, self.num_shadow_hand_dofs)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
# create some wrapper tensors for different slices
self.shadow_hand_default_dof_pos = torch.zeros(self.num_shadow_hand_dofs, dtype=torch.float, device=self.device)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.shadow_hand_dof_state = self.dof_state.view(self.num_envs, -1, 2)[:, :self.num_shadow_hand_dofs]
self.shadow_hand_dof_pos = self.shadow_hand_dof_state[..., 0]
self.shadow_hand_dof_vel = self.shadow_hand_dof_state[..., 1]
self.rigid_body_states = gymtorch.wrap_tensor(rigid_body_tensor).view(self.num_envs, -1, 13)
self.num_bodies = self.rigid_body_states.shape[1]
self.root_state_tensor = gymtorch.wrap_tensor(actor_root_state_tensor).view(-1, 13)
self.num_dofs = self.gym.get_sim_dof_count(self.sim) // self.num_envs
self.prev_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.cur_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.global_indices = torch.arange(self.num_envs * 3, dtype=torch.int32, device=self.device).view(self.num_envs, -1)
self.x_unit_tensor = to_torch([1, 0, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.y_unit_tensor = to_torch([0, 1, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.z_unit_tensor = to_torch([0, 0, 1], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.reset_goal_buf = self.reset_buf.clone()
self.successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.consecutive_successes = torch.zeros(1, dtype=torch.float, device=self.device)
self.av_factor = to_torch(self.av_factor, dtype=torch.float, device=self.device)
self.total_successes = 0
self.total_resets = 0
# object apply random forces parameters
self.force_decay = to_torch(self.force_decay, dtype=torch.float, device=self.device)
self.force_prob_range = to_torch(self.force_prob_range, dtype=torch.float, device=self.device)
self.random_force_prob = torch.exp((torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(self.num_envs, device=self.device) + torch.log(self.force_prob_range[1]))
self.rb_forces = torch.zeros((self.num_envs, self.num_bodies, 3), dtype=torch.float, device=self.device)
def create_sim(self):
self.dt = self.cfg["sim"]["dt"]
self.up_axis_idx = 2 if self.up_axis == 'z' else 1 # index of up axis: Y=1, Z=2
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
# If randomizing, apply once immediately on startup before the fist sim step
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../assets'))
shadow_hand_asset_file = os.path.normpath("mjcf/open_ai_assets/hand/shadow_hand.xml")
if "asset" in self.cfg["env"]:
# asset_root = self.cfg["env"]["asset"].get("assetRoot", asset_root)
shadow_hand_asset_file = os.path.normpath(self.cfg["env"]["asset"].get("assetFileName", shadow_hand_asset_file))
object_asset_file = self.asset_files_dict[self.object_type]
# load shadow hand_ asset
asset_options = gymapi.AssetOptions()
asset_options.flip_visual_attachments = False
asset_options.fix_base_link = True
asset_options.collapse_fixed_joints = True
asset_options.disable_gravity = True
asset_options.thickness = 0.001
asset_options.angular_damping = 0.01
if self.physics_engine == gymapi.SIM_PHYSX:
asset_options.use_physx_armature = True
# Note - DOF mode is set in the MJCF file and loaded by Isaac Gym
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
shadow_hand_asset = self.gym.load_asset(self.sim, asset_root, shadow_hand_asset_file, asset_options)
self.num_shadow_hand_bodies = self.gym.get_asset_rigid_body_count(shadow_hand_asset)
self.num_shadow_hand_shapes = self.gym.get_asset_rigid_shape_count(shadow_hand_asset)
self.num_shadow_hand_dofs = self.gym.get_asset_dof_count(shadow_hand_asset)
self.num_shadow_hand_actuators = self.gym.get_asset_actuator_count(shadow_hand_asset)
self.num_shadow_hand_tendons = self.gym.get_asset_tendon_count(shadow_hand_asset)
# tendon set up
limit_stiffness = 30
t_damping = 0.1
relevant_tendons = ["robot0:T_FFJ1c", "robot0:T_MFJ1c", "robot0:T_RFJ1c", "robot0:T_LFJ1c"]
tendon_props = self.gym.get_asset_tendon_properties(shadow_hand_asset)
for i in range(self.num_shadow_hand_tendons):
for rt in relevant_tendons:
if self.gym.get_asset_tendon_name(shadow_hand_asset, i) == rt:
tendon_props[i].limit_stiffness = limit_stiffness
tendon_props[i].damping = t_damping
self.gym.set_asset_tendon_properties(shadow_hand_asset, tendon_props)
actuated_dof_names = [self.gym.get_asset_actuator_joint_name(shadow_hand_asset, i) for i in range(self.num_shadow_hand_actuators)]
self.actuated_dof_indices = [self.gym.find_asset_dof_index(shadow_hand_asset, name) for name in actuated_dof_names]
# get shadow_hand dof properties, loaded by Isaac Gym from the MJCF file
shadow_hand_dof_props = self.gym.get_asset_dof_properties(shadow_hand_asset)
self.shadow_hand_dof_lower_limits = []
self.shadow_hand_dof_upper_limits = []
self.shadow_hand_dof_default_pos = []
self.shadow_hand_dof_default_vel = []
for i in range(self.num_shadow_hand_dofs):
self.shadow_hand_dof_lower_limits.append(shadow_hand_dof_props['lower'][i])
self.shadow_hand_dof_upper_limits.append(shadow_hand_dof_props['upper'][i])
self.shadow_hand_dof_default_pos.append(0.0)
self.shadow_hand_dof_default_vel.append(0.0)
self.actuated_dof_indices = to_torch(self.actuated_dof_indices, dtype=torch.long, device=self.device)
self.shadow_hand_dof_lower_limits = to_torch(self.shadow_hand_dof_lower_limits, device=self.device)
self.shadow_hand_dof_upper_limits = to_torch(self.shadow_hand_dof_upper_limits, device=self.device)
self.shadow_hand_dof_default_pos = to_torch(self.shadow_hand_dof_default_pos, device=self.device)
self.shadow_hand_dof_default_vel = to_torch(self.shadow_hand_dof_default_vel, device=self.device)
self.fingertip_handles = [self.gym.find_asset_rigid_body_index(shadow_hand_asset, name) for name in self.fingertips]
# create fingertip force sensors, if needed
if self.obs_type == "full_state" or self.asymmetric_obs:
sensor_pose = gymapi.Transform()
for ft_handle in self.fingertip_handles:
self.gym.create_asset_force_sensor(shadow_hand_asset, ft_handle, sensor_pose)
# load manipulated object and goal assets
object_asset_options = gymapi.AssetOptions()
object_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options)
object_asset_options.disable_gravity = True
goal_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options)
shadow_hand_start_pose = gymapi.Transform()
shadow_hand_start_pose.p = gymapi.Vec3(*get_axis_params(0.5, self.up_axis_idx))
object_start_pose = gymapi.Transform()
object_start_pose.p = gymapi.Vec3()
object_start_pose.p.x = shadow_hand_start_pose.p.x
pose_dy, pose_dz = -0.39, 0.10
object_start_pose.p.y = shadow_hand_start_pose.p.y + pose_dy
object_start_pose.p.z = shadow_hand_start_pose.p.z + pose_dz
if self.object_type == "pen":
object_start_pose.p.z = shadow_hand_start_pose.p.z + 0.02
self.goal_displacement = gymapi.Vec3(-0.2, -0.06, 0.12)
self.goal_displacement_tensor = to_torch(
[self.goal_displacement.x, self.goal_displacement.y, self.goal_displacement.z], device=self.device)
goal_start_pose = gymapi.Transform()
goal_start_pose.p = object_start_pose.p + self.goal_displacement
goal_start_pose.p.z -= 0.04
# compute aggregate size
max_agg_bodies = self.num_shadow_hand_bodies + 2
max_agg_shapes = self.num_shadow_hand_shapes + 2
self.shadow_hands = []
self.envs = []
self.object_init_state = []
self.hand_start_states = []
self.hand_indices = []
self.fingertip_indices = []
self.object_indices = []
self.goal_object_indices = []
self.fingertip_handles = [self.gym.find_asset_rigid_body_index(shadow_hand_asset, name) for name in self.fingertips]
shadow_hand_rb_count = self.gym.get_asset_rigid_body_count(shadow_hand_asset)
object_rb_count = self.gym.get_asset_rigid_body_count(object_asset)
self.object_rb_handles = list(range(shadow_hand_rb_count, shadow_hand_rb_count + object_rb_count))
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
if self.aggregate_mode >= 1:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
# add hand - collision filter = -1 to use asset collision filters set in mjcf loader
shadow_hand_actor = self.gym.create_actor(env_ptr, shadow_hand_asset, shadow_hand_start_pose, "hand", i, -1, 0)
self.hand_start_states.append([shadow_hand_start_pose.p.x, shadow_hand_start_pose.p.y, shadow_hand_start_pose.p.z,
shadow_hand_start_pose.r.x, shadow_hand_start_pose.r.y, shadow_hand_start_pose.r.z, shadow_hand_start_pose.r.w,
0, 0, 0, 0, 0, 0])
self.gym.set_actor_dof_properties(env_ptr, shadow_hand_actor, shadow_hand_dof_props)
hand_idx = self.gym.get_actor_index(env_ptr, shadow_hand_actor, gymapi.DOMAIN_SIM)
self.hand_indices.append(hand_idx)
# enable DOF force sensors, if needed
if self.obs_type == "full_state" or self.asymmetric_obs:
self.gym.enable_actor_dof_force_sensors(env_ptr, shadow_hand_actor)
# add object
object_handle = self.gym.create_actor(env_ptr, object_asset, object_start_pose, "object", i, 0, 0)
self.object_init_state.append([object_start_pose.p.x, object_start_pose.p.y, object_start_pose.p.z,
object_start_pose.r.x, object_start_pose.r.y, object_start_pose.r.z, object_start_pose.r.w,
0, 0, 0, 0, 0, 0])
object_idx = self.gym.get_actor_index(env_ptr, object_handle, gymapi.DOMAIN_SIM)
self.object_indices.append(object_idx)
# add goal object
goal_handle = self.gym.create_actor(env_ptr, goal_asset, goal_start_pose, "goal_object", i + self.num_envs, 0, 0)
goal_object_idx = self.gym.get_actor_index(env_ptr, goal_handle, gymapi.DOMAIN_SIM)
self.goal_object_indices.append(goal_object_idx)
if self.object_type != "block":
self.gym.set_rigid_body_color(
env_ptr, object_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
self.gym.set_rigid_body_color(
env_ptr, goal_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
if self.aggregate_mode > 0:
self.gym.end_aggregate(env_ptr)
self.envs.append(env_ptr)
self.shadow_hands.append(shadow_hand_actor)
# we are not using new mass values after DR when calculating random forces applied to an object,
# which should be ok as long as the randomization range is not too big
object_rb_props = self.gym.get_actor_rigid_body_properties(env_ptr, object_handle)
self.object_rb_masses = [prop.mass for prop in object_rb_props]
self.object_init_state = to_torch(self.object_init_state, device=self.device, dtype=torch.float).view(self.num_envs, 13)
self.goal_states = self.object_init_state.clone()
self.goal_states[:, self.up_axis_idx] -= 0.04
self.goal_init_state = self.goal_states.clone()
self.hand_start_states = to_torch(self.hand_start_states, device=self.device).view(self.num_envs, 13)
self.fingertip_handles = to_torch(self.fingertip_handles, dtype=torch.long, device=self.device)
self.object_rb_handles = to_torch(self.object_rb_handles, dtype=torch.long, device=self.device)
self.object_rb_masses = to_torch(self.object_rb_masses, dtype=torch.float, device=self.device)
self.hand_indices = to_torch(self.hand_indices, dtype=torch.long, device=self.device)
self.object_indices = to_torch(self.object_indices, dtype=torch.long, device=self.device)
self.goal_object_indices = to_torch(self.goal_object_indices, dtype=torch.long, device=self.device)
def compute_reward(self, actions):
self.rew_buf[:], self.reset_buf[:], self.reset_goal_buf[:], self.progress_buf[:], self.successes[:], self.consecutive_successes[:] = compute_hand_reward(
self.rew_buf, self.reset_buf, self.reset_goal_buf, self.progress_buf, self.successes, self.consecutive_successes,
self.max_episode_length, self.object_pos, self.object_rot, self.goal_pos, self.goal_rot,
self.dist_reward_scale, self.rot_reward_scale, self.rot_eps, self.actions, self.action_penalty_scale,
self.success_tolerance, self.reach_goal_bonus, self.fall_dist, self.fall_penalty,
self.max_consecutive_successes, self.av_factor, (self.object_type == "pen")
)
self.extras['consecutive_successes'] = self.consecutive_successes.mean()
if self.print_success_stat:
self.total_resets = self.total_resets + self.reset_buf.sum()
direct_average_successes = self.total_successes + self.successes.sum()
self.total_successes = self.total_successes + (self.successes * self.reset_buf).sum()
# The direct average shows the overall result more quickly, but slightly undershoots long term
# policy performance.
print("Direct average consecutive successes = {:.1f}".format(direct_average_successes/(self.total_resets + self.num_envs)))
if self.total_resets > 0:
print("Post-Reset average consecutive successes = {:.1f}".format(self.total_successes/self.total_resets))
def compute_observations(self):
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
if self.obs_type == "full_state" or self.asymmetric_obs:
self.gym.refresh_force_sensor_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.object_pose = self.root_state_tensor[self.object_indices, 0:7]
self.object_pos = self.root_state_tensor[self.object_indices, 0:3]
self.object_rot = self.root_state_tensor[self.object_indices, 3:7]
self.object_linvel = self.root_state_tensor[self.object_indices, 7:10]
self.object_angvel = self.root_state_tensor[self.object_indices, 10:13]
self.goal_pose = self.goal_states[:, 0:7]
self.goal_pos = self.goal_states[:, 0:3]
self.goal_rot = self.goal_states[:, 3:7]
self.fingertip_state = self.rigid_body_states[:, self.fingertip_handles][:, :, 0:13]
self.fingertip_pos = self.rigid_body_states[:, self.fingertip_handles][:, :, 0:3]
if self.obs_type == "openai":
self.compute_fingertip_observations(True)
elif self.obs_type == "full_no_vel":
self.compute_full_observations(True)
elif self.obs_type == "full":
self.compute_full_observations()
elif self.obs_type == "full_state":
self.compute_full_state()
else:
print("Unknown observations type!")
if self.asymmetric_obs:
self.compute_full_state(True)
def compute_fingertip_observations(self, no_vel=False):
if no_vel:
# Per https://arxiv.org/pdf/1808.00177.pdf Table 2
# Fingertip positions
# Object Position, but not orientation
# Relative target orientation
# 3*self.num_fingertips = 15
self.obs_buf[:, 0:15] = self.fingertip_pos.reshape(self.num_envs, 15)
self.obs_buf[:, 15:18] = self.object_pose[:, 0:3]
self.obs_buf[:, 18:22] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
self.obs_buf[:, 22:42] = self.actions
else:
# 13*self.num_fingertips = 65
self.obs_buf[:, 0:65] = self.fingertip_state.reshape(self.num_envs, 65)
self.obs_buf[:, 65:72] = self.object_pose
self.obs_buf[:, 72:75] = self.object_linvel
self.obs_buf[:, 75:78] = self.vel_obs_scale * self.object_angvel
self.obs_buf[:, 78:85] = self.goal_pose
self.obs_buf[:, 85:89] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
self.obs_buf[:, 89:109] = self.actions
def compute_full_observations(self, no_vel=False):
if no_vel:
self.obs_buf[:, 0:self.num_shadow_hand_dofs] = unscale(self.shadow_hand_dof_pos,
self.shadow_hand_dof_lower_limits, self.shadow_hand_dof_upper_limits)
self.obs_buf[:, 24:31] = self.object_pose
self.obs_buf[:, 31:38] = self.goal_pose
self.obs_buf[:, 38:42] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
# 3*self.num_fingertips = 15
self.obs_buf[:, 42:57] = self.fingertip_pos.reshape(self.num_envs, 15)
self.obs_buf[:, 57:77] = self.actions
else:
self.obs_buf[:, 0:self.num_shadow_hand_dofs] = unscale(self.shadow_hand_dof_pos,
self.shadow_hand_dof_lower_limits, self.shadow_hand_dof_upper_limits)
self.obs_buf[:, self.num_shadow_hand_dofs:2*self.num_shadow_hand_dofs] = self.vel_obs_scale * self.shadow_hand_dof_vel
self.obs_buf[:, 48:55] = self.object_pose
self.obs_buf[:, 55:58] = self.object_linvel
self.obs_buf[:, 58:61] = self.vel_obs_scale * self.object_angvel
self.obs_buf[:, 61:68] = self.goal_pose
self.obs_buf[:, 68:72] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
# 13*self.num_fingertips = 65
self.obs_buf[:, 72:137] = self.fingertip_state.reshape(self.num_envs, 65)
self.obs_buf[:, 137:157] = self.actions
def compute_full_state(self, asymm_obs=False):
if asymm_obs:
self.states_buf[:, 0:self.num_shadow_hand_dofs] = unscale(self.shadow_hand_dof_pos,
self.shadow_hand_dof_lower_limits, self.shadow_hand_dof_upper_limits)
self.states_buf[:, self.num_shadow_hand_dofs:2*self.num_shadow_hand_dofs] = self.vel_obs_scale * self.shadow_hand_dof_vel
self.states_buf[:, 2*self.num_shadow_hand_dofs:3*self.num_shadow_hand_dofs] = self.force_torque_obs_scale * self.dof_force_tensor
obj_obs_start = 3*self.num_shadow_hand_dofs # 72
self.states_buf[:, obj_obs_start:obj_obs_start + 7] = self.object_pose
self.states_buf[:, obj_obs_start + 7:obj_obs_start + 10] = self.object_linvel
self.states_buf[:, obj_obs_start + 10:obj_obs_start + 13] = self.vel_obs_scale * self.object_angvel
goal_obs_start = obj_obs_start + 13 # 85
self.states_buf[:, goal_obs_start:goal_obs_start + 7] = self.goal_pose
self.states_buf[:, goal_obs_start + 7:goal_obs_start + 11] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
# fingertip observations, state(pose and vel) + force-torque sensors
num_ft_states = 13 * self.num_fingertips # 65
num_ft_force_torques = 6 * self.num_fingertips # 30
fingertip_obs_start = goal_obs_start + 11 # 96
self.states_buf[:, fingertip_obs_start:fingertip_obs_start + num_ft_states] = self.fingertip_state.reshape(self.num_envs, num_ft_states)
self.states_buf[:, fingertip_obs_start + num_ft_states:fingertip_obs_start + num_ft_states +
num_ft_force_torques] = self.force_torque_obs_scale * self.vec_sensor_tensor
# obs_end = 96 + 65 + 30 = 191
# obs_total = obs_end + num_actions = 211
obs_end = fingertip_obs_start + num_ft_states + num_ft_force_torques
self.states_buf[:, obs_end:obs_end + self.num_actions] = self.actions
else:
self.obs_buf[:, 0:self.num_shadow_hand_dofs] = unscale(self.shadow_hand_dof_pos,
self.shadow_hand_dof_lower_limits, self.shadow_hand_dof_upper_limits)
self.obs_buf[:, self.num_shadow_hand_dofs:2*self.num_shadow_hand_dofs] = self.vel_obs_scale * self.shadow_hand_dof_vel
self.obs_buf[:, 2*self.num_shadow_hand_dofs:3*self.num_shadow_hand_dofs] = self.force_torque_obs_scale * self.dof_force_tensor
obj_obs_start = 3*self.num_shadow_hand_dofs # 72
self.obs_buf[:, obj_obs_start:obj_obs_start + 7] = self.object_pose
self.obs_buf[:, obj_obs_start + 7:obj_obs_start + 10] = self.object_linvel
self.obs_buf[:, obj_obs_start + 10:obj_obs_start + 13] = self.vel_obs_scale * self.object_angvel
goal_obs_start = obj_obs_start + 13 # 85
self.obs_buf[:, goal_obs_start:goal_obs_start + 7] = self.goal_pose
self.obs_buf[:, goal_obs_start + 7:goal_obs_start + 11] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
# fingertip observations, state(pose and vel) + force-torque sensors
num_ft_states = 13 * self.num_fingertips # 65
num_ft_force_torques = 6 * self.num_fingertips # 30
fingertip_obs_start = goal_obs_start + 11 # 96
self.obs_buf[:, fingertip_obs_start:fingertip_obs_start + num_ft_states] = self.fingertip_state.reshape(self.num_envs, num_ft_states)
self.obs_buf[:, fingertip_obs_start + num_ft_states:fingertip_obs_start + num_ft_states +
num_ft_force_torques] = self.force_torque_obs_scale * self.vec_sensor_tensor
# obs_end = 96 + 65 + 30 = 191
# obs_total = obs_end + num_actions = 211
obs_end = fingertip_obs_start + num_ft_states + num_ft_force_torques
self.obs_buf[:, obs_end:obs_end + self.num_actions] = self.actions
def reset_target_pose(self, env_ids, apply_reset=False):
rand_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), 4), device=self.device)
new_rot = randomize_rotation(rand_floats[:, 0], rand_floats[:, 1], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids])
self.goal_states[env_ids, 0:3] = self.goal_init_state[env_ids, 0:3]
self.goal_states[env_ids, 3:7] = new_rot
self.root_state_tensor[self.goal_object_indices[env_ids], 0:3] = self.goal_states[env_ids, 0:3] + self.goal_displacement_tensor
self.root_state_tensor[self.goal_object_indices[env_ids], 3:7] = self.goal_states[env_ids, 3:7]
self.root_state_tensor[self.goal_object_indices[env_ids], 7:13] = torch.zeros_like(self.root_state_tensor[self.goal_object_indices[env_ids], 7:13])
if apply_reset:
goal_object_indices = self.goal_object_indices[env_ids].to(torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state_tensor),
gymtorch.unwrap_tensor(goal_object_indices), len(env_ids))
self.reset_goal_buf[env_ids] = 0
def reset_idx(self, env_ids, goal_env_ids):
# randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
# generate random values
rand_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), self.num_shadow_hand_dofs * 2 + 5), device=self.device)
# randomize start object poses
self.reset_target_pose(env_ids)
# reset rigid body forces
self.rb_forces[env_ids, :, :] = 0.0
# reset object
self.root_state_tensor[self.object_indices[env_ids]] = self.object_init_state[env_ids].clone()
self.root_state_tensor[self.object_indices[env_ids], 0:2] = self.object_init_state[env_ids, 0:2] + \
self.reset_position_noise * rand_floats[:, 0:2]
self.root_state_tensor[self.object_indices[env_ids], self.up_axis_idx] = self.object_init_state[env_ids, self.up_axis_idx] + \
self.reset_position_noise * rand_floats[:, self.up_axis_idx]
new_object_rot = randomize_rotation(rand_floats[:, 3], rand_floats[:, 4], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids])
if self.object_type == "pen":
rand_angle_y = torch.tensor(0.3)
new_object_rot = randomize_rotation_pen(rand_floats[:, 3], rand_floats[:, 4], rand_angle_y,
self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids], self.z_unit_tensor[env_ids])
self.root_state_tensor[self.object_indices[env_ids], 3:7] = new_object_rot
self.root_state_tensor[self.object_indices[env_ids], 7:13] = torch.zeros_like(self.root_state_tensor[self.object_indices[env_ids], 7:13])
object_indices = torch.unique(torch.cat([self.object_indices[env_ids],
self.goal_object_indices[env_ids],
self.goal_object_indices[goal_env_ids]]).to(torch.int32))
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state_tensor),
gymtorch.unwrap_tensor(object_indices), len(object_indices))
# reset random force probabilities
self.random_force_prob[env_ids] = torch.exp((torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(len(env_ids), device=self.device) + torch.log(self.force_prob_range[1]))
# reset shadow hand
delta_max = self.shadow_hand_dof_upper_limits - self.shadow_hand_dof_default_pos
delta_min = self.shadow_hand_dof_lower_limits - self.shadow_hand_dof_default_pos
rand_delta = delta_min + (delta_max - delta_min) * 0.5 * (rand_floats[:, 5:5+self.num_shadow_hand_dofs] + 1)
pos = self.shadow_hand_default_dof_pos + self.reset_dof_pos_noise * rand_delta
self.shadow_hand_dof_pos[env_ids, :] = pos
self.shadow_hand_dof_vel[env_ids, :] = self.shadow_hand_dof_default_vel + \
self.reset_dof_vel_noise * rand_floats[:, 5+self.num_shadow_hand_dofs:5+self.num_shadow_hand_dofs*2]
self.prev_targets[env_ids, :self.num_shadow_hand_dofs] = pos
self.cur_targets[env_ids, :self.num_shadow_hand_dofs] = pos
hand_indices = self.hand_indices[env_ids].to(torch.int32)
self.gym.set_dof_position_target_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.prev_targets),
gymtorch.unwrap_tensor(hand_indices), len(env_ids))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(hand_indices), len(env_ids))
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
self.successes[env_ids] = 0
def pre_physics_step(self, actions):
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
goal_env_ids = self.reset_goal_buf.nonzero(as_tuple=False).squeeze(-1)
# if only goals need reset, then call set API
if len(goal_env_ids) > 0 and len(env_ids) == 0:
self.reset_target_pose(goal_env_ids, apply_reset=True)
# if goals need reset in addition to other envs, call set API in reset_idx()
elif len(goal_env_ids) > 0:
self.reset_target_pose(goal_env_ids)
if len(env_ids) > 0:
self.reset_idx(env_ids, goal_env_ids)
self.actions = actions.clone().to(self.device)
if self.use_relative_control:
targets = self.prev_targets[:, self.actuated_dof_indices] + self.shadow_hand_dof_speed_scale * self.dt * self.actions
self.cur_targets[:, self.actuated_dof_indices] = tensor_clamp(targets,
self.shadow_hand_dof_lower_limits[self.actuated_dof_indices], self.shadow_hand_dof_upper_limits[self.actuated_dof_indices])
else:
self.cur_targets[:, self.actuated_dof_indices] = scale(self.actions,
self.shadow_hand_dof_lower_limits[self.actuated_dof_indices], self.shadow_hand_dof_upper_limits[self.actuated_dof_indices])
self.cur_targets[:, self.actuated_dof_indices] = self.act_moving_average * self.cur_targets[:,
self.actuated_dof_indices] + (1.0 - self.act_moving_average) * self.prev_targets[:, self.actuated_dof_indices]
self.cur_targets[:, self.actuated_dof_indices] = tensor_clamp(self.cur_targets[:, self.actuated_dof_indices],
self.shadow_hand_dof_lower_limits[self.actuated_dof_indices], self.shadow_hand_dof_upper_limits[self.actuated_dof_indices])
self.prev_targets[:, self.actuated_dof_indices] = self.cur_targets[:, self.actuated_dof_indices]
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(self.cur_targets))
if self.force_scale > 0.0:
self.rb_forces *= torch.pow(self.force_decay, self.dt / self.force_decay_interval)
# apply new forces
force_indices = (torch.rand(self.num_envs, device=self.device) < self.random_force_prob).nonzero()
self.rb_forces[force_indices, self.object_rb_handles, :] = torch.randn(
self.rb_forces[force_indices, self.object_rb_handles, :].shape, device=self.device) * self.object_rb_masses * self.force_scale
self.gym.apply_rigid_body_force_tensors(self.sim, gymtorch.unwrap_tensor(self.rb_forces), None, gymapi.LOCAL_SPACE)
def post_physics_step(self):
self.progress_buf += 1
self.randomize_buf += 1
self.compute_observations()
self.compute_reward(self.actions)
if self.viewer and self.debug_viz:
# draw axes on target object
self.gym.clear_lines(self.viewer)
self.gym.refresh_rigid_body_state_tensor(self.sim)
for i in range(self.num_envs):
targetx = (self.goal_pos[i] + quat_apply(self.goal_rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
targety = (self.goal_pos[i] + quat_apply(self.goal_rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
targetz = (self.goal_pos[i] + quat_apply(self.goal_rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.goal_pos[i].cpu().numpy() + self.goal_displacement_tensor.cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], targetx[0], targetx[1], targetx[2]], [0.85, 0.1, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], targety[0], targety[1], targety[2]], [0.1, 0.85, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], targetz[0], targetz[1], targetz[2]], [0.1, 0.1, 0.85])
objectx = (self.object_pos[i] + quat_apply(self.object_rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
objecty = (self.object_pos[i] + quat_apply(self.object_rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
objectz = (self.object_pos[i] + quat_apply(self.object_rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.object_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], objectx[0], objectx[1], objectx[2]], [0.85, 0.1, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], objecty[0], objecty[1], objecty[2]], [0.1, 0.85, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], objectz[0], objectz[1], objectz[2]], [0.1, 0.1, 0.85])
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_hand_reward(
rew_buf, reset_buf, reset_goal_buf, progress_buf, successes, consecutive_successes,
max_episode_length: float, object_pos, object_rot, target_pos, target_rot,
dist_reward_scale: float, rot_reward_scale: float, rot_eps: float,
actions, action_penalty_scale: float,
success_tolerance: float, reach_goal_bonus: float, fall_dist: float,
fall_penalty: float, max_consecutive_successes: int, av_factor: float, ignore_z_rot: bool
):
# Distance from the hand to the object
goal_dist = torch.norm(object_pos - target_pos, p=2, dim=-1)
if ignore_z_rot:
success_tolerance = 2.0 * success_tolerance
# Orientation alignment for the cube in hand and goal cube
quat_diff = quat_mul(object_rot, quat_conjugate(target_rot))
rot_dist = 2.0 * torch.asin(torch.clamp(torch.norm(quat_diff[:, 0:3], p=2, dim=-1), max=1.0))
dist_rew = goal_dist * dist_reward_scale
rot_rew = 1.0/(torch.abs(rot_dist) + rot_eps) * rot_reward_scale
action_penalty = torch.sum(actions ** 2, dim=-1)
# Total reward is: position distance + orientation alignment + action regularization + success bonus + fall penalty
reward = dist_rew + rot_rew + action_penalty * action_penalty_scale
# Find out which envs hit the goal and update successes count
goal_resets = torch.where(torch.abs(rot_dist) <= success_tolerance, torch.ones_like(reset_goal_buf), reset_goal_buf)
successes = successes + goal_resets
# Success bonus: orientation is within `success_tolerance` of goal orientation
reward = torch.where(goal_resets == 1, reward + reach_goal_bonus, reward)
# Fall penalty: distance to the goal is larger than a threshold
reward = torch.where(goal_dist >= fall_dist, reward + fall_penalty, reward)
# Check env termination conditions, including maximum success number
resets = torch.where(goal_dist >= fall_dist, torch.ones_like(reset_buf), reset_buf)
if max_consecutive_successes > 0:
# Reset progress buffer on goal envs if max_consecutive_successes > 0
progress_buf = torch.where(torch.abs(rot_dist) <= success_tolerance, torch.zeros_like(progress_buf), progress_buf)
resets = torch.where(successes >= max_consecutive_successes, torch.ones_like(resets), resets)
resets = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(resets), resets)
# Apply penalty for not reaching the goal
if max_consecutive_successes > 0:
reward = torch.where(progress_buf >= max_episode_length - 1, reward + 0.5 * fall_penalty, reward)
num_resets = torch.sum(resets)
finished_cons_successes = torch.sum(successes * resets.float())
cons_successes = torch.where(num_resets > 0, av_factor*finished_cons_successes/num_resets + (1.0 - av_factor)*consecutive_successes, consecutive_successes)
return reward, resets, goal_resets, progress_buf, successes, cons_successes
@torch.jit.script
def randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor):
return quat_mul(quat_from_angle_axis(rand0 * np.pi, x_unit_tensor),
quat_from_angle_axis(rand1 * np.pi, y_unit_tensor))
@torch.jit.script
def randomize_rotation_pen(rand0, rand1, max_angle, x_unit_tensor, y_unit_tensor, z_unit_tensor):
rot = quat_mul(quat_from_angle_axis(0.5 * np.pi + rand0 * max_angle, x_unit_tensor),
quat_from_angle_axis(rand0 * np.pi, z_unit_tensor))
return rot
| 45,910 | Python | 55.40172 | 217 | 0.624439 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/quadwalker.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymutil, gymtorch, gymapi
from .base.vec_task import VecTask
from .keyboard import Keyboard
from isaacgymenvs.utils.torch_jit_utils import to_torch, get_axis_params, torch_rand_float, quat_rotate, quat_rotate_inverse
class QuadWalker(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
# normalization
self.lin_vel_scale = self.cfg["env"]["learn"]["linearVelocityScale"]
self.ang_vel_scale = self.cfg["env"]["learn"]["angularVelocityScale"]
self.dof_vel_scale = 1/self.cfg["env"]["control"]["maxVelocity"]
self.max_dof_effort = self.cfg["env"]['control']["maxEffort"]
self.max_dof_velocity = self.cfg["env"]['control']["maxVelocity"]
self.dof_friction = self.cfg["env"]['control']["friction"]
# reward scales
self.rew_scales = {}
self.rew_scales["lin_vel_xy"] = self.cfg["env"]["learn"]["linearVelocityXYRewardScale"]
self.rew_scales["ang_vel_z"] = self.cfg["env"]["learn"]["angularVelocityZRewardScale"]
self.rew_scales["torque"] = self.cfg["env"]["learn"]["torqueRewardScale"]
self.reset_dist = self.cfg["env"]["resetDist"]
# randomization
self.randomization_params = self.cfg["task"]["randomization_params"]
self.randomize = self.cfg["task"]["randomize"]
# command ranges
self.command_x_range = self.cfg["env"]["randomCommandVelocityRanges"]["linear_x"]
self.command_y_range = self.cfg["env"]["randomCommandVelocityRanges"]["linear_y"]
self.command_yaw_range = self.cfg["env"]["randomCommandVelocityRanges"]["yaw"]
# plane params
self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"]
self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"]
self.plane_restitution = self.cfg["env"]["plane"]["restitution"]
# base init state
pos = self.cfg["env"]["baseInitState"]["pos"]
rot = self.cfg["env"]["baseInitState"]["rot"]
v_lin = self.cfg["env"]["baseInitState"]["vLinear"]
v_ang = self.cfg["env"]["baseInitState"]["vAngular"]
state = pos + rot + v_lin + v_ang
self.base_init_state = state
# default joint positions
self.named_default_joint_angles = self.cfg["env"]["defaultJointAngles"]
self.cfg["env"]["numObservations"] = 60
self.cfg["env"]["numActions"] = 12
self.Kp = self.cfg["env"]["control"]["stiffness"]
self.Kd = self.cfg["env"]["control"]["damping"]
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
# other
self.dt = self.sim_params.dt
self.max_episode_length_s = self.cfg["env"]["learn"]["episodeLength_s"]
self.max_episode_length = int(self.max_episode_length_s / self.dt + 0.5)
for key in self.rew_scales.keys():
self.rew_scales[key] *= self.dt
if self.viewer != None:
p = self.cfg["env"]["viewer"]["pos"]
lookat = self.cfg["env"]["viewer"]["lookat"]
cam_pos = gymapi.Vec3(p[0], p[1], p[2])
cam_target = gymapi.Vec3(lookat[0], lookat[1], lookat[2])
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# cam_pos = gymapi.Vec3(10.0, 9.95, 0.5)
# cam_target = gymapi.Vec3(10.0, -20.0, 0.5)
# self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym state tensors
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim)
net_contact_forces = self.gym.acquire_net_contact_force_tensor(self.sim)
torques = self.gym.acquire_dof_force_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
# create some wrapper tensors for different slices
self.root_states = gymtorch.wrap_tensor(actor_root_state)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
self.contact_forces = gymtorch.wrap_tensor(net_contact_forces).view(self.num_envs, -1, 3) # shape: num_envs, num_bodies, xyz axis
self.torques = gymtorch.wrap_tensor(torques).view(self.num_envs, self.num_dof)
self.up_axis_idx = 1 # index of up axis: Y=1, Z=2
self.gravity_vec = to_torch(get_axis_params(-1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
self.commands = torch.zeros(self.num_envs, 3, dtype=torch.float, device=self.device, requires_grad=False)
self.commands_x = self.commands.view(self.num_envs, 3)[..., 0]
self.commands_y = self.commands.view(self.num_envs, 3)[..., 1]
self.commands_yaw = self.commands.view(self.num_envs, 3)[..., 2]
self.default_dof_pos = torch.zeros_like(self.dof_pos, dtype=torch.float, device=self.device, requires_grad=False)
for i in range(self.cfg["env"]["numActions"]):
name = self.dof_names[i]
angle = self.named_default_joint_angles[name]
self.default_dof_pos[:, i] = angle
self.initial_root_states = self.root_states.clone()
self.initial_root_states[:] = to_torch(self.base_init_state, device=self.device, requires_grad=False)
self.gravity_vec = to_torch(get_axis_params(-1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
self.actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.keys = Keyboard(3)
self.reset_idx(torch.arange(self.num_envs, device=self.device))
def create_sim(self):
# set the up axis to be z-up given that assets are y-up by default
self.up_axis = self.cfg["sim"]["up_axis"]
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
# set the normal force to be z dimension
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0) if self.up_axis == 'z' else gymapi.Vec3(0.0, 1.0, 0.0)
plane_params.static_friction = self.plane_static_friction
plane_params.dynamic_friction = self.plane_dynamic_friction
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
# define plane on which environments are initialized
lower = gymapi.Vec3(-spacing, -spacing, 0.0) if self.up_axis == 'z' else gymapi.Vec3(0.5 * -spacing, 0.0, -spacing)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets")
asset_file = "urdf/QuadCoordFix/urdf/QuadCoordFix.urdf"
if "asset" in self.cfg["env"]:
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), self.cfg["env"]["asset"].get("assetRoot", asset_root))
asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
asset_options = gymapi.AssetOptions()
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_EFFORT
asset_options.collapse_fixed_joints = True
asset_options.replace_cylinder_with_capsule = True
asset_options.fix_base_link = False
asset_options.angular_damping = 0.0
asset_options.linear_damping = 0.0
asset_options.max_angular_velocity = 10000
asset_options.armature = 0.0
asset_options.thickness = 0.01
asset_options.disable_gravity = False
quadwalker_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dof = self.gym.get_asset_dof_count(quadwalker_asset)
self.body_names = self.gym.get_asset_rigid_body_names(quadwalker_asset)
self.dof_names = self.gym.get_asset_dof_names(quadwalker_asset)
print('self.num_dof')
print(self.num_dof)
print('self.body_names')
print(self.body_names)
print('self.dof_names')
print(self.dof_names)
hip_names = [s for s in self.body_names if "Hip" in s]
thigh_names = [s for s in self.body_names if "Thigh" in s]
shin_names = [s for s in self.body_names if "Shin" in s]
foot_names = [s for s in self.body_names if "Foot" in s]
self.hip_indices = torch.zeros(len(hip_names), dtype=torch.long, device=self.device, requires_grad=False)
self.thigh_indices = torch.zeros(len(thigh_names), dtype=torch.long, device=self.device, requires_grad=False)
self.shin_indices = torch.zeros(len(shin_names), dtype=torch.long, device=self.device, requires_grad=False)
self.foot_indices = torch.zeros(len(foot_names), dtype=torch.long, device=self.device, requires_grad=False)
pose = gymapi.Transform()
pose.p = gymapi.Vec3(*self.base_init_state[:3])
self.quadwalker_handles = []
self.envs = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
quadwalker_handle = self.gym.create_actor(env_ptr, quadwalker_asset, pose, "quadwalker", i, 1, 0)
rand_color = torch.rand((3), device=self.device)
self.gym.set_rigid_body_color(env_ptr, quadwalker_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(rand_color[0],rand_color[1],rand_color[2]))
rand_color = torch.rand((3), device=self.device)
self.gym.set_rigid_body_color(env_ptr, quadwalker_handle, 1, gymapi.MESH_VISUAL, gymapi.Vec3(rand_color[0],rand_color[1],rand_color[2]))
dof_props = self.gym.get_actor_dof_properties(env_ptr, quadwalker_handle)
dof_props['driveMode'][:] = gymapi.DOF_MODE_EFFORT
dof_props['stiffness'][:] = self.Kp
dof_props['damping'][:] = self.Kd
dof_props['velocity'][:] = self.max_dof_velocity
dof_props['effort'].fill(0.0)
dof_props['friction'][:] = self.dof_friction
self.gym.set_actor_dof_properties(env_ptr, quadwalker_handle, dof_props)
self.gym.enable_actor_dof_force_sensors(env_ptr, quadwalker_handle)
self.envs.append(env_ptr)
self.quadwalker_handles.append(quadwalker_handle)
for i in range(len(hip_names)):
self.hip_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.quadwalker_handles[0], hip_names[i])
for i in range(len(thigh_names)):
self.thigh_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.quadwalker_handles[0], thigh_names[i])
for i in range(len(shin_names)):
self.shin_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.quadwalker_handles[0], shin_names[i])
for i in range(len(foot_names)):
self.foot_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.quadwalker_handles[0], foot_names[i])
print(self.hip_indices)
print(self.thigh_indices)
print(self.shin_indices)
print(self.foot_indices)
def compute_reward(self):
base_quat = self.root_states[:, 3:7]
base_lin_vel = quat_rotate_inverse(base_quat, self.root_states[:, 7:10])
base_ang_vel = quat_rotate_inverse(base_quat, self.root_states[:, 10:13])
# velocity tracking reward
# lin_vel_error = torch.sum(torch.square(self.commands[:, :2] - base_lin_vel[:, :2]), dim=1)
# ang_vel_error = torch.square(self.commands[:, 2] - base_ang_vel[:, 2])
# print(base_lin_vel[0, :2])
# print('!!!')
# print(self.commands[0, :2])
# print(base_lin_vel[0, [0,2]])
# print(self.progress_buf)
self.rew_buf[:], self.reset_buf[:] = compute_quadwalker_reward(self.root_states,
self.commands,
self.torques,
self.dof_vel,
self.contact_forces,
self.reset_buf,
self.progress_buf,
self.hip_indices,
self.thigh_indices,
self.shin_indices,
self.rew_scales,
self.reset_dist,
self.max_episode_length)
# print(self.rew_buf[0])
# print(self.reset_buf)
def compute_observations(self, env_ids=None):
if env_ids is None:
env_ids = np.arange(self.num_envs)
self.gym.refresh_dof_state_tensor(self.sim) # done in step
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.obs_buf[:] = compute_quadwalker_observations( # tensors
self.root_states,
self.commands,
self.dof_pos,
self.default_dof_pos,
self.dof_vel,
self.gravity_vec,
self.actions_tensor,
# scales
self.lin_vel_scale,
self.ang_vel_scale,
self.dof_vel_scale)
# obs = torch.cat((sin_encode,
# cos_encode,
# dof_vel * dof_vel_scale,
# base_lin_vel * lin_vel_scale,
# base_ang_vel * ang_vel_scale,
# projected_gravity,
# commands_scaled,
# actions
# ), dim=-1)
# print(self.obs_buf[0, 3*self.num_dof:3*self.num_dof+9])
return self.obs_buf
def reset_idx(self, env_ids):
# Randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
positions_offset = torch_rand_float(0.5, 1.5, (len(env_ids), self.num_dof), device=self.device)
# positions_offset = torch.ones((len(env_ids), self.num_dof), device=self.device)
# velocities = torch_rand_float(-0.1, 0.1, (len(env_ids), self.num_dof), device=self.device)
velocities = torch.zeros((len(env_ids), self.num_dof), device=self.device)
self.dof_pos[env_ids] = self.default_dof_pos[env_ids] * positions_offset[:]
self.dof_vel[env_ids, :] = velocities[:]
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.initial_root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.reset_commands(env_ids)
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
def reset_commands(self, env_ids):
self.commands_x[env_ids] = torch_rand_float(self.command_x_range[0], self.command_x_range[1], (len(env_ids), 1), device=self.device).squeeze()
self.commands_y[env_ids] = torch_rand_float(self.command_y_range[0], self.command_y_range[1], (len(env_ids), 1), device=self.device).squeeze()
self.commands_yaw[env_ids] = torch_rand_float(self.command_yaw_range[0], self.command_yaw_range[1], (len(env_ids), 1), device=self.device).squeeze()
def pre_physics_step(self, actions):
self.actions_tensor = torch.zeros( [self.num_envs, self.num_dof], device=self.device, dtype=torch.float)
self.actions_tensor[:, 0:self.num_dof] = actions.to(self.device) * self.max_dof_effort
# a = self.keys.get_keys()
# scale = torch.tensor([10, self.max_dof_effort, self.max_dof_effort])
# self.actions_tensor[0,0:3] = a*scale
forces = gymtorch.unwrap_tensor(self.actions_tensor)
self.gym.set_dof_actuation_force_tensor(self.sim, forces)
# print(actions_tensor[0])
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
env_ids = torch.where(self.progress_buf % 100 == 0, torch.ones_like(self.progress_buf), torch.zeros_like(self.progress_buf)).nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_commands(env_ids)
self.compute_observations()
a = self.keys.get_keys()
scale = torch.tensor([5., 1., 0.5])
self.obs_buf[0, 45:48] = a*scale
# print(self.obs_buf[0,45:48])
self.compute_reward()
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def convert_angle(angle):
# Apply sine and cosine functions
sin_component = torch.sin(angle)
cos_component = torch.cos(angle)
# Normalize angle to [-pi, pi]
normalized_angle = torch.remainder(angle + np.pi, 2 * np.pi) - np.pi
# Apply offset
# normalized_angle += np.pi
# Normalize again if needed
# normalized_angle = torch.remainder(normalized_angle + np.pi, 2 * np.pi) - np.pi
# Normalize angle to [-1, 1]
normalized_angle /= torch.pi
return sin_component, cos_component, normalized_angle
@torch.jit.script
def compute_quadwalker_reward(
# tensors
root_states,
commands,
torques,
dof_vel,
contact_forces,
reset_buf,
progress_buf,
hip_idx,
thigh_idx,
shin_idx,
# Dict
rew_scales,
# other
reset_dist,
max_episode_length):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Dict[str, float], float, float) -> Tuple[Tensor, Tensor]
# prepare quantities (TODO: return from obs ?)
base_quat = root_states[:, 3:7]
base_lin_vel = quat_rotate_inverse(base_quat, root_states[:, 7:10])
base_ang_vel = quat_rotate_inverse(base_quat, root_states[:, 10:13])
# velocity tracking reward
lin_vel_error = torch.sum(torch.square(commands[:, :2] - base_lin_vel[:, :2]), dim=1)
ang_vel_error = torch.square(commands[:, 2] - base_ang_vel[:, 2])
rew_lin_vel_xy = torch.exp(-lin_vel_error/0.25) * rew_scales["lin_vel_xy"]
rew_ang_vel_z = torch.exp(-ang_vel_error/0.25) * rew_scales["ang_vel_z"]
# torque penalty
rew_torque = torch.sum(torch.square(torques), dim=1) * rew_scales["torque"]
# joint speed penalty
# rew_joint_speed = torch.sum(torch.square(dof_vel), dim=1) * rew_scales["torque"]/12
total_reward = rew_lin_vel_xy + rew_ang_vel_z + rew_torque
total_reward = torch.clip(total_reward, 0., None)
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset_buf)
# This is a hacky fix, the contact forces sometimes don't update when an environment resets causing a double reset.
# This waits 10 environment steps before factoring in contact forces
check_forces = torch.where(progress_buf >= 10, torch.ones_like(reset_buf), reset_buf)
reset = reset | ((torch.norm(contact_forces[:, 0, :], dim=1) > 1.) & check_forces) # Body Collision
reset = reset | ((torch.any(torch.norm(contact_forces[:, hip_idx, :], dim=2) > 1., dim=1)) & check_forces)
reset = reset | ((torch.any(torch.norm(contact_forces[:, thigh_idx, :], dim=2) > 1., dim=1)) & check_forces)
# reset = reset | (torch.any(torch.norm(contact_forces[:, shin_idx, :], dim=2) > 1., dim=1))
return total_reward.detach(), reset
@torch.jit.script
def compute_quadwalker_observations(root_states,
commands,
dof_pos,
default_dof_pos,
dof_vel,
gravity_vec,
actions,
lin_vel_scale,
ang_vel_scale,
dof_vel_scale
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, float, float, float) -> Tensor
base_quat = root_states[:, 3:7]
base_lin_vel = quat_rotate_inverse(base_quat, root_states[:, 7:10])
base_ang_vel = quat_rotate_inverse(base_quat, root_states[:, 10:13])
projected_gravity = quat_rotate(base_quat, gravity_vec)
commands_scaled = commands*torch.tensor([lin_vel_scale, lin_vel_scale, ang_vel_scale], requires_grad=False, device=commands.device)
sin_encode, cos_encode, motor_angle = convert_angle(dof_pos.squeeze())
obs = torch.cat((sin_encode, #12 (0:12)
cos_encode, #12 (12:24)
dof_vel * dof_vel_scale, #12 (24:36)
base_lin_vel * lin_vel_scale, #3 (36:39)
base_ang_vel * ang_vel_scale, #3 (39:42)
projected_gravity, #3 (42:45)
commands_scaled, #3 (45:48)
actions #12 (48:60)
), dim=-1)
return obs
| 25,614 | Python | 47.330189 | 217 | 0.569454 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/franka_cabinet.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymutil, gymtorch, gymapi
from isaacgymenvs.utils.torch_jit_utils import to_torch, get_axis_params, tensor_clamp, \
tf_vector, tf_combine
from .base.vec_task import VecTask
class FrankaCabinet(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.action_scale = self.cfg["env"]["actionScale"]
self.start_position_noise = self.cfg["env"]["startPositionNoise"]
self.start_rotation_noise = self.cfg["env"]["startRotationNoise"]
self.num_props = self.cfg["env"]["numProps"]
self.aggregate_mode = self.cfg["env"]["aggregateMode"]
self.dof_vel_scale = self.cfg["env"]["dofVelocityScale"]
self.dist_reward_scale = self.cfg["env"]["distRewardScale"]
self.rot_reward_scale = self.cfg["env"]["rotRewardScale"]
self.around_handle_reward_scale = self.cfg["env"]["aroundHandleRewardScale"]
self.open_reward_scale = self.cfg["env"]["openRewardScale"]
self.finger_dist_reward_scale = self.cfg["env"]["fingerDistRewardScale"]
self.action_penalty_scale = self.cfg["env"]["actionPenaltyScale"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.up_axis = "z"
self.up_axis_idx = 2
self.distX_offset = 0.04
self.dt = 1/60.
# prop dimensions
self.prop_width = 0.08
self.prop_height = 0.08
self.prop_length = 0.08
self.prop_spacing = 0.09
num_obs = 23
num_acts = 9
self.cfg["env"]["numObservations"] = 23
self.cfg["env"]["numActions"] = 9
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
# get gym GPU state tensors
actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
rigid_body_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
# create some wrapper tensors for different slices
self.franka_default_dof_pos = to_torch([1.157, -1.066, -0.155, -2.239, -1.841, 1.003, 0.469, 0.035, 0.035], device=self.device)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.franka_dof_state = self.dof_state.view(self.num_envs, -1, 2)[:, :self.num_franka_dofs]
self.franka_dof_pos = self.franka_dof_state[..., 0]
self.franka_dof_vel = self.franka_dof_state[..., 1]
self.cabinet_dof_state = self.dof_state.view(self.num_envs, -1, 2)[:, self.num_franka_dofs:]
self.cabinet_dof_pos = self.cabinet_dof_state[..., 0]
self.cabinet_dof_vel = self.cabinet_dof_state[..., 1]
self.rigid_body_states = gymtorch.wrap_tensor(rigid_body_tensor).view(self.num_envs, -1, 13)
self.num_bodies = self.rigid_body_states.shape[1]
self.root_state_tensor = gymtorch.wrap_tensor(actor_root_state_tensor).view(self.num_envs, -1, 13)
if self.num_props > 0:
self.prop_states = self.root_state_tensor[:, 2:]
self.num_dofs = self.gym.get_sim_dof_count(self.sim) // self.num_envs
self.franka_dof_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.global_indices = torch.arange(self.num_envs * (2 + self.num_props), dtype=torch.int32, device=self.device).view(self.num_envs, -1)
self.reset_idx(torch.arange(self.num_envs, device=self.device))
def create_sim(self):
self.sim_params.up_axis = gymapi.UP_AXIS_Z
self.sim_params.gravity.x = 0
self.sim_params.gravity.y = 0
self.sim_params.gravity.z = -9.81
self.sim = super().create_sim(
self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets")
franka_asset_file = "urdf/franka_description/robots/franka_panda.urdf"
cabinet_asset_file = "urdf/sektion_cabinet_model/urdf/sektion_cabinet_2.urdf"
if "asset" in self.cfg["env"]:
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), self.cfg["env"]["asset"].get("assetRoot", asset_root))
franka_asset_file = self.cfg["env"]["asset"].get("assetFileNameFranka", franka_asset_file)
cabinet_asset_file = self.cfg["env"]["asset"].get("assetFileNameCabinet", cabinet_asset_file)
# load franka asset
asset_options = gymapi.AssetOptions()
asset_options.flip_visual_attachments = True
asset_options.fix_base_link = True
asset_options.collapse_fixed_joints = True
asset_options.disable_gravity = True
asset_options.thickness = 0.001
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS
asset_options.use_mesh_materials = True
franka_asset = self.gym.load_asset(self.sim, asset_root, franka_asset_file, asset_options)
# load cabinet asset
asset_options.flip_visual_attachments = False
asset_options.collapse_fixed_joints = True
asset_options.disable_gravity = False
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
asset_options.armature = 0.005
cabinet_asset = self.gym.load_asset(self.sim, asset_root, cabinet_asset_file, asset_options)
franka_dof_stiffness = to_torch([400, 400, 400, 400, 400, 400, 400, 1.0e6, 1.0e6], dtype=torch.float, device=self.device)
franka_dof_damping = to_torch([80, 80, 80, 80, 80, 80, 80, 1.0e2, 1.0e2], dtype=torch.float, device=self.device)
self.num_franka_bodies = self.gym.get_asset_rigid_body_count(franka_asset)
self.num_franka_dofs = self.gym.get_asset_dof_count(franka_asset)
self.num_cabinet_bodies = self.gym.get_asset_rigid_body_count(cabinet_asset)
self.num_cabinet_dofs = self.gym.get_asset_dof_count(cabinet_asset)
print("num franka bodies: ", self.num_franka_bodies)
print("num franka dofs: ", self.num_franka_dofs)
print("num cabinet bodies: ", self.num_cabinet_bodies)
print("num cabinet dofs: ", self.num_cabinet_dofs)
# set franka dof properties
franka_dof_props = self.gym.get_asset_dof_properties(franka_asset)
self.franka_dof_lower_limits = []
self.franka_dof_upper_limits = []
for i in range(self.num_franka_dofs):
franka_dof_props['driveMode'][i] = gymapi.DOF_MODE_POS
if self.physics_engine == gymapi.SIM_PHYSX:
franka_dof_props['stiffness'][i] = franka_dof_stiffness[i]
franka_dof_props['damping'][i] = franka_dof_damping[i]
else:
franka_dof_props['stiffness'][i] = 7000.0
franka_dof_props['damping'][i] = 50.0
self.franka_dof_lower_limits.append(franka_dof_props['lower'][i])
self.franka_dof_upper_limits.append(franka_dof_props['upper'][i])
self.franka_dof_lower_limits = to_torch(self.franka_dof_lower_limits, device=self.device)
self.franka_dof_upper_limits = to_torch(self.franka_dof_upper_limits, device=self.device)
self.franka_dof_speed_scales = torch.ones_like(self.franka_dof_lower_limits)
self.franka_dof_speed_scales[[7, 8]] = 0.1
franka_dof_props['effort'][7] = 200
franka_dof_props['effort'][8] = 200
# set cabinet dof properties
cabinet_dof_props = self.gym.get_asset_dof_properties(cabinet_asset)
for i in range(self.num_cabinet_dofs):
cabinet_dof_props['damping'][i] = 10.0
# create prop assets
box_opts = gymapi.AssetOptions()
box_opts.density = 400
prop_asset = self.gym.create_box(self.sim, self.prop_width, self.prop_height, self.prop_width, box_opts)
franka_start_pose = gymapi.Transform()
franka_start_pose.p = gymapi.Vec3(1.0, 0.0, 0.0)
franka_start_pose.r = gymapi.Quat(0.0, 0.0, 1.0, 0.0)
cabinet_start_pose = gymapi.Transform()
cabinet_start_pose.p = gymapi.Vec3(*get_axis_params(0.4, self.up_axis_idx))
# compute aggregate size
num_franka_bodies = self.gym.get_asset_rigid_body_count(franka_asset)
num_franka_shapes = self.gym.get_asset_rigid_shape_count(franka_asset)
num_cabinet_bodies = self.gym.get_asset_rigid_body_count(cabinet_asset)
num_cabinet_shapes = self.gym.get_asset_rigid_shape_count(cabinet_asset)
num_prop_bodies = self.gym.get_asset_rigid_body_count(prop_asset)
num_prop_shapes = self.gym.get_asset_rigid_shape_count(prop_asset)
max_agg_bodies = num_franka_bodies + num_cabinet_bodies + self.num_props * num_prop_bodies
max_agg_shapes = num_franka_shapes + num_cabinet_shapes + self.num_props * num_prop_shapes
self.frankas = []
self.cabinets = []
self.default_prop_states = []
self.prop_start = []
self.envs = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
if self.aggregate_mode >= 3:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
franka_actor = self.gym.create_actor(env_ptr, franka_asset, franka_start_pose, "franka", i, 1, 0)
self.gym.set_actor_dof_properties(env_ptr, franka_actor, franka_dof_props)
if self.aggregate_mode == 2:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
cabinet_pose = cabinet_start_pose
cabinet_pose.p.x += self.start_position_noise * (np.random.rand() - 0.5)
dz = 0.5 * np.random.rand()
dy = np.random.rand() - 0.5
cabinet_pose.p.y += self.start_position_noise * dy
cabinet_pose.p.z += self.start_position_noise * dz
cabinet_actor = self.gym.create_actor(env_ptr, cabinet_asset, cabinet_pose, "cabinet", i, 2, 0)
self.gym.set_actor_dof_properties(env_ptr, cabinet_actor, cabinet_dof_props)
if self.aggregate_mode == 1:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
if self.num_props > 0:
self.prop_start.append(self.gym.get_sim_actor_count(self.sim))
drawer_handle = self.gym.find_actor_rigid_body_handle(env_ptr, cabinet_actor, "drawer_top")
drawer_pose = self.gym.get_rigid_transform(env_ptr, drawer_handle)
props_per_row = int(np.ceil(np.sqrt(self.num_props)))
xmin = -0.5 * self.prop_spacing * (props_per_row - 1)
yzmin = -0.5 * self.prop_spacing * (props_per_row - 1)
prop_count = 0
for j in range(props_per_row):
prop_up = yzmin + j * self.prop_spacing
for k in range(props_per_row):
if prop_count >= self.num_props:
break
propx = xmin + k * self.prop_spacing
prop_state_pose = gymapi.Transform()
prop_state_pose.p.x = drawer_pose.p.x + propx
propz, propy = 0, prop_up
prop_state_pose.p.y = drawer_pose.p.y + propy
prop_state_pose.p.z = drawer_pose.p.z + propz
prop_state_pose.r = gymapi.Quat(0, 0, 0, 1)
prop_handle = self.gym.create_actor(env_ptr, prop_asset, prop_state_pose, "prop{}".format(prop_count), i, 0, 0)
prop_count += 1
prop_idx = j * props_per_row + k
self.default_prop_states.append([prop_state_pose.p.x, prop_state_pose.p.y, prop_state_pose.p.z,
prop_state_pose.r.x, prop_state_pose.r.y, prop_state_pose.r.z, prop_state_pose.r.w,
0, 0, 0, 0, 0, 0])
if self.aggregate_mode > 0:
self.gym.end_aggregate(env_ptr)
self.envs.append(env_ptr)
self.frankas.append(franka_actor)
self.cabinets.append(cabinet_actor)
self.hand_handle = self.gym.find_actor_rigid_body_handle(env_ptr, franka_actor, "panda_link7")
self.drawer_handle = self.gym.find_actor_rigid_body_handle(env_ptr, cabinet_actor, "drawer_top")
self.lfinger_handle = self.gym.find_actor_rigid_body_handle(env_ptr, franka_actor, "panda_leftfinger")
self.rfinger_handle = self.gym.find_actor_rigid_body_handle(env_ptr, franka_actor, "panda_rightfinger")
self.default_prop_states = to_torch(self.default_prop_states, device=self.device, dtype=torch.float).view(self.num_envs, self.num_props, 13)
self.init_data()
def init_data(self):
hand = self.gym.find_actor_rigid_body_handle(self.envs[0], self.frankas[0], "panda_link7")
lfinger = self.gym.find_actor_rigid_body_handle(self.envs[0], self.frankas[0], "panda_leftfinger")
rfinger = self.gym.find_actor_rigid_body_handle(self.envs[0], self.frankas[0], "panda_rightfinger")
hand_pose = self.gym.get_rigid_transform(self.envs[0], hand)
lfinger_pose = self.gym.get_rigid_transform(self.envs[0], lfinger)
rfinger_pose = self.gym.get_rigid_transform(self.envs[0], rfinger)
finger_pose = gymapi.Transform()
finger_pose.p = (lfinger_pose.p + rfinger_pose.p) * 0.5
finger_pose.r = lfinger_pose.r
hand_pose_inv = hand_pose.inverse()
grasp_pose_axis = 1
franka_local_grasp_pose = hand_pose_inv * finger_pose
franka_local_grasp_pose.p += gymapi.Vec3(*get_axis_params(0.04, grasp_pose_axis))
self.franka_local_grasp_pos = to_torch([franka_local_grasp_pose.p.x, franka_local_grasp_pose.p.y,
franka_local_grasp_pose.p.z], device=self.device).repeat((self.num_envs, 1))
self.franka_local_grasp_rot = to_torch([franka_local_grasp_pose.r.x, franka_local_grasp_pose.r.y,
franka_local_grasp_pose.r.z, franka_local_grasp_pose.r.w], device=self.device).repeat((self.num_envs, 1))
drawer_local_grasp_pose = gymapi.Transform()
drawer_local_grasp_pose.p = gymapi.Vec3(*get_axis_params(0.01, grasp_pose_axis, 0.3))
drawer_local_grasp_pose.r = gymapi.Quat(0, 0, 0, 1)
self.drawer_local_grasp_pos = to_torch([drawer_local_grasp_pose.p.x, drawer_local_grasp_pose.p.y,
drawer_local_grasp_pose.p.z], device=self.device).repeat((self.num_envs, 1))
self.drawer_local_grasp_rot = to_torch([drawer_local_grasp_pose.r.x, drawer_local_grasp_pose.r.y,
drawer_local_grasp_pose.r.z, drawer_local_grasp_pose.r.w], device=self.device).repeat((self.num_envs, 1))
self.gripper_forward_axis = to_torch([0, 0, 1], device=self.device).repeat((self.num_envs, 1))
self.drawer_inward_axis = to_torch([-1, 0, 0], device=self.device).repeat((self.num_envs, 1))
self.gripper_up_axis = to_torch([0, 1, 0], device=self.device).repeat((self.num_envs, 1))
self.drawer_up_axis = to_torch([0, 0, 1], device=self.device).repeat((self.num_envs, 1))
self.franka_grasp_pos = torch.zeros_like(self.franka_local_grasp_pos)
self.franka_grasp_rot = torch.zeros_like(self.franka_local_grasp_rot)
self.franka_grasp_rot[..., -1] = 1 # xyzw
self.drawer_grasp_pos = torch.zeros_like(self.drawer_local_grasp_pos)
self.drawer_grasp_rot = torch.zeros_like(self.drawer_local_grasp_rot)
self.drawer_grasp_rot[..., -1] = 1
self.franka_lfinger_pos = torch.zeros_like(self.franka_local_grasp_pos)
self.franka_rfinger_pos = torch.zeros_like(self.franka_local_grasp_pos)
self.franka_lfinger_rot = torch.zeros_like(self.franka_local_grasp_rot)
self.franka_rfinger_rot = torch.zeros_like(self.franka_local_grasp_rot)
def compute_reward(self, actions):
self.rew_buf[:], self.reset_buf[:] = compute_franka_reward(
self.reset_buf, self.progress_buf, self.actions, self.cabinet_dof_pos,
self.franka_grasp_pos, self.drawer_grasp_pos, self.franka_grasp_rot, self.drawer_grasp_rot,
self.franka_lfinger_pos, self.franka_rfinger_pos,
self.gripper_forward_axis, self.drawer_inward_axis, self.gripper_up_axis, self.drawer_up_axis,
self.num_envs, self.dist_reward_scale, self.rot_reward_scale, self.around_handle_reward_scale, self.open_reward_scale,
self.finger_dist_reward_scale, self.action_penalty_scale, self.distX_offset, self.max_episode_length
)
def compute_observations(self):
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
hand_pos = self.rigid_body_states[:, self.hand_handle][:, 0:3]
hand_rot = self.rigid_body_states[:, self.hand_handle][:, 3:7]
drawer_pos = self.rigid_body_states[:, self.drawer_handle][:, 0:3]
drawer_rot = self.rigid_body_states[:, self.drawer_handle][:, 3:7]
self.franka_grasp_rot[:], self.franka_grasp_pos[:], self.drawer_grasp_rot[:], self.drawer_grasp_pos[:] = \
compute_grasp_transforms(hand_rot, hand_pos, self.franka_local_grasp_rot, self.franka_local_grasp_pos,
drawer_rot, drawer_pos, self.drawer_local_grasp_rot, self.drawer_local_grasp_pos
)
self.franka_lfinger_pos = self.rigid_body_states[:, self.lfinger_handle][:, 0:3]
self.franka_rfinger_pos = self.rigid_body_states[:, self.rfinger_handle][:, 0:3]
self.franka_lfinger_rot = self.rigid_body_states[:, self.lfinger_handle][:, 3:7]
self.franka_rfinger_rot = self.rigid_body_states[:, self.rfinger_handle][:, 3:7]
dof_pos_scaled = (2.0 * (self.franka_dof_pos - self.franka_dof_lower_limits)
/ (self.franka_dof_upper_limits - self.franka_dof_lower_limits) - 1.0)
to_target = self.drawer_grasp_pos - self.franka_grasp_pos
self.obs_buf = torch.cat((dof_pos_scaled, self.franka_dof_vel * self.dof_vel_scale, to_target,
self.cabinet_dof_pos[:, 3].unsqueeze(-1), self.cabinet_dof_vel[:, 3].unsqueeze(-1)), dim=-1)
return self.obs_buf
def reset_idx(self, env_ids):
env_ids_int32 = env_ids.to(dtype=torch.int32)
# reset franka
pos = tensor_clamp(
self.franka_default_dof_pos.unsqueeze(0) + 0.25 * (torch.rand((len(env_ids), self.num_franka_dofs), device=self.device) - 0.5),
self.franka_dof_lower_limits, self.franka_dof_upper_limits)
self.franka_dof_pos[env_ids, :] = pos
self.franka_dof_vel[env_ids, :] = torch.zeros_like(self.franka_dof_vel[env_ids])
self.franka_dof_targets[env_ids, :self.num_franka_dofs] = pos
# reset cabinet
self.cabinet_dof_state[env_ids, :] = torch.zeros_like(self.cabinet_dof_state[env_ids])
# reset props
if self.num_props > 0:
prop_indices = self.global_indices[env_ids, 2:].flatten()
self.prop_states[env_ids] = self.default_prop_states[env_ids]
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state_tensor),
gymtorch.unwrap_tensor(prop_indices), len(prop_indices))
multi_env_ids_int32 = self.global_indices[env_ids, :2].flatten()
self.gym.set_dof_position_target_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.franka_dof_targets),
gymtorch.unwrap_tensor(multi_env_ids_int32), len(multi_env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(multi_env_ids_int32), len(multi_env_ids_int32))
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
def pre_physics_step(self, actions):
self.actions = actions.clone().to(self.device)
targets = self.franka_dof_targets[:, :self.num_franka_dofs] + self.franka_dof_speed_scales * self.dt * self.actions * self.action_scale
self.franka_dof_targets[:, :self.num_franka_dofs] = tensor_clamp(
targets, self.franka_dof_lower_limits, self.franka_dof_upper_limits)
env_ids_int32 = torch.arange(self.num_envs, dtype=torch.int32, device=self.device)
self.gym.set_dof_position_target_tensor(self.sim,
gymtorch.unwrap_tensor(self.franka_dof_targets))
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
self.compute_reward(self.actions)
# debug viz
if self.viewer and self.debug_viz:
self.gym.clear_lines(self.viewer)
self.gym.refresh_rigid_body_state_tensor(self.sim)
for i in range(self.num_envs):
px = (self.franka_grasp_pos[i] + quat_apply(self.franka_grasp_rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
py = (self.franka_grasp_pos[i] + quat_apply(self.franka_grasp_rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
pz = (self.franka_grasp_pos[i] + quat_apply(self.franka_grasp_rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.franka_grasp_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], px[0], px[1], px[2]], [0.85, 0.1, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], py[0], py[1], py[2]], [0.1, 0.85, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], pz[0], pz[1], pz[2]], [0.1, 0.1, 0.85])
px = (self.drawer_grasp_pos[i] + quat_apply(self.drawer_grasp_rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
py = (self.drawer_grasp_pos[i] + quat_apply(self.drawer_grasp_rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
pz = (self.drawer_grasp_pos[i] + quat_apply(self.drawer_grasp_rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.drawer_grasp_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], px[0], px[1], px[2]], [1, 0, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], py[0], py[1], py[2]], [0, 1, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], pz[0], pz[1], pz[2]], [0, 0, 1])
px = (self.franka_lfinger_pos[i] + quat_apply(self.franka_lfinger_rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
py = (self.franka_lfinger_pos[i] + quat_apply(self.franka_lfinger_rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
pz = (self.franka_lfinger_pos[i] + quat_apply(self.franka_lfinger_rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.franka_lfinger_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], px[0], px[1], px[2]], [1, 0, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], py[0], py[1], py[2]], [0, 1, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], pz[0], pz[1], pz[2]], [0, 0, 1])
px = (self.franka_rfinger_pos[i] + quat_apply(self.franka_rfinger_rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
py = (self.franka_rfinger_pos[i] + quat_apply(self.franka_rfinger_rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
pz = (self.franka_rfinger_pos[i] + quat_apply(self.franka_rfinger_rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.franka_rfinger_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], px[0], px[1], px[2]], [1, 0, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], py[0], py[1], py[2]], [0, 1, 0])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], pz[0], pz[1], pz[2]], [0, 0, 1])
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_franka_reward(
reset_buf, progress_buf, actions, cabinet_dof_pos,
franka_grasp_pos, drawer_grasp_pos, franka_grasp_rot, drawer_grasp_rot,
franka_lfinger_pos, franka_rfinger_pos,
gripper_forward_axis, drawer_inward_axis, gripper_up_axis, drawer_up_axis,
num_envs, dist_reward_scale, rot_reward_scale, around_handle_reward_scale, open_reward_scale,
finger_dist_reward_scale, action_penalty_scale, distX_offset, max_episode_length
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, int, float, float, float, float, float, float, float, float) -> Tuple[Tensor, Tensor]
# distance from hand to the drawer
d = torch.norm(franka_grasp_pos - drawer_grasp_pos, p=2, dim=-1)
dist_reward = 1.0 / (1.0 + d ** 2)
dist_reward *= dist_reward
dist_reward = torch.where(d <= 0.02, dist_reward * 2, dist_reward)
axis1 = tf_vector(franka_grasp_rot, gripper_forward_axis)
axis2 = tf_vector(drawer_grasp_rot, drawer_inward_axis)
axis3 = tf_vector(franka_grasp_rot, gripper_up_axis)
axis4 = tf_vector(drawer_grasp_rot, drawer_up_axis)
dot1 = torch.bmm(axis1.view(num_envs, 1, 3), axis2.view(num_envs, 3, 1)).squeeze(-1).squeeze(-1) # alignment of forward axis for gripper
dot2 = torch.bmm(axis3.view(num_envs, 1, 3), axis4.view(num_envs, 3, 1)).squeeze(-1).squeeze(-1) # alignment of up axis for gripper
# reward for matching the orientation of the hand to the drawer (fingers wrapped)
rot_reward = 0.5 * (torch.sign(dot1) * dot1 ** 2 + torch.sign(dot2) * dot2 ** 2)
# bonus if left finger is above the drawer handle and right below
around_handle_reward = torch.zeros_like(rot_reward)
around_handle_reward = torch.where(franka_lfinger_pos[:, 2] > drawer_grasp_pos[:, 2],
torch.where(franka_rfinger_pos[:, 2] < drawer_grasp_pos[:, 2],
around_handle_reward + 0.5, around_handle_reward), around_handle_reward)
# reward for distance of each finger from the drawer
finger_dist_reward = torch.zeros_like(rot_reward)
lfinger_dist = torch.abs(franka_lfinger_pos[:, 2] - drawer_grasp_pos[:, 2])
rfinger_dist = torch.abs(franka_rfinger_pos[:, 2] - drawer_grasp_pos[:, 2])
finger_dist_reward = torch.where(franka_lfinger_pos[:, 2] > drawer_grasp_pos[:, 2],
torch.where(franka_rfinger_pos[:, 2] < drawer_grasp_pos[:, 2],
(0.04 - lfinger_dist) + (0.04 - rfinger_dist), finger_dist_reward), finger_dist_reward)
# regularization on the actions (summed for each environment)
action_penalty = torch.sum(actions ** 2, dim=-1)
# how far the cabinet has been opened out
open_reward = cabinet_dof_pos[:, 3] * around_handle_reward + cabinet_dof_pos[:, 3] # drawer_top_joint
rewards = dist_reward_scale * dist_reward + rot_reward_scale * rot_reward \
+ around_handle_reward_scale * around_handle_reward + open_reward_scale * open_reward \
+ finger_dist_reward_scale * finger_dist_reward - action_penalty_scale * action_penalty
# bonus for opening drawer properly
rewards = torch.where(cabinet_dof_pos[:, 3] > 0.01, rewards + 0.5, rewards)
rewards = torch.where(cabinet_dof_pos[:, 3] > 0.2, rewards + around_handle_reward, rewards)
rewards = torch.where(cabinet_dof_pos[:, 3] > 0.39, rewards + (2.0 * around_handle_reward), rewards)
# prevent bad style in opening drawer
rewards = torch.where(franka_lfinger_pos[:, 0] < drawer_grasp_pos[:, 0] - distX_offset,
torch.ones_like(rewards) * -1, rewards)
rewards = torch.where(franka_rfinger_pos[:, 0] < drawer_grasp_pos[:, 0] - distX_offset,
torch.ones_like(rewards) * -1, rewards)
# reset if drawer is open or max length reached
reset_buf = torch.where(cabinet_dof_pos[:, 3] > 0.39, torch.ones_like(reset_buf), reset_buf)
reset_buf = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset_buf)
return rewards, reset_buf
@torch.jit.script
def compute_grasp_transforms(hand_rot, hand_pos, franka_local_grasp_rot, franka_local_grasp_pos,
drawer_rot, drawer_pos, drawer_local_grasp_rot, drawer_local_grasp_pos
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]
global_franka_rot, global_franka_pos = tf_combine(
hand_rot, hand_pos, franka_local_grasp_rot, franka_local_grasp_pos)
global_drawer_rot, global_drawer_pos = tf_combine(
drawer_rot, drawer_pos, drawer_local_grasp_rot, drawer_local_grasp_pos)
return global_franka_rot, global_franka_pos, global_drawer_rot, global_drawer_pos
| 32,782 | Python | 56.716549 | 217 | 0.613141 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/__init__.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from .ant import Ant
from .anymal import Anymal
from .anymal_terrain import AnymalTerrain
from .ball_balance import BallBalance
from .cartpole import Cartpole
from .factory.factory_task_gears import FactoryTaskGears
from .factory.factory_task_insertion import FactoryTaskInsertion
from .factory.factory_task_nut_bolt_pick import FactoryTaskNutBoltPick
from .factory.factory_task_nut_bolt_place import FactoryTaskNutBoltPlace
from .factory.factory_task_nut_bolt_screw import FactoryTaskNutBoltScrew
from .franka_cabinet import FrankaCabinet
from .franka_cube_stack import FrankaCubeStack
from .humanoid import Humanoid
from .humanoid_amp import HumanoidAMP
from .ingenuity import Ingenuity
from .quadcopter import Quadcopter
from .shadow_hand import ShadowHand
from .allegro_hand import AllegroHand
from .dextreme.allegro_hand_dextreme import AllegroHandDextremeManualDR, AllegroHandDextremeADR
from .trifinger import Trifinger
from .torquepole import TorquePole
from .jumpy import Jumpy
from .quadjumpy import QuadJumpy
from .quadwalker import QuadWalker
from .bipedwalker import BipedWalker
from .allegro_kuka.allegro_kuka_reorientation import AllegroKukaReorientation
from .allegro_kuka.allegro_kuka_regrasping import AllegroKukaRegrasping
from .allegro_kuka.allegro_kuka_throw import AllegroKukaThrow
from .allegro_kuka.allegro_kuka_two_arms_regrasping import AllegroKukaTwoArmsRegrasping
from .allegro_kuka.allegro_kuka_two_arms_reorientation import AllegroKukaTwoArmsReorientation
from .industreal.industreal_task_pegs_insert import IndustRealTaskPegsInsert
from .industreal.industreal_task_gears_insert import IndustRealTaskGearsInsert
def resolve_allegro_kuka(cfg, *args, **kwargs):
subtask_name: str = cfg["env"]["subtask"]
subtask_map = dict(
reorientation=AllegroKukaReorientation,
throw=AllegroKukaThrow,
regrasping=AllegroKukaRegrasping,
)
if subtask_name not in subtask_map:
print("!!!!!")
raise ValueError(f"Unknown subtask={subtask_name} in {subtask_map}")
return subtask_map[subtask_name](cfg, *args, **kwargs)
def resolve_allegro_kuka_two_arms(cfg, *args, **kwargs):
subtask_name: str = cfg["env"]["subtask"]
subtask_map = dict(
reorientation=AllegroKukaTwoArmsReorientation,
regrasping=AllegroKukaTwoArmsRegrasping,
)
if subtask_name not in subtask_map:
raise ValueError(f"Unknown subtask={subtask_name} in {subtask_map}")
return subtask_map[subtask_name](cfg, *args, **kwargs)
# Mappings from strings to environments
isaacgym_task_map = {
"AllegroHand": AllegroHand,
"AllegroKuka": resolve_allegro_kuka,
"AllegroKukaTwoArms": resolve_allegro_kuka_two_arms,
"AllegroHandManualDR": AllegroHandDextremeManualDR,
"AllegroHandADR": AllegroHandDextremeADR,
"Ant": Ant,
"Anymal": Anymal,
"AnymalTerrain": AnymalTerrain,
"BallBalance": BallBalance,
"Cartpole": Cartpole,
"FactoryTaskGears": FactoryTaskGears,
"FactoryTaskInsertion": FactoryTaskInsertion,
"FactoryTaskNutBoltPick": FactoryTaskNutBoltPick,
"FactoryTaskNutBoltPlace": FactoryTaskNutBoltPlace,
"FactoryTaskNutBoltScrew": FactoryTaskNutBoltScrew,
"IndustRealTaskPegsInsert": IndustRealTaskPegsInsert,
"IndustRealTaskGearsInsert": IndustRealTaskGearsInsert,
"FrankaCabinet": FrankaCabinet,
"FrankaCubeStack": FrankaCubeStack,
"Humanoid": Humanoid,
"HumanoidAMP": HumanoidAMP,
"Ingenuity": Ingenuity,
"Quadcopter": Quadcopter,
"ShadowHand": ShadowHand,
"Trifinger": Trifinger,
"TorquePole": TorquePole,
"Jumpy": Jumpy,
"QuadJumpy": QuadJumpy,
"QuadWalker": QuadWalker,
"BipedWalker": BipedWalker,
}
| 5,267 | Python | 40.480315 | 95 | 0.775584 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/bipedwalker.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymutil, gymtorch, gymapi
from .base.vec_task import VecTask
from .keyboard import Keyboard
from isaacgymenvs.utils.torch_jit_utils import to_torch, get_axis_params, torch_rand_float, quat_rotate, quat_rotate_inverse
class BipedWalker(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
# normalization
self.lin_vel_scale = self.cfg["env"]["learn"]["linearVelocityScale"]
self.ang_vel_scale = self.cfg["env"]["learn"]["angularVelocityScale"]
self.dof_vel_scale = 1/self.cfg["env"]["control"]["maxVelocity"]
self.max_dof_effort = self.cfg["env"]['control']["maxEffort"]
self.max_dof_velocity = self.cfg["env"]['control']["maxVelocity"]
self.dof_friction = self.cfg["env"]['control']["friction"]
# reward scales
self.rew_scales = {}
self.rew_scales["lin_vel_xy"] = self.cfg["env"]["learn"]["linearVelocityXYRewardScale"]
self.rew_scales["ang_vel_z"] = self.cfg["env"]["learn"]["angularVelocityZRewardScale"]
self.rew_scales["lin_vel_z"] = self.cfg["env"]["learn"]["linearVelocityXYRewardScale"]
self.rew_scales["ang_vel_xy"] = self.cfg["env"]["learn"]["angularVelocityZRewardScale"]
self.rew_scales["torque"] = self.cfg["env"]["learn"]["torqueRewardScale"]
self.rew_scales["toe_force"] = self.cfg["env"]["learn"]["toeForceRewardScale"]
self.rew_scales["joints_speed"] = self.cfg["env"]["learn"]["jointSpeedRewardScale"]
self.rew_scales["orient"] = self.cfg["env"]["learn"]["orientationRewardScale"]
self.rew_scales["joint_acc"] = self.cfg["env"]["learn"]["jointAccRewardScale"]
self.rew_scales["action_rate"] = self.cfg["env"]["learn"]["actionRateRewardScale"]
self.rew_scales["collision"] = self.cfg["env"]["learn"]["kneeCollisionRewardScale"]
self.reset_dist = self.cfg["env"]["resetDist"]
# randomization
self.randomization_params = self.cfg["task"]["randomization_params"]
self.randomize = self.cfg["task"]["randomize"]
# command ranges
self.command_x_range = self.cfg["env"]["randomCommandVelocityRanges"]["linear_x"]
self.command_y_range = self.cfg["env"]["randomCommandVelocityRanges"]["linear_y"]
self.command_yaw_range = self.cfg["env"]["randomCommandVelocityRanges"]["yaw"]
# plane params
self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"]
self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"]
self.plane_restitution = self.cfg["env"]["plane"]["restitution"]
# base init state
pos = self.cfg["env"]["baseInitState"]["pos"]
rot = self.cfg["env"]["baseInitState"]["rot"]
v_lin = self.cfg["env"]["baseInitState"]["vLinear"]
v_ang = self.cfg["env"]["baseInitState"]["vAngular"]
state = pos + rot + v_lin + v_ang
self.base_init_state = state
# default joint positions
self.named_default_joint_angles = self.cfg["env"]["defaultJointAngles"]
self.cfg["env"]["numObservations"] = 40
self.cfg["env"]["numActions"] = 8
self.Kp = self.cfg["env"]["control"]["stiffness"]
self.Kd = self.cfg["env"]["control"]["damping"]
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
# other
self.dt = self.sim_params.dt
self.max_episode_length_s = self.cfg["env"]["learn"]["episodeLength_s"]
self.max_episode_length = int(self.max_episode_length_s / self.dt + 0.5)
for key in self.rew_scales.keys():
self.rew_scales[key] *= self.dt
if self.viewer != None:
p = self.cfg["env"]["viewer"]["pos"]
lookat = self.cfg["env"]["viewer"]["lookat"]
cam_pos = gymapi.Vec3(p[0], p[1], p[2])
cam_target = gymapi.Vec3(lookat[0], lookat[1], lookat[2])
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# cam_pos = gymapi.Vec3(10.0, 9.95, 0.5)
# cam_target = gymapi.Vec3(10.0, -20.0, 0.5)
# self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym state tensors
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim)
net_contact_forces = self.gym.acquire_net_contact_force_tensor(self.sim)
torques = self.gym.acquire_dof_force_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
# create some wrapper tensors for different slices
self.root_states = gymtorch.wrap_tensor(actor_root_state)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
self.contact_forces = gymtorch.wrap_tensor(net_contact_forces).view(self.num_envs, -1, 3) # shape: num_envs, num_bodies, xyz axis
self.torques = gymtorch.wrap_tensor(torques).view(self.num_envs, self.num_dof)
self.up_axis_idx = 1 # index of up axis: Y=1, Z=2
self.gravity_vec = to_torch(get_axis_params(-1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
self.commands = torch.zeros(self.num_envs, 3, dtype=torch.float, device=self.device, requires_grad=False)
self.commands_x = self.commands.view(self.num_envs, 3)[..., 0]
self.commands_y = self.commands.view(self.num_envs, 3)[..., 1]
self.commands_yaw = self.commands.view(self.num_envs, 3)[..., 2]
self.default_dof_pos = torch.zeros_like(self.dof_pos, dtype=torch.float, device=self.device, requires_grad=False)
self.last_actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.last_dof_vel = torch.zeros_like(self.dof_vel)
for i in range(self.cfg["env"]["numActions"]):
name = self.dof_names[i]
angle = self.named_default_joint_angles[name]
self.default_dof_pos[:, i] = angle
self.initial_root_states = self.root_states.clone()
self.initial_root_states[:] = to_torch(self.base_init_state, device=self.device, requires_grad=False)
self.gravity_vec = to_torch(get_axis_params(-1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
self.actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.keys = Keyboard(3)
self.reset_idx(torch.arange(self.num_envs, device=self.device))
def create_sim(self):
# set the up axis to be z-up given that assets are y-up by default
self.up_axis = self.cfg["sim"]["up_axis"]
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
# set the normal force to be z dimension
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0) if self.up_axis == 'z' else gymapi.Vec3(0.0, 1.0, 0.0)
plane_params.static_friction = self.plane_static_friction
plane_params.dynamic_friction = self.plane_dynamic_friction
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
# define plane on which environments are initialized
lower = gymapi.Vec3(-spacing, -spacing, 0.0) if self.up_axis == 'z' else gymapi.Vec3(0.5 * -spacing, 0.0, -spacing)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets")
asset_file = "urdf/Biped_SphereFoot/urdf/Biped_SphereFoot.urdf"
if "asset" in self.cfg["env"]:
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), self.cfg["env"]["asset"].get("assetRoot", asset_root))
asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
asset_options = gymapi.AssetOptions()
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_EFFORT
asset_options.collapse_fixed_joints = True
asset_options.replace_cylinder_with_capsule = True
asset_options.fix_base_link = False
asset_options.angular_damping = 0.0
asset_options.linear_damping = 0.0
asset_options.max_angular_velocity = 10000
asset_options.armature = 0.0
asset_options.thickness = 0.01
asset_options.disable_gravity = False
bipedwalker_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dof = self.gym.get_asset_dof_count(bipedwalker_asset)
self.body_names = self.gym.get_asset_rigid_body_names(bipedwalker_asset)
self.dof_names = self.gym.get_asset_dof_names(bipedwalker_asset)
print('self.num_dof')
print(self.num_dof)
print('self.body_names')
print(self.body_names)
print('self.dof_names')
print(self.dof_names)
hip_names = [s for s in self.body_names if "Hip" in s]
thigh_names = [s for s in self.body_names if "Thigh" in s]
shin_names = [s for s in self.body_names if "Shin" in s]
foot_names = [s for s in self.body_names if "Foot" in s]
self.hip_indices = torch.zeros(len(hip_names), dtype=torch.long, device=self.device, requires_grad=False)
self.thigh_indices = torch.zeros(len(thigh_names), dtype=torch.long, device=self.device, requires_grad=False)
self.shin_indices = torch.zeros(len(shin_names), dtype=torch.long, device=self.device, requires_grad=False)
self.foot_indices = torch.zeros(len(foot_names), dtype=torch.long, device=self.device, requires_grad=False)
pose = gymapi.Transform()
pose.p = gymapi.Vec3(*self.base_init_state[:3])
self.bipedwalker_handles = []
self.envs = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
bipedwalker_handle = self.gym.create_actor(env_ptr, bipedwalker_asset, pose, "bipedwalker", i, 1, 0)
rand_color = torch.rand((3), device=self.device)
self.gym.set_rigid_body_color(env_ptr, bipedwalker_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(rand_color[0],rand_color[1],rand_color[2]))
rand_color = torch.rand((3), device=self.device)
self.gym.set_rigid_body_color(env_ptr, bipedwalker_handle, 1, gymapi.MESH_VISUAL, gymapi.Vec3(rand_color[0],rand_color[1],rand_color[2]))
dof_props = self.gym.get_actor_dof_properties(env_ptr, bipedwalker_handle)
dof_props['driveMode'][:] = gymapi.DOF_MODE_EFFORT
dof_props['stiffness'][:] = self.Kp
dof_props['damping'][:] = self.Kd
dof_props['velocity'][:] = self.max_dof_velocity
dof_props['effort'].fill(0.0)
dof_props['friction'][:] = self.dof_friction
dof_props['velocity'][6:8] = 200.0
dof_props['friction'][6:8] = 0.001
self.gym.set_actor_dof_properties(env_ptr, bipedwalker_handle, dof_props)
self.gym.enable_actor_dof_force_sensors(env_ptr, bipedwalker_handle)
self.envs.append(env_ptr)
self.bipedwalker_handles.append(bipedwalker_handle)
for i in range(len(hip_names)):
self.hip_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.bipedwalker_handles[0], hip_names[i])
for i in range(len(thigh_names)):
self.thigh_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.bipedwalker_handles[0], thigh_names[i])
for i in range(len(shin_names)):
self.shin_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.bipedwalker_handles[0], shin_names[i])
for i in range(len(foot_names)):
self.foot_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.bipedwalker_handles[0], foot_names[i])
print(self.hip_indices)
print(self.thigh_indices)
print(self.shin_indices)
print(self.foot_indices)
def compute_reward(self):
base_quat = self.root_states[:, 3:7]
base_lin_vel = quat_rotate_inverse(base_quat, self.root_states[:, 7:10])
base_ang_vel = quat_rotate_inverse(base_quat, self.root_states[:, 10:13])
# velocity tracking reward
# lin_vel_error = torch.sum(torch.square(self.commands[:, :2] - base_lin_vel[:, :2]), dim=1)
# ang_vel_error = torch.square(self.commands[:, 2] - base_ang_vel[:, 2])
# print(base_lin_vel[0, :2])
# print('!!!')
# print(self.commands[0, :2])
# print(base_lin_vel[0, [0,2]])
# print(self.progress_buf)
self.rew_buf[:], self.reset_buf[:] = compute_bipedwalker_reward(self.root_states,
self.commands,
self.torques,
self.dof_vel,
self.last_dof_vel,
self.contact_forces,
self.reset_buf,
self.progress_buf,
self.hip_indices,
self.thigh_indices,
self.shin_indices,
self.obs_buf[:,26:29],
self.actions,
self.last_actions,
self.rew_scales,
self.reset_dist,
self.max_episode_length)
self.last_actions[:] = self.actions[:]
self.last_dof_vel[:] = self.dof_vel[:]
# print(self.rew_buf[0])
# print(self.reset_buf)
# print(torch.norm(torch.norm(self.contact_forces[:,[3,6]],dim=1),dim=1))
toe_force = torch.norm(torch.norm(self.contact_forces[:,[3,6]],dim=1),dim=1)
rew_toe_force = torch.where(toe_force>1.0, toe_force, torch.zeros_like(toe_force))
# print(toe_force)
# print(rew_toe_force)
# joint acc penalty
rew_joint_acc = torch.sum(torch.square(self.last_dof_vel - self.dof_vel), dim=1) * self.rew_scales["joint_acc"]
# collision penalty
knee_contact = torch.norm(self.contact_forces[:, self.thigh_indices, :], dim=2) > 1.
rew_collision = torch.sum(knee_contact, dim=1) * self.rew_scales["collision"] # sum vs any ?
print(rew_collision)
def compute_observations(self, env_ids=None):
if env_ids is None:
env_ids = np.arange(self.num_envs)
self.gym.refresh_dof_state_tensor(self.sim) # done in step
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.obs_buf[:] = compute_bipedwalker_observations( # tensors
self.root_states,
self.commands,
self.dof_pos,
self.default_dof_pos,
self.dof_vel,
self.gravity_vec,
self.actions_tensor,
# scales
self.lin_vel_scale,
self.ang_vel_scale,
self.dof_vel_scale)
# print(self.obs_buf[0,0:6])
# print(self.obs_buf[0, 26:29])
# obs = torch.cat((
# base_lin_vel * lin_vel_scale, #3 (0:3)
# base_ang_vel * ang_vel_scale, #3 (3:6)
# sin_encode[:, 0:6], #6 (6:12)
# cos_encode[:, 0:6], #6 (12:18)
# dof_vel * dof_vel_scale, #8 (18:26)
# projected_gravity, #3 (26:29)
# commands_scaled, #3 (29:32)
# actions #8 (32:40)
# ), dim=-1)
return self.obs_buf
def reset_idx(self, env_ids):
# Randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
positions_offset = torch_rand_float(0.5, 1.5, (len(env_ids), self.num_dof), device=self.device)
# positions_offset = torch.ones((len(env_ids), self.num_dof), device=self.device)
# velocities = torch_rand_float(-0.1, 0.1, (len(env_ids), self.num_dof), device=self.device)
velocities = torch.zeros((len(env_ids), self.num_dof), device=self.device)
self.dof_pos[env_ids] = self.default_dof_pos[env_ids] * positions_offset[:]
self.dof_vel[env_ids, :] = velocities[:]
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.initial_root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.reset_commands(env_ids)
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
self.last_actions[env_ids] = 0.
self.last_dof_vel[env_ids] = 0.
def reset_commands(self, env_ids):
self.commands_x[env_ids] = torch_rand_float(self.command_x_range[0], self.command_x_range[1], (len(env_ids), 1), device=self.device).squeeze()
self.commands_y[env_ids] = torch_rand_float(self.command_y_range[0], self.command_y_range[1], (len(env_ids), 1), device=self.device).squeeze()
self.commands_yaw[env_ids] = torch_rand_float(self.command_yaw_range[0], self.command_yaw_range[1], (len(env_ids), 1), device=self.device).squeeze()
def pre_physics_step(self, actions):
self.actions_tensor = torch.zeros( [self.num_envs, self.num_dof], device=self.device, dtype=torch.float)
self.actions_tensor[:, 0:self.num_dof] = actions.to(self.device) * self.max_dof_effort
# a = self.keys.get_keys()
# scale = torch.tensor([10, self.max_dof_effort, self.max_dof_effort])
# self.actions_tensor[0,0:3] = a*scale
forces = gymtorch.unwrap_tensor(self.actions_tensor)
self.gym.set_dof_actuation_force_tensor(self.sim, forces)
# print(actions_tensor[0])
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
env_ids = torch.where(self.progress_buf % 100 == 0, torch.ones_like(self.progress_buf), torch.zeros_like(self.progress_buf)).nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_commands(env_ids)
self.compute_observations()
a = self.keys.get_keys()
scale = torch.tensor([1., 0.5, 0.25])
self.obs_buf[0, 29:32] = a*scale
# print(self.obs_buf[0,29:32])
self.compute_reward()
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def convert_angle(angle):
# Apply sine and cosine functions
sin_component = torch.sin(angle)
cos_component = torch.cos(angle)
# Normalize angle to [-pi, pi]
normalized_angle = torch.remainder(angle + np.pi, 2 * np.pi) - np.pi
# Apply offset
# normalized_angle += np.pi
# Normalize again if needed
# normalized_angle = torch.remainder(normalized_angle + np.pi, 2 * np.pi) - np.pi
# Normalize angle to [-1, 1]
normalized_angle /= torch.pi
return sin_component, cos_component, normalized_angle
@torch.jit.script
def compute_bipedwalker_reward(
# tensors
root_states,
commands,
torques,
dof_vel,
last_dof_vel,
contact_forces,
reset_buf,
progress_buf,
hip_idx,
thigh_idx,
shin_idx,
projected_gravity,
actions,
last_actions,
# Dict
rew_scales,
# other
reset_dist,
max_episode_length):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Dict[str, float], float, float) -> Tuple[Tensor, Tensor]
# prepare quantities (TODO: return from obs ?)
height = root_states[:,2]
base_quat = root_states[:, 3:7]
base_lin_vel = quat_rotate_inverse(base_quat, root_states[:, 7:10])
base_ang_vel = quat_rotate_inverse(base_quat, root_states[:, 10:13])
# velocity tracking reward
lin_vel_error = torch.sum(torch.square(commands[:, :2] - base_lin_vel[:, :2]), dim=1)
ang_vel_error = torch.square(commands[:, 2] - base_ang_vel[:, 2])
rew_lin_vel_xy = torch.exp(-lin_vel_error/0.25) * rew_scales["lin_vel_xy"]
rew_ang_vel_z = torch.exp(-ang_vel_error/0.25) * rew_scales["ang_vel_z"]
# other base velocity penalties
rew_lin_vel_z = torch.square(base_lin_vel[:, 2]) * rew_scales["lin_vel_z"]
rew_ang_vel_xy = torch.sum(torch.square(base_ang_vel[:, :2]), dim=1) * rew_scales["ang_vel_xy"]
# orientation penalty
rew_orient = torch.sum(torch.square(projected_gravity[:, :2]), dim=1) * rew_scales["orient"]
# torque penalty
rew_torque = torch.sum(torch.square(torques), dim=1) * rew_scales["torque"]
# joint speed penalty
rew_joint_speed = torch.sum(torch.square(dof_vel[:, 0:6]), dim=1) * rew_scales["joints_speed"]
# joint acc penalty
rew_joint_acc = torch.sum(torch.square(last_dof_vel - dof_vel), dim=1) * rew_scales["joint_acc"]
# collision penalty
knee_contact = torch.norm(contact_forces[:, thigh_idx, :], dim=2) > 1.
rew_collision = torch.sum(knee_contact, dim=1) * rew_scales["collision"] # sum vs any ?
# action rate penalty
rew_action_rate = torch.sum(torch.square(last_actions - actions), dim=1) * rew_scales["action_rate"]
# air time reward
# # contact = torch.norm(contact_forces[:, feet_indices, :], dim=2) > 1.
# contact = self.contact_forces[:, self.feet_indices, 2] > 1.
# first_contact = (self.feet_air_time > 0.) * contact
# self.feet_air_time += self.dt
# rew_airTime = torch.sum((self.feet_air_time - 0.5) * first_contact, dim=1) * self.rew_scales["air_time"] # reward only on first contact with the ground
# rew_airTime *= torch.norm(self.commands[:, :2], dim=1) > 0.1 #no reward for zero command
# self.feet_air_time *= ~contact
# cosmetic penalty for hip motion
# rew_hip = torch.sum(torch.abs(self.dof_pos[:, [0, 3, 6, 9]] - self.default_dof_pos[:, [0, 3, 6, 9]]), dim=1)* self.rew_scales["hip"]
# stubbed toes penalty
toe_force = torch.norm(torch.norm(contact_forces[:,[3,6]],dim=1),dim=1)
rew_toe_force = torch.where(toe_force>10.0, toe_force * rew_scales["toe_force"], torch.zeros_like(toe_force))
total_reward = rew_lin_vel_xy + rew_ang_vel_z + rew_torque + rew_toe_force + rew_joint_speed + rew_joint_acc + rew_collision
total_reward = torch.clip(total_reward, 0., None)
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset_buf)
# This is a hacky fix, the contact forces sometimes don't update when an environment resets causing a double reset.
# This waits 10 environment steps before factoring in contact forces
check_forces = torch.where(progress_buf >= 10, torch.ones_like(reset_buf), reset_buf)
reset = reset | ((torch.norm(contact_forces[:, 0, :], dim=1) > 1.) & check_forces) # Body Collision
reset = reset | ((torch.any(torch.norm(contact_forces[:, hip_idx, :], dim=2) > 1., dim=1)) & check_forces)
reset = reset | ((torch.any(torch.norm(contact_forces[:, thigh_idx, :], dim=2) > 1., dim=1)) & check_forces)
reset = reset | (height<0.25)
# reset = reset | (torch.any(torch.norm(contact_forces[:, shin_idx, :], dim=2) > 1., dim=1))
return total_reward.detach(), reset
@torch.jit.script
def compute_bipedwalker_observations(root_states,
commands,
dof_pos,
default_dof_pos,
dof_vel,
gravity_vec,
actions,
lin_vel_scale,
ang_vel_scale,
dof_vel_scale
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, float, float, float) -> Tensor
base_quat = root_states[:, 3:7]
base_lin_vel = quat_rotate_inverse(base_quat, root_states[:, 7:10])
base_ang_vel = quat_rotate_inverse(base_quat, root_states[:, 10:13])
projected_gravity = quat_rotate(base_quat, gravity_vec)
commands_scaled = commands*torch.tensor([lin_vel_scale, lin_vel_scale, ang_vel_scale], requires_grad=False, device=commands.device)
sin_encode, cos_encode, motor_angle = convert_angle(dof_pos.squeeze())
obs = torch.cat((
base_lin_vel * lin_vel_scale, #3 (0:3)
base_ang_vel * ang_vel_scale, #3 (3:6)
sin_encode[:, 0:6], #6 (6:12)
cos_encode[:, 0:6], #6 (12:18)
dof_vel * dof_vel_scale, #8 (18:26)
projected_gravity, #3 (26:29)
commands_scaled, #3 (29:32)
actions #8 (32:40)
), dim=-1)
return obs
| 30,231 | Python | 48.560656 | 217 | 0.567497 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/humanoid_amp.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE..
from enum import Enum
import numpy as np
import torch
import os
from gym import spaces
from isaacgym import gymapi
from isaacgym import gymtorch
from isaacgymenvs.tasks.amp.humanoid_amp_base import HumanoidAMPBase, dof_to_obs
from isaacgymenvs.tasks.amp.utils_amp import gym_util
from isaacgymenvs.tasks.amp.utils_amp.motion_lib import MotionLib
from isaacgymenvs.utils.torch_jit_utils import quat_mul, to_torch, calc_heading_quat_inv, quat_to_tan_norm, my_quat_rotate
NUM_AMP_OBS_PER_STEP = 13 + 52 + 28 + 12 # [root_h, root_rot, root_vel, root_ang_vel, dof_pos, dof_vel, key_body_pos]
class HumanoidAMP(HumanoidAMPBase):
class StateInit(Enum):
Default = 0
Start = 1
Random = 2
Hybrid = 3
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
state_init = cfg["env"]["stateInit"]
self._state_init = HumanoidAMP.StateInit[state_init]
self._hybrid_init_prob = cfg["env"]["hybridInitProb"]
self._num_amp_obs_steps = cfg["env"]["numAMPObsSteps"]
assert(self._num_amp_obs_steps >= 2)
self._reset_default_env_ids = []
self._reset_ref_env_ids = []
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
motion_file = cfg['env'].get('motion_file', "amp_humanoid_backflip.npy")
motion_file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets/amp/motions/" + motion_file)
self._load_motion(motion_file_path)
self.num_amp_obs = self._num_amp_obs_steps * NUM_AMP_OBS_PER_STEP
self._amp_obs_space = spaces.Box(np.ones(self.num_amp_obs) * -np.Inf, np.ones(self.num_amp_obs) * np.Inf)
self._amp_obs_buf = torch.zeros((self.num_envs, self._num_amp_obs_steps, NUM_AMP_OBS_PER_STEP), device=self.device, dtype=torch.float)
self._curr_amp_obs_buf = self._amp_obs_buf[:, 0]
self._hist_amp_obs_buf = self._amp_obs_buf[:, 1:]
self._amp_obs_demo_buf = None
return
def post_physics_step(self):
super().post_physics_step()
self._update_hist_amp_obs()
self._compute_amp_observations()
amp_obs_flat = self._amp_obs_buf.view(-1, self.get_num_amp_obs())
self.extras["amp_obs"] = amp_obs_flat
return
def get_num_amp_obs(self):
return self.num_amp_obs
@property
def amp_observation_space(self):
return self._amp_obs_space
def fetch_amp_obs_demo(self, num_samples):
return self.task.fetch_amp_obs_demo(num_samples)
def fetch_amp_obs_demo(self, num_samples):
dt = self.dt
motion_ids = self._motion_lib.sample_motions(num_samples)
if (self._amp_obs_demo_buf is None):
self._build_amp_obs_demo_buf(num_samples)
else:
assert(self._amp_obs_demo_buf.shape[0] == num_samples)
motion_times0 = self._motion_lib.sample_time(motion_ids)
motion_ids = np.tile(np.expand_dims(motion_ids, axis=-1), [1, self._num_amp_obs_steps])
motion_times = np.expand_dims(motion_times0, axis=-1)
time_steps = -dt * np.arange(0, self._num_amp_obs_steps)
motion_times = motion_times + time_steps
motion_ids = motion_ids.flatten()
motion_times = motion_times.flatten()
root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel, key_pos \
= self._motion_lib.get_motion_state(motion_ids, motion_times)
root_states = torch.cat([root_pos, root_rot, root_vel, root_ang_vel], dim=-1)
amp_obs_demo = build_amp_observations(root_states, dof_pos, dof_vel, key_pos,
self._local_root_obs)
self._amp_obs_demo_buf[:] = amp_obs_demo.view(self._amp_obs_demo_buf.shape)
amp_obs_demo_flat = self._amp_obs_demo_buf.view(-1, self.get_num_amp_obs())
return amp_obs_demo_flat
def _build_amp_obs_demo_buf(self, num_samples):
self._amp_obs_demo_buf = torch.zeros((num_samples, self._num_amp_obs_steps, NUM_AMP_OBS_PER_STEP), device=self.device, dtype=torch.float)
return
def _load_motion(self, motion_file):
self._motion_lib = MotionLib(motion_file=motion_file,
num_dofs=self.num_dof,
key_body_ids=self._key_body_ids.cpu().numpy(),
device=self.device)
return
def reset_idx(self, env_ids):
super().reset_idx(env_ids)
self._init_amp_obs(env_ids)
return
def _reset_actors(self, env_ids):
if (self._state_init == HumanoidAMP.StateInit.Default):
self._reset_default(env_ids)
elif (self._state_init == HumanoidAMP.StateInit.Start
or self._state_init == HumanoidAMP.StateInit.Random):
self._reset_ref_state_init(env_ids)
elif (self._state_init == HumanoidAMP.StateInit.Hybrid):
self._reset_hybrid_state_init(env_ids)
else:
assert(False), "Unsupported state initialization strategy: {:s}".format(str(self._state_init))
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
self._terminate_buf[env_ids] = 0
return
def _reset_default(self, env_ids):
self._dof_pos[env_ids] = self._initial_dof_pos[env_ids]
self._dof_vel[env_ids] = self._initial_dof_vel[env_ids]
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._initial_root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self._reset_default_env_ids = env_ids
return
def _reset_ref_state_init(self, env_ids):
num_envs = env_ids.shape[0]
motion_ids = self._motion_lib.sample_motions(num_envs)
if (self._state_init == HumanoidAMP.StateInit.Random
or self._state_init == HumanoidAMP.StateInit.Hybrid):
motion_times = self._motion_lib.sample_time(motion_ids)
elif (self._state_init == HumanoidAMP.StateInit.Start):
motion_times = np.zeros(num_envs)
else:
assert(False), "Unsupported state initialization strategy: {:s}".format(str(self._state_init))
root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel, key_pos \
= self._motion_lib.get_motion_state(motion_ids, motion_times)
self._set_env_state(env_ids=env_ids,
root_pos=root_pos,
root_rot=root_rot,
dof_pos=dof_pos,
root_vel=root_vel,
root_ang_vel=root_ang_vel,
dof_vel=dof_vel)
self._reset_ref_env_ids = env_ids
self._reset_ref_motion_ids = motion_ids
self._reset_ref_motion_times = motion_times
return
def _reset_hybrid_state_init(self, env_ids):
num_envs = env_ids.shape[0]
ref_probs = to_torch(np.array([self._hybrid_init_prob] * num_envs), device=self.device)
ref_init_mask = torch.bernoulli(ref_probs) == 1.0
ref_reset_ids = env_ids[ref_init_mask]
if (len(ref_reset_ids) > 0):
self._reset_ref_state_init(ref_reset_ids)
default_reset_ids = env_ids[torch.logical_not(ref_init_mask)]
if (len(default_reset_ids) > 0):
self._reset_default(default_reset_ids)
return
def _init_amp_obs(self, env_ids):
self._compute_amp_observations(env_ids)
if (len(self._reset_default_env_ids) > 0):
self._init_amp_obs_default(self._reset_default_env_ids)
if (len(self._reset_ref_env_ids) > 0):
self._init_amp_obs_ref(self._reset_ref_env_ids, self._reset_ref_motion_ids,
self._reset_ref_motion_times)
return
def _init_amp_obs_default(self, env_ids):
curr_amp_obs = self._curr_amp_obs_buf[env_ids].unsqueeze(-2)
self._hist_amp_obs_buf[env_ids] = curr_amp_obs
return
def _init_amp_obs_ref(self, env_ids, motion_ids, motion_times):
dt = self.dt
motion_ids = np.tile(np.expand_dims(motion_ids, axis=-1), [1, self._num_amp_obs_steps - 1])
motion_times = np.expand_dims(motion_times, axis=-1)
time_steps = -dt * (np.arange(0, self._num_amp_obs_steps - 1) + 1)
motion_times = motion_times + time_steps
motion_ids = motion_ids.flatten()
motion_times = motion_times.flatten()
root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel, key_pos \
= self._motion_lib.get_motion_state(motion_ids, motion_times)
root_states = torch.cat([root_pos, root_rot, root_vel, root_ang_vel], dim=-1)
amp_obs_demo = build_amp_observations(root_states, dof_pos, dof_vel, key_pos,
self._local_root_obs)
self._hist_amp_obs_buf[env_ids] = amp_obs_demo.view(self._hist_amp_obs_buf[env_ids].shape)
return
def _set_env_state(self, env_ids, root_pos, root_rot, dof_pos, root_vel, root_ang_vel, dof_vel):
self._root_states[env_ids, 0:3] = root_pos
self._root_states[env_ids, 3:7] = root_rot
self._root_states[env_ids, 7:10] = root_vel
self._root_states[env_ids, 10:13] = root_ang_vel
self._dof_pos[env_ids] = dof_pos
self._dof_vel[env_ids] = dof_vel
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim, gymtorch.unwrap_tensor(self._dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
return
def _update_hist_amp_obs(self, env_ids=None):
if (env_ids is None):
for i in reversed(range(self._amp_obs_buf.shape[1] - 1)):
self._amp_obs_buf[:, i + 1] = self._amp_obs_buf[:, i]
else:
for i in reversed(range(self._amp_obs_buf.shape[1] - 1)):
self._amp_obs_buf[env_ids, i + 1] = self._amp_obs_buf[env_ids, i]
return
def _compute_amp_observations(self, env_ids=None):
key_body_pos = self._rigid_body_pos[:, self._key_body_ids, :]
if (env_ids is None):
self._curr_amp_obs_buf[:] = build_amp_observations(self._root_states, self._dof_pos, self._dof_vel, key_body_pos,
self._local_root_obs)
else:
self._curr_amp_obs_buf[env_ids] = build_amp_observations(self._root_states[env_ids], self._dof_pos[env_ids],
self._dof_vel[env_ids], key_body_pos[env_ids],
self._local_root_obs)
return
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def build_amp_observations(root_states, dof_pos, dof_vel, key_body_pos, local_root_obs):
# type: (Tensor, Tensor, Tensor, Tensor, bool) -> Tensor
root_pos = root_states[:, 0:3]
root_rot = root_states[:, 3:7]
root_vel = root_states[:, 7:10]
root_ang_vel = root_states[:, 10:13]
root_h = root_pos[:, 2:3]
heading_rot = calc_heading_quat_inv(root_rot)
if (local_root_obs):
root_rot_obs = quat_mul(heading_rot, root_rot)
else:
root_rot_obs = root_rot
root_rot_obs = quat_to_tan_norm(root_rot_obs)
local_root_vel = my_quat_rotate(heading_rot, root_vel)
local_root_ang_vel = my_quat_rotate(heading_rot, root_ang_vel)
root_pos_expand = root_pos.unsqueeze(-2)
local_key_body_pos = key_body_pos - root_pos_expand
heading_rot_expand = heading_rot.unsqueeze(-2)
heading_rot_expand = heading_rot_expand.repeat((1, local_key_body_pos.shape[1], 1))
flat_end_pos = local_key_body_pos.view(local_key_body_pos.shape[0] * local_key_body_pos.shape[1], local_key_body_pos.shape[2])
flat_heading_rot = heading_rot_expand.view(heading_rot_expand.shape[0] * heading_rot_expand.shape[1],
heading_rot_expand.shape[2])
local_end_pos = my_quat_rotate(flat_heading_rot, flat_end_pos)
flat_local_key_pos = local_end_pos.view(local_key_body_pos.shape[0], local_key_body_pos.shape[1] * local_key_body_pos.shape[2])
dof_obs = dof_to_obs(dof_pos)
obs = torch.cat((root_h, root_rot_obs, local_root_vel, local_root_ang_vel, dof_obs, dof_vel, flat_local_key_pos), dim=-1)
return obs | 14,984 | Python | 44 | 217 | 0.602309 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/quadjumpy.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymutil, gymtorch, gymapi
from .base.vec_task import VecTask
from .keyboard import Keyboard
from isaacgymenvs.utils.torch_jit_utils import to_torch, get_axis_params, torch_rand_float, quat_rotate, quat_rotate_inverse
class QuadJumpy(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.reset_dist = self.cfg["env"]["resetDist"]
self.max_push_effort = self.cfg["env"]["maxEffort"]
self.max_episode_length = self.cfg["env"]["maxEpisodeLen"]
self.cfg["env"]["numObservations"] = 58
self.cfg["env"]["numActions"] = 12
# randomization
self.randomization_params = self.cfg["task"]["randomization_params"]
self.randomize = self.cfg["task"]["randomize"]
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
# get gym state tensors
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim)
net_contact_forces = self.gym.acquire_net_contact_force_tensor(self.sim)
torques = self.gym.acquire_dof_force_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
# create some wrapper tensors for different slices
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
self.root_states = gymtorch.wrap_tensor(actor_root_state)
self.initial_root_states = self.root_states.clone()
self.contact_forces = gymtorch.wrap_tensor(net_contact_forces).view(self.num_envs, -1, 3) # shape: num_envs, num_bodies, xyz axis
self.torques = gymtorch.wrap_tensor(torques).view(self.num_envs, self.num_dof)
self.up_axis_idx = 1 # index of up axis: Y=1, Z=2
self.gravity_vec = to_torch(get_axis_params(-1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
self.keys = Keyboard(3)
self.max_height_reached = torch.zeros((self.num_envs), device=self.device)
self.air_time = torch.zeros((self.num_envs), device=self.device)
cam_pos = gymapi.Vec3(10.0, 9.95, 0.5)
cam_target = gymapi.Vec3(10.0, -20.0, 0.5)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
def create_sim(self):
# set the up axis to be z-up given that assets are y-up by default
self.up_axis = self.cfg["sim"]["up_axis"]
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
# set the normal force to be z dimension
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0) if self.up_axis == 'z' else gymapi.Vec3(0.0, 1.0, 0.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
# define plane on which environments are initialized
lower = gymapi.Vec3(-spacing, -spacing, 0.0) if self.up_axis == 'z' else gymapi.Vec3(0.5 * -spacing, 0.0, -spacing)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets")
asset_file = "urdf/Quad_Foot/urdf/Quad_Foot.urdf"
if "asset" in self.cfg["env"]:
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), self.cfg["env"]["asset"].get("assetRoot", asset_root))
asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
asset_options = gymapi.AssetOptions()
asset_options.fix_base_link = False
asset_options.angular_damping = 0.0
asset_options.max_angular_velocity = 10000
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_EFFORT
torquepole_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dof = self.gym.get_asset_dof_count(torquepole_asset)
self.body_names = self.gym.get_asset_rigid_body_names(torquepole_asset)
self.dof_names = self.gym.get_asset_dof_names(torquepole_asset)
print('self.num_dof')
print(self.num_dof)
print('self.body_names')
print(self.body_names)
print('self.dof_names')
print(self.dof_names)
hip_names = [s for s in self.body_names if "Hip" in s]
thigh_names = [s for s in self.body_names if "Thigh" in s]
shin_names = [s for s in self.body_names if "Shin" in s]
foot_names = [s for s in self.body_names if "Foot" in s]
self.hip_indices = torch.zeros(len(hip_names), dtype=torch.long, device=self.device, requires_grad=False)
self.thigh_indices = torch.zeros(len(thigh_names), dtype=torch.long, device=self.device, requires_grad=False)
self.shin_indices = torch.zeros(len(shin_names), dtype=torch.long, device=self.device, requires_grad=False)
self.foot_indices = torch.zeros(len(foot_names), dtype=torch.long, device=self.device, requires_grad=False)
pose = gymapi.Transform()
if self.up_axis == 'z':
pose.p.z = 1.0
# asset is rotated z-up by default, no additional rotations needed
# pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
pose.r = gymapi.Quat.from_euler_zyx(1.5708, 0.0, 0.0)
else:
pose.p.y = 0.0
pose.r = gymapi.Quat(-np.sqrt(2)/2, 0.0, 0.0, np.sqrt(2)/2)
self.torquepole_handles = []
self.envs = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
torquepole_handle = self.gym.create_actor(env_ptr, torquepole_asset, pose, "torquepole", i, 1, 0)
rand_color = torch.rand((3), device=self.device)
self.gym.set_rigid_body_color(env_ptr, torquepole_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(rand_color[0],rand_color[1],rand_color[2]))
rand_color = torch.rand((3), device=self.device)
self.gym.set_rigid_body_color(env_ptr, torquepole_handle, 1, gymapi.MESH_VISUAL, gymapi.Vec3(rand_color[0],rand_color[1],rand_color[2]))
dof_props = self.gym.get_actor_dof_properties(env_ptr, torquepole_handle)
dof_props['driveMode'][:] = gymapi.DOF_MODE_EFFORT
dof_props['stiffness'][:] = 0.0
dof_props['damping'][:] = 0.0
dof_props['velocity'].fill(25.0)
dof_props['effort'].fill(0.0)
dof_props['friction'].fill(0.01)
self.gym.set_actor_dof_properties(env_ptr, torquepole_handle, dof_props)
self.envs.append(env_ptr)
self.torquepole_handles.append(torquepole_handle)
for i in range(len(hip_names)):
self.hip_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.torquepole_handles[0], hip_names[i])
for i in range(len(thigh_names)):
self.thigh_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.torquepole_handles[0], thigh_names[i])
for i in range(len(shin_names)):
self.shin_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.torquepole_handles[0], shin_names[i])
for i in range(len(foot_names)):
self.foot_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.torquepole_handles[0], foot_names[i])
print(self.hip_indices)
print(self.thigh_indices)
print(self.shin_indices)
print(self.foot_indices)
def compute_reward(self):
# retrieve environment observations from buffer
height = self.obs_buf[:, 4*self.num_dof]
# print(height)
self.max_height_reached = torch.max(self.max_height_reached, height)
self.max_height_reached = torch.where((torch.any(torch.norm(self.contact_forces[:, self.foot_indices, :], dim=1) > 0.1, dim=1)), torch.zeros_like(self.max_height_reached), self.max_height_reached)
self.air_time += 1
self.air_time = torch.where((torch.any(torch.norm(self.contact_forces[:, self.foot_indices, :], dim=1) > 0.1, dim=1)), torch.zeros_like(self.air_time), self.air_time)
# print(self.max_height_reached)
# print(self.contact_forces.shape)
# print(self.contact_forces[0, :, :])
# print(torch.norm(self.contact_forces[0:3, 0, :], dim=1) > 1.)
# print(torch.any(torch.norm(self.contact_forces[:, self.hip_indices, :], dim=1) > 1., dim=1).shape)
self.rew_buf[:], self.reset_buf[:] = compute_torquepole_reward(height,
self.max_height_reached,
self.air_time,
self.contact_forces,
self.hip_indices,
self.thigh_indices,
self.shin_indices,
self.reset_dist,
self.reset_buf,
self.progress_buf,
self.max_episode_length)
# print(self.rew_buf[0])
# print(self.reset_buf)
def convert_angle(self, angle):
# Apply sine and cosine functions
sin_component = torch.sin(angle)
cos_component = torch.cos(angle)
# Normalize angle to [-pi, pi]
normalized_angle = torch.remainder(angle + np.pi, 2 * np.pi) - np.pi
# Apply offset
# normalized_angle += np.pi
# Normalize again if needed
# normalized_angle = torch.remainder(normalized_angle + np.pi, 2 * np.pi) - np.pi
# Normalize angle to [-1, 1]
normalized_angle /= torch.pi
return sin_component, cos_component, normalized_angle
def compute_observations(self, env_ids=None):
if env_ids is None:
env_ids = np.arange(self.num_envs)
self.gym.refresh_dof_state_tensor(self.sim) # done in step
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
sin_encode, cos_encode, motor_angle = self.convert_angle(self.dof_pos[env_ids, 0:self.num_dof].squeeze())
self.obs_buf[env_ids, 0:self.num_dof] = sin_encode
self.obs_buf[env_ids, self.num_dof:2*self.num_dof] = cos_encode
self.obs_buf[env_ids, 2*self.num_dof:3*self.num_dof] = self.dof_vel[env_ids, :]/20.0 # Motor 0, Velocity
self.obs_buf[env_ids, 3*self.num_dof:4*self.num_dof] = self.actions_tensor[env_ids, :] # Actions
self.obs_buf[env_ids, 4*self.num_dof] = self.root_states[env_ids, 2] # Height
lin_vel_scale = 0.01
ang_vel_scale = 0.01
base_quat = self.root_states[:, 3:7]
base_lin_vel = quat_rotate_inverse(base_quat, self.root_states[:, 7:10]) * lin_vel_scale
base_ang_vel = quat_rotate_inverse(base_quat, self.root_states[:, 10:13]) * ang_vel_scale
projected_gravity = quat_rotate(base_quat, self.gravity_vec)
self.obs_buf[env_ids, 4*self.num_dof+1:4*self.num_dof+4] = base_lin_vel
self.obs_buf[env_ids, 4*self.num_dof+4:4*self.num_dof+7] = base_ang_vel
self.obs_buf[env_ids, 4*self.num_dof+7:4*self.num_dof+10] = projected_gravity
# print('!!!')
# print(self.obs_buf[0,...])
# print(self.obs_buf[0, 2*self.num_dof:3*self.num_dof])
# print(self.obs_buf[0, 3*self.num_dof:4*self.num_dof])
# print(self.obs_buf[0, 4*self.num_dof:4*self.num_dof+3])
# print(self.obs_buf[0, 4*self.num_dof+3:4*self.num_dof+6])
# print(self.obs_buf[0, 4*self.num_dof+6:4*self.num_dof+9])
# print(base_quat[0,...])
return self.obs_buf
def reset_idx(self, env_ids):
# Randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
positions = torch.ones((len(env_ids), self.num_dof), device=self.device)*0.0
positions[:, 1] = 0.5
positions[:, 4] = -0.5
positions[:, 7] = -0.5
positions[:, 10] = 0.5
positions[:, 2] = -2.0
positions[:, 5] = 2.0
positions[:, 8] = 2.0
positions[:, 11] = -2.0
velocities = torch.zeros((len(env_ids), self.num_dof), device=self.device)
self.dof_pos[env_ids, :] = positions[:]
self.dof_vel[env_ids, :] = velocities[:]
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.initial_root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
self.max_height_reached[env_ids] = 0
self.air_time[env_ids] = 0
def pre_physics_step(self, actions):
self.actions_tensor = torch.zeros( [self.num_envs, self.num_dof], device=self.device, dtype=torch.float)
self.actions_tensor[:, 0:self.num_dof] = actions.to(self.device) * self.max_push_effort
a = self.keys.get_keys()
scale = torch.tensor([10, self.max_push_effort, self.max_push_effort])
self.actions_tensor[0,0:3] = a*scale
forces = gymtorch.unwrap_tensor(self.actions_tensor)
self.gym.set_dof_actuation_force_tensor(self.sim, forces)
# print(actions_tensor[0])
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
self.compute_reward()
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_torquepole_reward(height, max_height_reached, air_time, contact_forces, hip_idx, thigh_idx, shin_idx, reset_dist, reset_buf, progress_buf, max_episode_length):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, float, Tensor, Tensor, float) -> Tuple[Tensor, Tensor]
reward = height/10.0
# reward = max_height_reached**2
reward += air_time/100.0
# reward = torch.where((torch.norm(contact_forces[:, 3, :], dim=1) > 0.1), torch.zeros_like(reward), reward)
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset_buf)
# This is a hacky fix, the contact forces sometimes don't update when an environment resets causing a double reset.
# This waits 10 environment steps before factoring in contact forces
check_forces = torch.where(progress_buf >= 10, torch.ones_like(reset_buf), reset_buf)
reset = reset | ((torch.norm(contact_forces[:, 0, :], dim=1) > 1.) & check_forces)
reset = reset | ((torch.any(torch.norm(contact_forces[:, hip_idx, :], dim=2) > 1., dim=1)) & check_forces)
reset = reset | ((torch.any(torch.norm(contact_forces[:, thigh_idx, :], dim=2) > 1., dim=1)) & check_forces)
# reset = reset | (torch.any(torch.norm(contact_forces[:, shin_idx, :], dim=2) > 1., dim=1))
return reward, reset
| 18,786 | Python | 48.052219 | 217 | 0.601671 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/humanoid.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymtorch
from isaacgym import gymapi
from isaacgymenvs.utils.torch_jit_utils import scale, unscale, quat_mul, quat_conjugate, quat_from_angle_axis, \
to_torch, get_axis_params, torch_rand_float, tensor_clamp, compute_heading_and_up, compute_rot, normalize_angle
from isaacgymenvs.tasks.base.vec_task import VecTask
class Humanoid(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.randomization_params = self.cfg["task"]["randomization_params"]
self.randomize = self.cfg["task"]["randomize"]
self.dof_vel_scale = self.cfg["env"]["dofVelocityScale"]
self.angular_velocity_scale = self.cfg["env"].get("angularVelocityScale", 0.1)
self.contact_force_scale = self.cfg["env"]["contactForceScale"]
self.power_scale = self.cfg["env"]["powerScale"]
self.heading_weight = self.cfg["env"]["headingWeight"]
self.up_weight = self.cfg["env"]["upWeight"]
self.actions_cost_scale = self.cfg["env"]["actionsCost"]
self.energy_cost_scale = self.cfg["env"]["energyCost"]
self.joints_at_limit_cost_scale = self.cfg["env"]["jointsAtLimitCost"]
self.death_cost = self.cfg["env"]["deathCost"]
self.termination_height = self.cfg["env"]["terminationHeight"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"]
self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"]
self.plane_restitution = self.cfg["env"]["plane"]["restitution"]
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.cfg["env"]["numObservations"] = 108
self.cfg["env"]["numActions"] = 21
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
if self.viewer != None:
cam_pos = gymapi.Vec3(50.0, 25.0, 2.4)
cam_target = gymapi.Vec3(45.0, 25.0, 0.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym GPU state tensors
actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
sensors_per_env = 2
self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, sensors_per_env * 6)
dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs, self.num_dof)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.root_states = gymtorch.wrap_tensor(actor_root_state)
self.initial_root_states = self.root_states.clone()
self.initial_root_states[:, 7:13] = 0
# create some wrapper tensors for different slices
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
self.initial_dof_pos = torch.zeros_like(self.dof_pos, device=self.device, dtype=torch.float)
zero_tensor = torch.tensor([0.0], device=self.device)
self.initial_dof_pos = torch.where(self.dof_limits_lower > zero_tensor, self.dof_limits_lower,
torch.where(self.dof_limits_upper < zero_tensor, self.dof_limits_upper, self.initial_dof_pos))
self.initial_dof_vel = torch.zeros_like(self.dof_vel, device=self.device, dtype=torch.float)
# initialize some data used later on
self.up_vec = to_torch(get_axis_params(1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
self.heading_vec = to_torch([1, 0, 0], device=self.device).repeat((self.num_envs, 1))
self.inv_start_rot = quat_conjugate(self.start_rotation).repeat((self.num_envs, 1))
self.basis_vec0 = self.heading_vec.clone()
self.basis_vec1 = self.up_vec.clone()
self.targets = to_torch([1000, 0, 0], device=self.device).repeat((self.num_envs, 1))
self.target_dirs = to_torch([1, 0, 0], device=self.device).repeat((self.num_envs, 1))
self.dt = self.cfg["sim"]["dt"]
self.potentials = to_torch([-1000./self.dt], device=self.device).repeat(self.num_envs)
self.prev_potentials = self.potentials.clone()
def create_sim(self):
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
# If randomizing, apply once immediately on startup before the fist sim step
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
plane_params.static_friction = self.plane_static_friction
plane_params.dynamic_friction = self.plane_dynamic_friction
plane_params.restitution = self.plane_restitution
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../assets')
asset_file = "mjcf/nv_humanoid.xml"
if "asset" in self.cfg["env"]:
asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
asset_options = gymapi.AssetOptions()
asset_options.angular_damping = 0.01
asset_options.max_angular_velocity = 100.0
# Note - DOF mode is set in the MJCF file and loaded by Isaac Gym
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
humanoid_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
# Note - for this asset we are loading the actuator info from the MJCF
actuator_props = self.gym.get_asset_actuator_properties(humanoid_asset)
motor_efforts = [prop.motor_effort for prop in actuator_props]
# create force sensors at the feet
right_foot_idx = self.gym.find_asset_rigid_body_index(humanoid_asset, "right_foot")
left_foot_idx = self.gym.find_asset_rigid_body_index(humanoid_asset, "left_foot")
sensor_pose = gymapi.Transform()
self.gym.create_asset_force_sensor(humanoid_asset, right_foot_idx, sensor_pose)
self.gym.create_asset_force_sensor(humanoid_asset, left_foot_idx, sensor_pose)
self.max_motor_effort = max(motor_efforts)
self.motor_efforts = to_torch(motor_efforts, device=self.device)
self.torso_index = 0
self.num_bodies = self.gym.get_asset_rigid_body_count(humanoid_asset)
self.num_dof = self.gym.get_asset_dof_count(humanoid_asset)
self.num_joints = self.gym.get_asset_joint_count(humanoid_asset)
start_pose = gymapi.Transform()
start_pose.p = gymapi.Vec3(*get_axis_params(1.34, self.up_axis_idx))
start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
self.start_rotation = torch.tensor([start_pose.r.x, start_pose.r.y, start_pose.r.z, start_pose.r.w], device=self.device)
self.humanoid_handles = []
self.envs = []
self.dof_limits_lower = []
self.dof_limits_upper = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
handle = self.gym.create_actor(env_ptr, humanoid_asset, start_pose, "humanoid", i, 0, 0)
self.gym.enable_actor_dof_force_sensors(env_ptr, handle)
for j in range(self.num_bodies):
self.gym.set_rigid_body_color(
env_ptr, handle, j, gymapi.MESH_VISUAL, gymapi.Vec3(0.97, 0.38, 0.06))
self.envs.append(env_ptr)
self.humanoid_handles.append(handle)
dof_prop = self.gym.get_actor_dof_properties(env_ptr, handle)
for j in range(self.num_dof):
if dof_prop['lower'][j] > dof_prop['upper'][j]:
self.dof_limits_lower.append(dof_prop['upper'][j])
self.dof_limits_upper.append(dof_prop['lower'][j])
else:
self.dof_limits_lower.append(dof_prop['lower'][j])
self.dof_limits_upper.append(dof_prop['upper'][j])
self.dof_limits_lower = to_torch(self.dof_limits_lower, device=self.device)
self.dof_limits_upper = to_torch(self.dof_limits_upper, device=self.device)
self.extremities = to_torch([5, 8], device=self.device, dtype=torch.long)
def compute_reward(self, actions):
self.rew_buf[:], self.reset_buf = compute_humanoid_reward(
self.obs_buf,
self.reset_buf,
self.progress_buf,
self.actions,
self.up_weight,
self.heading_weight,
self.potentials,
self.prev_potentials,
self.actions_cost_scale,
self.energy_cost_scale,
self.joints_at_limit_cost_scale,
self.max_motor_effort,
self.motor_efforts,
self.termination_height,
self.death_cost,
self.max_episode_length
)
def compute_observations(self):
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_force_sensor_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.obs_buf[:], self.potentials[:], self.prev_potentials[:], self.up_vec[:], self.heading_vec[:] = compute_humanoid_observations(
self.obs_buf, self.root_states, self.targets, self.potentials,
self.inv_start_rot, self.dof_pos, self.dof_vel, self.dof_force_tensor,
self.dof_limits_lower, self.dof_limits_upper, self.dof_vel_scale,
self.vec_sensor_tensor, self.actions, self.dt, self.contact_force_scale, self.angular_velocity_scale,
self.basis_vec0, self.basis_vec1)
def reset_idx(self, env_ids):
# Randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
positions = torch_rand_float(-0.2, 0.2, (len(env_ids), self.num_dof), device=self.device)
velocities = torch_rand_float(-0.1, 0.1, (len(env_ids), self.num_dof), device=self.device)
self.dof_pos[env_ids] = tensor_clamp(self.initial_dof_pos[env_ids] + positions, self.dof_limits_lower, self.dof_limits_upper)
self.dof_vel[env_ids] = velocities
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.initial_root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
to_target = self.targets[env_ids] - self.initial_root_states[env_ids, 0:3]
to_target[:, self.up_axis_idx] = 0
self.prev_potentials[env_ids] = -torch.norm(to_target, p=2, dim=-1) / self.dt
self.potentials[env_ids] = self.prev_potentials[env_ids].clone()
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
def pre_physics_step(self, actions):
self.actions = actions.to(self.device).clone()
forces = self.actions * self.motor_efforts.unsqueeze(0) * self.power_scale
force_tensor = gymtorch.unwrap_tensor(forces)
self.gym.set_dof_actuation_force_tensor(self.sim, force_tensor)
def post_physics_step(self):
self.progress_buf += 1
self.randomize_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).flatten()
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
self.compute_reward(self.actions)
# debug viz
if self.viewer and self.debug_viz:
self.gym.clear_lines(self.viewer)
points = []
colors = []
for i in range(self.num_envs):
origin = self.gym.get_env_origin(self.envs[i])
pose = self.root_states[:, 0:3][i].cpu().numpy()
glob_pos = gymapi.Vec3(origin.x + pose[0], origin.y + pose[1], origin.z + pose[2])
points.append([glob_pos.x, glob_pos.y, glob_pos.z, glob_pos.x + 4 * self.heading_vec[i, 0].cpu().numpy(),
glob_pos.y + 4 * self.heading_vec[i, 1].cpu().numpy(),
glob_pos.z + 4 * self.heading_vec[i, 2].cpu().numpy()])
colors.append([0.97, 0.1, 0.06])
points.append([glob_pos.x, glob_pos.y, glob_pos.z, glob_pos.x + 4 * self.up_vec[i, 0].cpu().numpy(), glob_pos.y + 4 * self.up_vec[i, 1].cpu().numpy(),
glob_pos.z + 4 * self.up_vec[i, 2].cpu().numpy()])
colors.append([0.05, 0.99, 0.04])
self.gym.add_lines(self.viewer, None, self.num_envs * 2, points, colors)
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_humanoid_reward(
obs_buf,
reset_buf,
progress_buf,
actions,
up_weight,
heading_weight,
potentials,
prev_potentials,
actions_cost_scale,
energy_cost_scale,
joints_at_limit_cost_scale,
max_motor_effort,
motor_efforts,
termination_height,
death_cost,
max_episode_length
):
# type: (Tensor, Tensor, Tensor, Tensor, float, float, Tensor, Tensor, float, float, float, float, Tensor, float, float, float) -> Tuple[Tensor, Tensor]
# reward from the direction headed
heading_weight_tensor = torch.ones_like(obs_buf[:, 11]) * heading_weight
heading_reward = torch.where(obs_buf[:, 11] > 0.8, heading_weight_tensor, heading_weight * obs_buf[:, 11] / 0.8)
# reward for being upright
up_reward = torch.zeros_like(heading_reward)
up_reward = torch.where(obs_buf[:, 10] > 0.93, up_reward + up_weight, up_reward)
actions_cost = torch.sum(actions ** 2, dim=-1)
# energy cost reward
motor_effort_ratio = motor_efforts / max_motor_effort
scaled_cost = joints_at_limit_cost_scale * (torch.abs(obs_buf[:, 12:33]) - 0.98) / 0.02
dof_at_limit_cost = torch.sum((torch.abs(obs_buf[:, 12:33]) > 0.98) * scaled_cost * motor_effort_ratio.unsqueeze(0), dim=-1)
electricity_cost = torch.sum(torch.abs(actions * obs_buf[:, 33:54]) * motor_effort_ratio.unsqueeze(0), dim=-1)
# reward for duration of being alive
alive_reward = torch.ones_like(potentials) * 2.0
progress_reward = potentials - prev_potentials
total_reward = progress_reward + alive_reward + up_reward + heading_reward - \
actions_cost_scale * actions_cost - energy_cost_scale * electricity_cost - dof_at_limit_cost
# adjust reward for fallen agents
total_reward = torch.where(obs_buf[:, 0] < termination_height, torch.ones_like(total_reward) * death_cost, total_reward)
# reset agents
reset = torch.where(obs_buf[:, 0] < termination_height, torch.ones_like(reset_buf), reset_buf)
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset)
return total_reward, reset
@torch.jit.script
def compute_humanoid_observations(obs_buf, root_states, targets, potentials, inv_start_rot, dof_pos, dof_vel,
dof_force, dof_limits_lower, dof_limits_upper, dof_vel_scale,
sensor_force_torques, actions, dt, contact_force_scale, angular_velocity_scale,
basis_vec0, basis_vec1):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, float, Tensor, Tensor, float, float, float, Tensor, Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]
torso_position = root_states[:, 0:3]
torso_rotation = root_states[:, 3:7]
velocity = root_states[:, 7:10]
ang_velocity = root_states[:, 10:13]
to_target = targets - torso_position
to_target[:, 2] = 0
prev_potentials_new = potentials.clone()
potentials = -torch.norm(to_target, p=2, dim=-1) / dt
torso_quat, up_proj, heading_proj, up_vec, heading_vec = compute_heading_and_up(
torso_rotation, inv_start_rot, to_target, basis_vec0, basis_vec1, 2)
vel_loc, angvel_loc, roll, pitch, yaw, angle_to_target = compute_rot(
torso_quat, velocity, ang_velocity, targets, torso_position)
roll = normalize_angle(roll).unsqueeze(-1)
yaw = normalize_angle(yaw).unsqueeze(-1)
angle_to_target = normalize_angle(angle_to_target).unsqueeze(-1)
dof_pos_scaled = unscale(dof_pos, dof_limits_lower, dof_limits_upper)
# obs_buf shapes: 1, 3, 3, 1, 1, 1, 1, 1, num_dofs (21), num_dofs (21), 6, num_acts (21)
obs = torch.cat((torso_position[:, 2].view(-1, 1), vel_loc, angvel_loc * angular_velocity_scale,
yaw, roll, angle_to_target, up_proj.unsqueeze(-1), heading_proj.unsqueeze(-1),
dof_pos_scaled, dof_vel * dof_vel_scale, dof_force * contact_force_scale,
sensor_force_torques.view(-1, 12) * contact_force_scale, actions), dim=-1)
return obs, potentials, prev_potentials_new, up_vec, heading_vec
| 20,168 | Python | 47.717391 | 217 | 0.631743 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/ant.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymtorch
from isaacgym import gymapi
from isaacgym.gymtorch import *
from isaacgymenvs.utils.torch_jit_utils import *
from isaacgymenvs.tasks.base.vec_task import VecTask
class Ant(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.randomization_params = self.cfg["task"]["randomization_params"]
self.randomize = self.cfg["task"]["randomize"]
self.dof_vel_scale = self.cfg["env"]["dofVelocityScale"]
self.contact_force_scale = self.cfg["env"]["contactForceScale"]
self.power_scale = self.cfg["env"]["powerScale"]
self.heading_weight = self.cfg["env"]["headingWeight"]
self.up_weight = self.cfg["env"]["upWeight"]
self.actions_cost_scale = self.cfg["env"]["actionsCost"]
self.energy_cost_scale = self.cfg["env"]["energyCost"]
self.joints_at_limit_cost_scale = self.cfg["env"]["jointsAtLimitCost"]
self.death_cost = self.cfg["env"]["deathCost"]
self.termination_height = self.cfg["env"]["terminationHeight"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"]
self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"]
self.plane_restitution = self.cfg["env"]["plane"]["restitution"]
self.cfg["env"]["numObservations"] = 60
self.cfg["env"]["numActions"] = 8
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
if self.viewer != None:
cam_pos = gymapi.Vec3(50.0, 25.0, 2.4)
cam_target = gymapi.Vec3(45.0, 25.0, 0.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym GPU state tensors
actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
sensors_per_env = 4
self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, sensors_per_env * 6)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.root_states = gymtorch.wrap_tensor(actor_root_state)
self.initial_root_states = self.root_states.clone()
self.initial_root_states[:, 7:13] = 0 # set lin_vel and ang_vel to 0
# create some wrapper tensors for different slices
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
self.initial_dof_pos = torch.zeros_like(self.dof_pos, device=self.device, dtype=torch.float)
zero_tensor = torch.tensor([0.0], device=self.device)
self.initial_dof_pos = torch.where(self.dof_limits_lower > zero_tensor, self.dof_limits_lower,
torch.where(self.dof_limits_upper < zero_tensor, self.dof_limits_upper, self.initial_dof_pos))
self.initial_dof_vel = torch.zeros_like(self.dof_vel, device=self.device, dtype=torch.float)
# initialize some data used later on
self.up_vec = to_torch(get_axis_params(1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
self.heading_vec = to_torch([1, 0, 0], device=self.device).repeat((self.num_envs, 1))
self.inv_start_rot = quat_conjugate(self.start_rotation).repeat((self.num_envs, 1))
self.basis_vec0 = self.heading_vec.clone()
self.basis_vec1 = self.up_vec.clone()
self.targets = to_torch([1000, 0, 0], device=self.device).repeat((self.num_envs, 1))
self.target_dirs = to_torch([1, 0, 0], device=self.device).repeat((self.num_envs, 1))
self.dt = self.cfg["sim"]["dt"]
self.potentials = to_torch([-1000./self.dt], device=self.device).repeat(self.num_envs)
self.prev_potentials = self.potentials.clone()
def create_sim(self):
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
print(f'num envs {self.num_envs} env spacing {self.cfg["env"]["envSpacing"]}')
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
# If randomizing, apply once immediately on startup before the fist sim step
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
plane_params.static_friction = self.plane_static_friction
plane_params.dynamic_friction = self.plane_dynamic_friction
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../assets')
asset_file = "mjcf/nv_ant.xml"
if "asset" in self.cfg["env"]:
asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
asset_options = gymapi.AssetOptions()
# Note - DOF mode is set in the MJCF file and loaded by Isaac Gym
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
asset_options.angular_damping = 0.0
ant_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dof = self.gym.get_asset_dof_count(ant_asset)
self.num_bodies = self.gym.get_asset_rigid_body_count(ant_asset)
# Note - for this asset we are loading the actuator info from the MJCF
actuator_props = self.gym.get_asset_actuator_properties(ant_asset)
motor_efforts = [prop.motor_effort for prop in actuator_props]
self.joint_gears = to_torch(motor_efforts, device=self.device)
start_pose = gymapi.Transform()
start_pose.p = gymapi.Vec3(*get_axis_params(0.44, self.up_axis_idx))
self.start_rotation = torch.tensor([start_pose.r.x, start_pose.r.y, start_pose.r.z, start_pose.r.w], device=self.device)
self.torso_index = 0
self.num_bodies = self.gym.get_asset_rigid_body_count(ant_asset)
body_names = [self.gym.get_asset_rigid_body_name(ant_asset, i) for i in range(self.num_bodies)]
extremity_names = [s for s in body_names if "foot" in s]
self.extremities_index = torch.zeros(len(extremity_names), dtype=torch.long, device=self.device)
# create force sensors attached to the "feet"
extremity_indices = [self.gym.find_asset_rigid_body_index(ant_asset, name) for name in extremity_names]
sensor_pose = gymapi.Transform()
for body_idx in extremity_indices:
self.gym.create_asset_force_sensor(ant_asset, body_idx, sensor_pose)
self.ant_handles = []
self.envs = []
self.dof_limits_lower = []
self.dof_limits_upper = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
ant_handle = self.gym.create_actor(env_ptr, ant_asset, start_pose, "ant", i, 1, 0)
for j in range(self.num_bodies):
self.gym.set_rigid_body_color(
env_ptr, ant_handle, j, gymapi.MESH_VISUAL, gymapi.Vec3(0.97, 0.38, 0.06))
self.envs.append(env_ptr)
self.ant_handles.append(ant_handle)
dof_prop = self.gym.get_actor_dof_properties(env_ptr, ant_handle)
for j in range(self.num_dof):
if dof_prop['lower'][j] > dof_prop['upper'][j]:
self.dof_limits_lower.append(dof_prop['upper'][j])
self.dof_limits_upper.append(dof_prop['lower'][j])
else:
self.dof_limits_lower.append(dof_prop['lower'][j])
self.dof_limits_upper.append(dof_prop['upper'][j])
self.dof_limits_lower = to_torch(self.dof_limits_lower, device=self.device)
self.dof_limits_upper = to_torch(self.dof_limits_upper, device=self.device)
for i in range(len(extremity_names)):
self.extremities_index[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.ant_handles[0], extremity_names[i])
def compute_reward(self, actions):
self.rew_buf[:], self.reset_buf[:] = compute_ant_reward(
self.obs_buf,
self.reset_buf,
self.progress_buf,
self.actions,
self.up_weight,
self.heading_weight,
self.potentials,
self.prev_potentials,
self.actions_cost_scale,
self.energy_cost_scale,
self.joints_at_limit_cost_scale,
self.termination_height,
self.death_cost,
self.max_episode_length
)
def compute_observations(self):
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_force_sensor_tensor(self.sim)
self.obs_buf[:], self.potentials[:], self.prev_potentials[:], self.up_vec[:], self.heading_vec[:] = compute_ant_observations(
self.obs_buf, self.root_states, self.targets, self.potentials,
self.inv_start_rot, self.dof_pos, self.dof_vel,
self.dof_limits_lower, self.dof_limits_upper, self.dof_vel_scale,
self.vec_sensor_tensor, self.actions, self.dt, self.contact_force_scale,
self.basis_vec0, self.basis_vec1, self.up_axis_idx)
# Required for PBT training
def compute_true_objective(self):
velocity = self.root_states[:, 7:10]
# We optimize for the maximum velocity along the x-axis (forward)
self.extras['true_objective'] = velocity[:, 0].squeeze()
def reset_idx(self, env_ids):
# Randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
positions = torch_rand_float(-0.2, 0.2, (len(env_ids), self.num_dof), device=self.device)
velocities = torch_rand_float(-0.1, 0.1, (len(env_ids), self.num_dof), device=self.device)
self.dof_pos[env_ids] = tensor_clamp(self.initial_dof_pos[env_ids] + positions, self.dof_limits_lower, self.dof_limits_upper)
self.dof_vel[env_ids] = velocities
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.initial_root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
to_target = self.targets[env_ids] - self.initial_root_states[env_ids, 0:3]
to_target[:, 2] = 0.0
self.prev_potentials[env_ids] = -torch.norm(to_target, p=2, dim=-1) / self.dt
self.potentials[env_ids] = self.prev_potentials[env_ids].clone()
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
def pre_physics_step(self, actions):
self.actions = actions.clone().to(self.device)
forces = self.actions * self.joint_gears * self.power_scale
force_tensor = gymtorch.unwrap_tensor(forces)
self.gym.set_dof_actuation_force_tensor(self.sim, force_tensor)
def post_physics_step(self):
self.progress_buf += 1
self.randomize_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).flatten()
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
self.compute_reward(self.actions)
self.compute_true_objective()
# debug viz
if self.viewer and self.debug_viz:
self.gym.clear_lines(self.viewer)
self.gym.refresh_actor_root_state_tensor(self.sim)
points = []
colors = []
for i in range(self.num_envs):
origin = self.gym.get_env_origin(self.envs[i])
pose = self.root_states[:, 0:3][i].cpu().numpy()
glob_pos = gymapi.Vec3(origin.x + pose[0], origin.y + pose[1], origin.z + pose[2])
points.append([glob_pos.x, glob_pos.y, glob_pos.z, glob_pos.x + 4 * self.heading_vec[i, 0].cpu().numpy(),
glob_pos.y + 4 * self.heading_vec[i, 1].cpu().numpy(),
glob_pos.z + 4 * self.heading_vec[i, 2].cpu().numpy()])
colors.append([0.97, 0.1, 0.06])
points.append([glob_pos.x, glob_pos.y, glob_pos.z, glob_pos.x + 4 * self.up_vec[i, 0].cpu().numpy(), glob_pos.y + 4 * self.up_vec[i, 1].cpu().numpy(),
glob_pos.z + 4 * self.up_vec[i, 2].cpu().numpy()])
colors.append([0.05, 0.99, 0.04])
self.gym.add_lines(self.viewer, None, self.num_envs * 2, points, colors)
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_ant_reward(
obs_buf,
reset_buf,
progress_buf,
actions,
up_weight,
heading_weight,
potentials,
prev_potentials,
actions_cost_scale,
energy_cost_scale,
joints_at_limit_cost_scale,
termination_height,
death_cost,
max_episode_length
):
# type: (Tensor, Tensor, Tensor, Tensor, float, float, Tensor, Tensor, float, float, float, float, float, float) -> Tuple[Tensor, Tensor]
# reward from direction headed
heading_weight_tensor = torch.ones_like(obs_buf[:, 11]) * heading_weight
heading_reward = torch.where(obs_buf[:, 11] > 0.8, heading_weight_tensor, heading_weight * obs_buf[:, 11] / 0.8)
# aligning up axis of ant and environment
up_reward = torch.zeros_like(heading_reward)
up_reward = torch.where(obs_buf[:, 10] > 0.93, up_reward + up_weight, up_reward)
# energy penalty for movement
actions_cost = torch.sum(actions ** 2, dim=-1)
electricity_cost = torch.sum(torch.abs(actions * obs_buf[:, 20:28]), dim=-1)
dof_at_limit_cost = torch.sum(obs_buf[:, 12:20] > 0.99, dim=-1)
# reward for duration of staying alive
alive_reward = torch.ones_like(potentials) * 0.5
progress_reward = potentials - prev_potentials
total_reward = progress_reward + alive_reward + up_reward + heading_reward - \
actions_cost_scale * actions_cost - energy_cost_scale * electricity_cost - dof_at_limit_cost * joints_at_limit_cost_scale
# adjust reward for fallen agents
total_reward = torch.where(obs_buf[:, 0] < termination_height, torch.ones_like(total_reward) * death_cost, total_reward)
# reset agents
reset = torch.where(obs_buf[:, 0] < termination_height, torch.ones_like(reset_buf), reset_buf)
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset)
return total_reward, reset
@torch.jit.script
def compute_ant_observations(obs_buf, root_states, targets, potentials,
inv_start_rot, dof_pos, dof_vel,
dof_limits_lower, dof_limits_upper, dof_vel_scale,
sensor_force_torques, actions, dt, contact_force_scale,
basis_vec0, basis_vec1, up_axis_idx):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, float, Tensor, Tensor, float, float, Tensor, Tensor, int) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]
torso_position = root_states[:, 0:3]
torso_rotation = root_states[:, 3:7]
velocity = root_states[:, 7:10]
ang_velocity = root_states[:, 10:13]
to_target = targets - torso_position
to_target[:, 2] = 0.0
prev_potentials_new = potentials.clone()
potentials = -torch.norm(to_target, p=2, dim=-1) / dt
torso_quat, up_proj, heading_proj, up_vec, heading_vec = compute_heading_and_up(
torso_rotation, inv_start_rot, to_target, basis_vec0, basis_vec1, 2)
vel_loc, angvel_loc, roll, pitch, yaw, angle_to_target = compute_rot(
torso_quat, velocity, ang_velocity, targets, torso_position)
dof_pos_scaled = unscale(dof_pos, dof_limits_lower, dof_limits_upper)
# obs_buf shapes: 1, 3, 3, 1, 1, 1, 1, 1, num_dofs(8), num_dofs(8), 24, num_dofs(8)
obs = torch.cat((torso_position[:, up_axis_idx].view(-1, 1), vel_loc, angvel_loc,
yaw.unsqueeze(-1), roll.unsqueeze(-1), angle_to_target.unsqueeze(-1),
up_proj.unsqueeze(-1), heading_proj.unsqueeze(-1), dof_pos_scaled,
dof_vel * dof_vel_scale, sensor_force_torques.view(-1, 24) * contact_force_scale,
actions), dim=-1)
return obs, potentials, prev_potentials_new, up_vec, heading_vec | 19,545 | Python | 46.906863 | 217 | 0.626349 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/cartpole.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymutil, gymtorch, gymapi
from .base.vec_task import VecTask
class Cartpole(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.reset_dist = self.cfg["env"]["resetDist"]
self.max_push_effort = self.cfg["env"]["maxEffort"]
self.max_episode_length = 500
self.cfg["env"]["numObservations"] = 4
self.cfg["env"]["numActions"] = 1
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
def create_sim(self):
# set the up axis to be z-up given that assets are y-up by default
self.up_axis = self.cfg["sim"]["up_axis"]
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
# set the normal force to be z dimension
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0) if self.up_axis == 'z' else gymapi.Vec3(0.0, 1.0, 0.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
# define plane on which environments are initialized
lower = gymapi.Vec3(0.5 * -spacing, -spacing, 0.0) if self.up_axis == 'z' else gymapi.Vec3(0.5 * -spacing, 0.0, -spacing)
upper = gymapi.Vec3(0.5 * spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets")
asset_file = "urdf/cartpole.urdf"
if "asset" in self.cfg["env"]:
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), self.cfg["env"]["asset"].get("assetRoot", asset_root))
asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
asset_options = gymapi.AssetOptions()
asset_options.fix_base_link = True
cartpole_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dof = self.gym.get_asset_dof_count(cartpole_asset)
pose = gymapi.Transform()
if self.up_axis == 'z':
pose.p.z = 2.0
# asset is rotated z-up by default, no additional rotations needed
pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
else:
pose.p.y = 2.0
pose.r = gymapi.Quat(-np.sqrt(2)/2, 0.0, 0.0, np.sqrt(2)/2)
self.cartpole_handles = []
self.envs = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
cartpole_handle = self.gym.create_actor(env_ptr, cartpole_asset, pose, "cartpole", i, 1, 0)
dof_props = self.gym.get_actor_dof_properties(env_ptr, cartpole_handle)
dof_props['driveMode'][0] = gymapi.DOF_MODE_EFFORT
dof_props['driveMode'][1] = gymapi.DOF_MODE_NONE
dof_props['stiffness'][:] = 0.0
dof_props['damping'][:] = 0.0
self.gym.set_actor_dof_properties(env_ptr, cartpole_handle, dof_props)
self.envs.append(env_ptr)
self.cartpole_handles.append(cartpole_handle)
def compute_reward(self):
# retrieve environment observations from buffer
pole_angle = self.obs_buf[:, 2]
pole_vel = self.obs_buf[:, 3]
cart_vel = self.obs_buf[:, 1]
cart_pos = self.obs_buf[:, 0]
self.rew_buf[:], self.reset_buf[:] = compute_cartpole_reward(
pole_angle, pole_vel, cart_vel, cart_pos,
self.reset_dist, self.reset_buf, self.progress_buf, self.max_episode_length
)
def compute_observations(self, env_ids=None):
if env_ids is None:
env_ids = np.arange(self.num_envs)
self.gym.refresh_dof_state_tensor(self.sim)
self.obs_buf[env_ids, 0] = self.dof_pos[env_ids, 0].squeeze()
self.obs_buf[env_ids, 1] = self.dof_vel[env_ids, 0].squeeze()
self.obs_buf[env_ids, 2] = self.dof_pos[env_ids, 1].squeeze()
self.obs_buf[env_ids, 3] = self.dof_vel[env_ids, 1].squeeze()
return self.obs_buf
def reset_idx(self, env_ids):
positions = 0.2 * (torch.rand((len(env_ids), self.num_dof), device=self.device) - 0.5)
velocities = 0.5 * (torch.rand((len(env_ids), self.num_dof), device=self.device) - 0.5)
self.dof_pos[env_ids, :] = positions[:]
self.dof_vel[env_ids, :] = velocities[:]
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def pre_physics_step(self, actions):
actions_tensor = torch.zeros(self.num_envs * self.num_dof, device=self.device, dtype=torch.float)
actions_tensor[::self.num_dof] = actions.to(self.device).squeeze() * self.max_push_effort
forces = gymtorch.unwrap_tensor(actions_tensor)
self.gym.set_dof_actuation_force_tensor(self.sim, forces)
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
self.compute_reward()
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_cartpole_reward(pole_angle, pole_vel, cart_vel, cart_pos,
reset_dist, reset_buf, progress_buf, max_episode_length):
# type: (Tensor, Tensor, Tensor, Tensor, float, Tensor, Tensor, float) -> Tuple[Tensor, Tensor]
# reward is combo of angle deviated from upright, velocity of cart, and velocity of pole moving
reward = 1.0 - pole_angle * pole_angle - 0.01 * torch.abs(cart_vel) - 0.005 * torch.abs(pole_vel)
# adjust reward for reset agents
reward = torch.where(torch.abs(cart_pos) > reset_dist, torch.ones_like(reward) * -2.0, reward)
reward = torch.where(torch.abs(pole_angle) > np.pi / 2, torch.ones_like(reward) * -2.0, reward)
reset = torch.where(torch.abs(cart_pos) > reset_dist, torch.ones_like(reset_buf), reset_buf)
reset = torch.where(torch.abs(pole_angle) > np.pi / 2, torch.ones_like(reset_buf), reset)
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset)
return reward, reset
| 9,134 | Python | 45.370558 | 217 | 0.629297 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/jumpy.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymutil, gymtorch, gymapi
from .base.vec_task import VecTask
from .keyboard import Keyboard
class Jumpy(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.reset_dist = self.cfg["env"]["resetDist"]
self.max_push_effort = self.cfg["env"]["maxEffort"]
self.max_episode_length = self.cfg["env"]["maxEpisodeLen"]
self.cfg["env"]["numObservations"] = 9
self.cfg["env"]["numActions"] = 2
# randomization
self.randomization_params = self.cfg["task"]["randomization_params"]
self.randomize = self.cfg["task"]["randomize"]
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
net_contact_forces = self.gym.acquire_net_contact_force_tensor(self.sim)
self.contact_forces = gymtorch.wrap_tensor(net_contact_forces).view(self.num_envs, -1, 3) # shape: num_envs, num_bodies, xyz axis
self.keys = Keyboard(3)
self.max_height_reached = torch.zeros((self.num_envs), device=self.device)
cam_pos = gymapi.Vec3(10.0, 9.95, 0.5)
cam_target = gymapi.Vec3(10.0, -20.0, 0.5)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
def create_sim(self):
# set the up axis to be z-up given that assets are y-up by default
self.up_axis = self.cfg["sim"]["up_axis"]
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
# set the normal force to be z dimension
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0) if self.up_axis == 'z' else gymapi.Vec3(0.0, 1.0, 0.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
# define plane on which environments are initialized
lower = gymapi.Vec3(-spacing, -spacing, 0.0) if self.up_axis == 'z' else gymapi.Vec3(0.5 * -spacing, 0.0, -spacing)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets")
asset_file = "urdf/Jumpy/urdf/Jumpy.urdf"
if "asset" in self.cfg["env"]:
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), self.cfg["env"]["asset"].get("assetRoot", asset_root))
asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
asset_options = gymapi.AssetOptions()
asset_options.fix_base_link = True
asset_options.angular_damping = 0.0
asset_options.max_angular_velocity = 10000
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_EFFORT
torquepole_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dof = self.gym.get_asset_dof_count(torquepole_asset)
pose = gymapi.Transform()
if self.up_axis == 'z':
pose.p.z = 1.0
# asset is rotated z-up by default, no additional rotations needed
# pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
pose.r = gymapi.Quat.from_euler_zyx(-1.5708, 0.0, 0.0)
else:
pose.p.y = 0.0
pose.r = gymapi.Quat(-np.sqrt(2)/2, 0.0, 0.0, np.sqrt(2)/2)
self.torquepole_handles = []
self.envs = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
torquepole_handle = self.gym.create_actor(env_ptr, torquepole_asset, pose, "torquepole", i, 0, 0)
rand_color = torch.rand((3), device=self.device)
self.gym.set_rigid_body_color(env_ptr, torquepole_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(rand_color[0],rand_color[1],rand_color[2]))
rand_color = torch.rand((3), device=self.device)
self.gym.set_rigid_body_color(env_ptr, torquepole_handle, 1, gymapi.MESH_VISUAL, gymapi.Vec3(rand_color[0],rand_color[1],rand_color[2]))
dof_props = self.gym.get_actor_dof_properties(env_ptr, torquepole_handle)
dof_props['driveMode'][:] = gymapi.DOF_MODE_EFFORT
dof_props['stiffness'][:] = 0.0
dof_props['damping'][:] = 0.0
dof_props['velocity'].fill(25.0)
dof_props['effort'].fill(0.0)
dof_props['friction'].fill(0.01)
self.gym.set_actor_dof_properties(env_ptr, torquepole_handle, dof_props)
self.envs.append(env_ptr)
self.torquepole_handles.append(torquepole_handle)
def compute_reward(self):
# retrieve environment observations from buffer
height = self.obs_buf[:, 6]
self.max_height_reached = torch.max(self.max_height_reached, height)
self.max_height_reached = torch.where((torch.norm(self.contact_forces[:, 3, :], dim=1) > 0.1), torch.zeros_like(self.max_height_reached), self.max_height_reached)
# print(self.max_height_reached[0:2])
self.rew_buf[:], self.reset_buf[:] = compute_torquepole_reward(height,
self.max_height_reached,
self.contact_forces,
self.reset_dist,
self.reset_buf,
self.progress_buf,
self.max_episode_length)
# print(self.rew_buf[0])
def convert_angle(self, angle):
# Apply sine and cosine functions
sin_component = torch.sin(angle)
cos_component = torch.cos(angle)
# Normalize angle to [-pi, pi]
normalized_angle = torch.remainder(angle + np.pi, 2 * np.pi) - np.pi
# Apply offset
# normalized_angle += np.pi
# Normalize again if needed
# normalized_angle = torch.remainder(normalized_angle + np.pi, 2 * np.pi) - np.pi
# Normalize angle to [-1, 1]
normalized_angle /= torch.pi
return sin_component, cos_component, normalized_angle
def compute_observations(self, env_ids=None):
if env_ids is None:
env_ids = np.arange(self.num_envs)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
sin_encode, cos_encode, motor_angle = self.convert_angle(self.dof_pos[env_ids, 1:3].squeeze())
self.obs_buf[env_ids, 0] = sin_encode[env_ids, 0] # Motor 0, Sin Component
self.obs_buf[env_ids, 1] = cos_encode[env_ids, 0] # Motor 0, Cos Component
self.obs_buf[env_ids, 2] = self.dof_vel[env_ids, 1]/20.0 # Motor 0, Velocity
self.obs_buf[env_ids, 3] = sin_encode[env_ids, 1] # Motor 1, Sin Component
self.obs_buf[env_ids, 4] = cos_encode[env_ids, 1] # Motor 1, Cos Component
self.obs_buf[env_ids, 5] = self.dof_vel[env_ids, 2]/20.0 # Motor 1, Velocity
self.obs_buf[env_ids, 6] = self.dof_pos[env_ids, 0] # Height
self.obs_buf[env_ids, 7:9] = self.actions_tensor[env_ids, 1:3]
return self.obs_buf
def reset_idx(self, env_ids):
print('reset_idx')
# Randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
positions = 2*np.pi*(torch.rand((len(env_ids), self.num_dof), device=self.device))
velocities = 5.0 * (torch.rand((len(env_ids), self.num_dof), device=self.device) - 0.5)
positions[:,0] = 0.0
velocities[:,0] = 0.0
positions[:,1] = 0.0
self.dof_pos[env_ids, :] = positions[:]
self.dof_vel[env_ids, :] = velocities[:]
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.glider_states[env_ids, ...] = modified_initial_state[env_ids, ...]
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
self.max_height_reached[env_ids] = 0
def pre_physics_step(self, actions):
self.actions_tensor = torch.zeros( [self.num_envs, self.num_dof], device=self.device, dtype=torch.float)
self.actions_tensor[:, 1:3] = actions.to(self.device) * self.max_push_effort
a = self.keys.get_keys()
scale = torch.tensor([10, self.max_push_effort, self.max_push_effort])
self.actions_tensor[0,:] = a*scale
forces = gymtorch.unwrap_tensor(self.actions_tensor)
self.gym.set_dof_actuation_force_tensor(self.sim, forces)
# print(actions_tensor[0])
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
self.compute_reward()
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_torquepole_reward(height, max_height_reached, contact_forces, reset_dist, reset_buf, progress_buf, max_episode_length):
# type: (Tensor, Tensor, Tensor, float, Tensor, Tensor, float) -> Tuple[Tensor, Tensor]
# reward = height**3
reward = max_height_reached**3
# reward = torch.where((torch.norm(contact_forces[:, 3, :], dim=1) > 0.1), torch.zeros_like(reward), reward)
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset_buf)
reset = reset | (torch.norm(contact_forces[:, 1, :], dim=1) > 1.)
reset = reset | (torch.norm(contact_forces[:, 2, :], dim=1) > 1.)
return reward, reset
| 12,905 | Python | 45.42446 | 217 | 0.601007 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/franka_cube_stack.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymtorch
from isaacgym import gymapi
from isaacgymenvs.utils.torch_jit_utils import quat_mul, to_torch, tensor_clamp
from isaacgymenvs.tasks.base.vec_task import VecTask
@torch.jit.script
def axisangle2quat(vec, eps=1e-6):
"""
Converts scaled axis-angle to quat.
Args:
vec (tensor): (..., 3) tensor where final dim is (ax,ay,az) axis-angle exponential coordinates
eps (float): Stability value below which small values will be mapped to 0
Returns:
tensor: (..., 4) tensor where final dim is (x,y,z,w) vec4 float quaternion
"""
# type: (Tensor, float) -> Tensor
# store input shape and reshape
input_shape = vec.shape[:-1]
vec = vec.reshape(-1, 3)
# Grab angle
angle = torch.norm(vec, dim=-1, keepdim=True)
# Create return array
quat = torch.zeros(torch.prod(torch.tensor(input_shape)), 4, device=vec.device)
quat[:, 3] = 1.0
# Grab indexes where angle is not zero an convert the input to its quaternion form
idx = angle.reshape(-1) > eps
quat[idx, :] = torch.cat([
vec[idx, :] * torch.sin(angle[idx, :] / 2.0) / angle[idx, :],
torch.cos(angle[idx, :] / 2.0)
], dim=-1)
# Reshape and return output
quat = quat.reshape(list(input_shape) + [4, ])
return quat
class FrankaCubeStack(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.action_scale = self.cfg["env"]["actionScale"]
self.start_position_noise = self.cfg["env"]["startPositionNoise"]
self.start_rotation_noise = self.cfg["env"]["startRotationNoise"]
self.franka_position_noise = self.cfg["env"]["frankaPositionNoise"]
self.franka_rotation_noise = self.cfg["env"]["frankaRotationNoise"]
self.franka_dof_noise = self.cfg["env"]["frankaDofNoise"]
self.aggregate_mode = self.cfg["env"]["aggregateMode"]
# Create dicts to pass to reward function
self.reward_settings = {
"r_dist_scale": self.cfg["env"]["distRewardScale"],
"r_lift_scale": self.cfg["env"]["liftRewardScale"],
"r_align_scale": self.cfg["env"]["alignRewardScale"],
"r_stack_scale": self.cfg["env"]["stackRewardScale"],
}
# Controller type
self.control_type = self.cfg["env"]["controlType"]
assert self.control_type in {"osc", "joint_tor"},\
"Invalid control type specified. Must be one of: {osc, joint_tor}"
# dimensions
# obs include: cubeA_pose (7) + cubeB_pos (3) + eef_pose (7) + q_gripper (2)
self.cfg["env"]["numObservations"] = 19 if self.control_type == "osc" else 26
# actions include: delta EEF if OSC (6) or joint torques (7) + bool gripper (1)
self.cfg["env"]["numActions"] = 7 if self.control_type == "osc" else 8
# Values to be filled in at runtime
self.states = {} # will be dict filled with relevant states to use for reward calculation
self.handles = {} # will be dict mapping names to relevant sim handles
self.num_dofs = None # Total number of DOFs per env
self.actions = None # Current actions to be deployed
self._init_cubeA_state = None # Initial state of cubeA for the current env
self._init_cubeB_state = None # Initial state of cubeB for the current env
self._cubeA_state = None # Current state of cubeA for the current env
self._cubeB_state = None # Current state of cubeB for the current env
self._cubeA_id = None # Actor ID corresponding to cubeA for a given env
self._cubeB_id = None # Actor ID corresponding to cubeB for a given env
# Tensor placeholders
self._root_state = None # State of root body (n_envs, 13)
self._dof_state = None # State of all joints (n_envs, n_dof)
self._q = None # Joint positions (n_envs, n_dof)
self._qd = None # Joint velocities (n_envs, n_dof)
self._rigid_body_state = None # State of all rigid bodies (n_envs, n_bodies, 13)
self._contact_forces = None # Contact forces in sim
self._eef_state = None # end effector state (at grasping point)
self._eef_lf_state = None # end effector state (at left fingertip)
self._eef_rf_state = None # end effector state (at left fingertip)
self._j_eef = None # Jacobian for end effector
self._mm = None # Mass matrix
self._arm_control = None # Tensor buffer for controlling arm
self._gripper_control = None # Tensor buffer for controlling gripper
self._pos_control = None # Position actions
self._effort_control = None # Torque actions
self._franka_effort_limits = None # Actuator effort limits for franka
self._global_indices = None # Unique indices corresponding to all envs in flattened array
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.up_axis = "z"
self.up_axis_idx = 2
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
# Franka defaults
self.franka_default_dof_pos = to_torch(
[0, 0.1963, 0, -2.6180, 0, 2.9416, 0.7854, 0.035, 0.035], device=self.device
)
# OSC Gains
self.kp = to_torch([150.] * 6, device=self.device)
self.kd = 2 * torch.sqrt(self.kp)
self.kp_null = to_torch([10.] * 7, device=self.device)
self.kd_null = 2 * torch.sqrt(self.kp_null)
#self.cmd_limit = None # filled in later
# Set control limits
self.cmd_limit = to_torch([0.1, 0.1, 0.1, 0.5, 0.5, 0.5], device=self.device).unsqueeze(0) if \
self.control_type == "osc" else self._franka_effort_limits[:7].unsqueeze(0)
# Reset all environments
self.reset_idx(torch.arange(self.num_envs, device=self.device))
# Refresh tensors
self._refresh()
def create_sim(self):
self.sim_params.up_axis = gymapi.UP_AXIS_Z
self.sim_params.gravity.x = 0
self.sim_params.gravity.y = 0
self.sim_params.gravity.z = -9.81
self.sim = super().create_sim(
self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets")
franka_asset_file = "urdf/franka_description/robots/franka_panda_gripper.urdf"
if "asset" in self.cfg["env"]:
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), self.cfg["env"]["asset"].get("assetRoot", asset_root))
franka_asset_file = self.cfg["env"]["asset"].get("assetFileNameFranka", franka_asset_file)
# load franka asset
asset_options = gymapi.AssetOptions()
asset_options.flip_visual_attachments = True
asset_options.fix_base_link = True
asset_options.collapse_fixed_joints = False
asset_options.disable_gravity = True
asset_options.thickness = 0.001
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_EFFORT
asset_options.use_mesh_materials = True
franka_asset = self.gym.load_asset(self.sim, asset_root, franka_asset_file, asset_options)
franka_dof_stiffness = to_torch([0, 0, 0, 0, 0, 0, 0, 5000., 5000.], dtype=torch.float, device=self.device)
franka_dof_damping = to_torch([0, 0, 0, 0, 0, 0, 0, 1.0e2, 1.0e2], dtype=torch.float, device=self.device)
# Create table asset
table_pos = [0.0, 0.0, 1.0]
table_thickness = 0.05
table_opts = gymapi.AssetOptions()
table_opts.fix_base_link = True
table_asset = self.gym.create_box(self.sim, *[1.2, 1.2, table_thickness], table_opts)
# Create table stand asset
table_stand_height = 0.1
table_stand_pos = [-0.5, 0.0, 1.0 + table_thickness / 2 + table_stand_height / 2]
table_stand_opts = gymapi.AssetOptions()
table_stand_opts.fix_base_link = True
table_stand_asset = self.gym.create_box(self.sim, *[0.2, 0.2, table_stand_height], table_opts)
self.cubeA_size = 0.050
self.cubeB_size = 0.070
# Create cubeA asset
cubeA_opts = gymapi.AssetOptions()
cubeA_asset = self.gym.create_box(self.sim, *([self.cubeA_size] * 3), cubeA_opts)
cubeA_color = gymapi.Vec3(0.6, 0.1, 0.0)
# Create cubeB asset
cubeB_opts = gymapi.AssetOptions()
cubeB_asset = self.gym.create_box(self.sim, *([self.cubeB_size] * 3), cubeB_opts)
cubeB_color = gymapi.Vec3(0.0, 0.4, 0.1)
self.num_franka_bodies = self.gym.get_asset_rigid_body_count(franka_asset)
self.num_franka_dofs = self.gym.get_asset_dof_count(franka_asset)
print("num franka bodies: ", self.num_franka_bodies)
print("num franka dofs: ", self.num_franka_dofs)
# set franka dof properties
franka_dof_props = self.gym.get_asset_dof_properties(franka_asset)
self.franka_dof_lower_limits = []
self.franka_dof_upper_limits = []
self._franka_effort_limits = []
for i in range(self.num_franka_dofs):
franka_dof_props['driveMode'][i] = gymapi.DOF_MODE_POS if i > 6 else gymapi.DOF_MODE_EFFORT
if self.physics_engine == gymapi.SIM_PHYSX:
franka_dof_props['stiffness'][i] = franka_dof_stiffness[i]
franka_dof_props['damping'][i] = franka_dof_damping[i]
else:
franka_dof_props['stiffness'][i] = 7000.0
franka_dof_props['damping'][i] = 50.0
self.franka_dof_lower_limits.append(franka_dof_props['lower'][i])
self.franka_dof_upper_limits.append(franka_dof_props['upper'][i])
self._franka_effort_limits.append(franka_dof_props['effort'][i])
self.franka_dof_lower_limits = to_torch(self.franka_dof_lower_limits, device=self.device)
self.franka_dof_upper_limits = to_torch(self.franka_dof_upper_limits, device=self.device)
self._franka_effort_limits = to_torch(self._franka_effort_limits, device=self.device)
self.franka_dof_speed_scales = torch.ones_like(self.franka_dof_lower_limits)
self.franka_dof_speed_scales[[7, 8]] = 0.1
franka_dof_props['effort'][7] = 200
franka_dof_props['effort'][8] = 200
# Define start pose for franka
franka_start_pose = gymapi.Transform()
franka_start_pose.p = gymapi.Vec3(-0.45, 0.0, 1.0 + table_thickness / 2 + table_stand_height)
franka_start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
# Define start pose for table
table_start_pose = gymapi.Transform()
table_start_pose.p = gymapi.Vec3(*table_pos)
table_start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
self._table_surface_pos = np.array(table_pos) + np.array([0, 0, table_thickness / 2])
self.reward_settings["table_height"] = self._table_surface_pos[2]
# Define start pose for table stand
table_stand_start_pose = gymapi.Transform()
table_stand_start_pose.p = gymapi.Vec3(*table_stand_pos)
table_stand_start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
# Define start pose for cubes (doesn't really matter since they're get overridden during reset() anyways)
cubeA_start_pose = gymapi.Transform()
cubeA_start_pose.p = gymapi.Vec3(-1.0, 0.0, 0.0)
cubeA_start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
cubeB_start_pose = gymapi.Transform()
cubeB_start_pose.p = gymapi.Vec3(1.0, 0.0, 0.0)
cubeB_start_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
# compute aggregate size
num_franka_bodies = self.gym.get_asset_rigid_body_count(franka_asset)
num_franka_shapes = self.gym.get_asset_rigid_shape_count(franka_asset)
max_agg_bodies = num_franka_bodies + 4 # 1 for table, table stand, cubeA, cubeB
max_agg_shapes = num_franka_shapes + 4 # 1 for table, table stand, cubeA, cubeB
self.frankas = []
self.envs = []
# Create environments
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row)
# Create actors and define aggregate group appropriately depending on setting
# NOTE: franka should ALWAYS be loaded first in sim!
if self.aggregate_mode >= 3:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
# Create franka
# Potentially randomize start pose
if self.franka_position_noise > 0:
rand_xy = self.franka_position_noise * (-1. + np.random.rand(2) * 2.0)
franka_start_pose.p = gymapi.Vec3(-0.45 + rand_xy[0], 0.0 + rand_xy[1],
1.0 + table_thickness / 2 + table_stand_height)
if self.franka_rotation_noise > 0:
rand_rot = torch.zeros(1, 3)
rand_rot[:, -1] = self.franka_rotation_noise * (-1. + np.random.rand() * 2.0)
new_quat = axisangle2quat(rand_rot).squeeze().numpy().tolist()
franka_start_pose.r = gymapi.Quat(*new_quat)
franka_actor = self.gym.create_actor(env_ptr, franka_asset, franka_start_pose, "franka", i, 0, 0)
self.gym.set_actor_dof_properties(env_ptr, franka_actor, franka_dof_props)
if self.aggregate_mode == 2:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
# Create table
table_actor = self.gym.create_actor(env_ptr, table_asset, table_start_pose, "table", i, 1, 0)
table_stand_actor = self.gym.create_actor(env_ptr, table_stand_asset, table_stand_start_pose, "table_stand",
i, 1, 0)
if self.aggregate_mode == 1:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
# Create cubes
self._cubeA_id = self.gym.create_actor(env_ptr, cubeA_asset, cubeA_start_pose, "cubeA", i, 2, 0)
self._cubeB_id = self.gym.create_actor(env_ptr, cubeB_asset, cubeB_start_pose, "cubeB", i, 4, 0)
# Set colors
self.gym.set_rigid_body_color(env_ptr, self._cubeA_id, 0, gymapi.MESH_VISUAL, cubeA_color)
self.gym.set_rigid_body_color(env_ptr, self._cubeB_id, 0, gymapi.MESH_VISUAL, cubeB_color)
if self.aggregate_mode > 0:
self.gym.end_aggregate(env_ptr)
# Store the created env pointers
self.envs.append(env_ptr)
self.frankas.append(franka_actor)
# Setup init state buffer
self._init_cubeA_state = torch.zeros(self.num_envs, 13, device=self.device)
self._init_cubeB_state = torch.zeros(self.num_envs, 13, device=self.device)
# Setup data
self.init_data()
def init_data(self):
# Setup sim handles
env_ptr = self.envs[0]
franka_handle = 0
self.handles = {
# Franka
"hand": self.gym.find_actor_rigid_body_handle(env_ptr, franka_handle, "panda_hand"),
"leftfinger_tip": self.gym.find_actor_rigid_body_handle(env_ptr, franka_handle, "panda_leftfinger_tip"),
"rightfinger_tip": self.gym.find_actor_rigid_body_handle(env_ptr, franka_handle, "panda_rightfinger_tip"),
"grip_site": self.gym.find_actor_rigid_body_handle(env_ptr, franka_handle, "panda_grip_site"),
# Cubes
"cubeA_body_handle": self.gym.find_actor_rigid_body_handle(self.envs[0], self._cubeA_id, "box"),
"cubeB_body_handle": self.gym.find_actor_rigid_body_handle(self.envs[0], self._cubeB_id, "box"),
}
# Get total DOFs
self.num_dofs = self.gym.get_sim_dof_count(self.sim) // self.num_envs
# Setup tensor buffers
_actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
_dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
_rigid_body_state_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
self._root_state = gymtorch.wrap_tensor(_actor_root_state_tensor).view(self.num_envs, -1, 13)
self._dof_state = gymtorch.wrap_tensor(_dof_state_tensor).view(self.num_envs, -1, 2)
self._rigid_body_state = gymtorch.wrap_tensor(_rigid_body_state_tensor).view(self.num_envs, -1, 13)
self._q = self._dof_state[..., 0]
self._qd = self._dof_state[..., 1]
self._eef_state = self._rigid_body_state[:, self.handles["grip_site"], :]
self._eef_lf_state = self._rigid_body_state[:, self.handles["leftfinger_tip"], :]
self._eef_rf_state = self._rigid_body_state[:, self.handles["rightfinger_tip"], :]
_jacobian = self.gym.acquire_jacobian_tensor(self.sim, "franka")
jacobian = gymtorch.wrap_tensor(_jacobian)
hand_joint_index = self.gym.get_actor_joint_dict(env_ptr, franka_handle)['panda_hand_joint']
self._j_eef = jacobian[:, hand_joint_index, :, :7]
_massmatrix = self.gym.acquire_mass_matrix_tensor(self.sim, "franka")
mm = gymtorch.wrap_tensor(_massmatrix)
self._mm = mm[:, :7, :7]
self._cubeA_state = self._root_state[:, self._cubeA_id, :]
self._cubeB_state = self._root_state[:, self._cubeB_id, :]
# Initialize states
self.states.update({
"cubeA_size": torch.ones_like(self._eef_state[:, 0]) * self.cubeA_size,
"cubeB_size": torch.ones_like(self._eef_state[:, 0]) * self.cubeB_size,
})
# Initialize actions
self._pos_control = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self._effort_control = torch.zeros_like(self._pos_control)
# Initialize control
self._arm_control = self._effort_control[:, :7]
self._gripper_control = self._pos_control[:, 7:9]
# Initialize indices
self._global_indices = torch.arange(self.num_envs * 5, dtype=torch.int32,
device=self.device).view(self.num_envs, -1)
def _update_states(self):
self.states.update({
# Franka
"q": self._q[:, :],
"q_gripper": self._q[:, -2:],
"eef_pos": self._eef_state[:, :3],
"eef_quat": self._eef_state[:, 3:7],
"eef_vel": self._eef_state[:, 7:],
"eef_lf_pos": self._eef_lf_state[:, :3],
"eef_rf_pos": self._eef_rf_state[:, :3],
# Cubes
"cubeA_quat": self._cubeA_state[:, 3:7],
"cubeA_pos": self._cubeA_state[:, :3],
"cubeA_pos_relative": self._cubeA_state[:, :3] - self._eef_state[:, :3],
"cubeB_quat": self._cubeB_state[:, 3:7],
"cubeB_pos": self._cubeB_state[:, :3],
"cubeA_to_cubeB_pos": self._cubeB_state[:, :3] - self._cubeA_state[:, :3],
})
def _refresh(self):
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
self.gym.refresh_jacobian_tensors(self.sim)
self.gym.refresh_mass_matrix_tensors(self.sim)
# Refresh states
self._update_states()
def compute_reward(self, actions):
self.rew_buf[:], self.reset_buf[:] = compute_franka_reward(
self.reset_buf, self.progress_buf, self.actions, self.states, self.reward_settings, self.max_episode_length
)
def compute_observations(self):
self._refresh()
obs = ["cubeA_quat", "cubeA_pos", "cubeA_to_cubeB_pos", "eef_pos", "eef_quat"]
obs += ["q_gripper"] if self.control_type == "osc" else ["q"]
self.obs_buf = torch.cat([self.states[ob] for ob in obs], dim=-1)
maxs = {ob: torch.max(self.states[ob]).item() for ob in obs}
return self.obs_buf
def reset_idx(self, env_ids):
env_ids_int32 = env_ids.to(dtype=torch.int32)
# Reset cubes, sampling cube B first, then A
# if not self._i:
self._reset_init_cube_state(cube='B', env_ids=env_ids, check_valid=False)
self._reset_init_cube_state(cube='A', env_ids=env_ids, check_valid=True)
# self._i = True
# Write these new init states to the sim states
self._cubeA_state[env_ids] = self._init_cubeA_state[env_ids]
self._cubeB_state[env_ids] = self._init_cubeB_state[env_ids]
# Reset agent
reset_noise = torch.rand((len(env_ids), 9), device=self.device)
pos = tensor_clamp(
self.franka_default_dof_pos.unsqueeze(0) +
self.franka_dof_noise * 2.0 * (reset_noise - 0.5),
self.franka_dof_lower_limits.unsqueeze(0), self.franka_dof_upper_limits)
# Overwrite gripper init pos (no noise since these are always position controlled)
pos[:, -2:] = self.franka_default_dof_pos[-2:]
# Reset the internal obs accordingly
self._q[env_ids, :] = pos
self._qd[env_ids, :] = torch.zeros_like(self._qd[env_ids])
# Set any position control to the current position, and any vel / effort control to be 0
# NOTE: Task takes care of actually propagating these controls in sim using the SimActions API
self._pos_control[env_ids, :] = pos
self._effort_control[env_ids, :] = torch.zeros_like(pos)
# Deploy updates
multi_env_ids_int32 = self._global_indices[env_ids, 0].flatten()
self.gym.set_dof_position_target_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self._pos_control),
gymtorch.unwrap_tensor(multi_env_ids_int32),
len(multi_env_ids_int32))
self.gym.set_dof_actuation_force_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self._effort_control),
gymtorch.unwrap_tensor(multi_env_ids_int32),
len(multi_env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self._dof_state),
gymtorch.unwrap_tensor(multi_env_ids_int32),
len(multi_env_ids_int32))
# Update cube states
multi_env_ids_cubes_int32 = self._global_indices[env_ids, -2:].flatten()
self.gym.set_actor_root_state_tensor_indexed(
self.sim, gymtorch.unwrap_tensor(self._root_state),
gymtorch.unwrap_tensor(multi_env_ids_cubes_int32), len(multi_env_ids_cubes_int32))
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
def _reset_init_cube_state(self, cube, env_ids, check_valid=True):
"""
Simple method to sample @cube's position based on self.startPositionNoise and self.startRotationNoise, and
automaticlly reset the pose internally. Populates the appropriate self._init_cubeX_state
If @check_valid is True, then this will also make sure that the sampled position is not in contact with the
other cube.
Args:
cube(str): Which cube to sample location for. Either 'A' or 'B'
env_ids (tensor or None): Specific environments to reset cube for
check_valid (bool): Whether to make sure sampled position is collision-free with the other cube.
"""
# If env_ids is None, we reset all the envs
if env_ids is None:
env_ids = torch.arange(start=0, end=self.num_envs, device=self.device, dtype=torch.long)
# Initialize buffer to hold sampled values
num_resets = len(env_ids)
sampled_cube_state = torch.zeros(num_resets, 13, device=self.device)
# Get correct references depending on which one was selected
if cube.lower() == 'a':
this_cube_state_all = self._init_cubeA_state
other_cube_state = self._init_cubeB_state[env_ids, :]
cube_heights = self.states["cubeA_size"]
elif cube.lower() == 'b':
this_cube_state_all = self._init_cubeB_state
other_cube_state = self._init_cubeA_state[env_ids, :]
cube_heights = self.states["cubeA_size"]
else:
raise ValueError(f"Invalid cube specified, options are 'A' and 'B'; got: {cube}")
# Minimum cube distance for guarenteed collision-free sampling is the sum of each cube's effective radius
min_dists = (self.states["cubeA_size"] + self.states["cubeB_size"])[env_ids] * np.sqrt(2) / 2.0
# We scale the min dist by 2 so that the cubes aren't too close together
min_dists = min_dists * 2.0
# Sampling is "centered" around middle of table
centered_cube_xy_state = torch.tensor(self._table_surface_pos[:2], device=self.device, dtype=torch.float32)
# Set z value, which is fixed height
sampled_cube_state[:, 2] = self._table_surface_pos[2] + cube_heights.squeeze(-1)[env_ids] / 2
# Initialize rotation, which is no rotation (quat w = 1)
sampled_cube_state[:, 6] = 1.0
# If we're verifying valid sampling, we need to check and re-sample if any are not collision-free
# We use a simple heuristic of checking based on cubes' radius to determine if a collision would occur
if check_valid:
success = False
# Indexes corresponding to envs we're still actively sampling for
active_idx = torch.arange(num_resets, device=self.device)
num_active_idx = len(active_idx)
for i in range(100):
# Sample x y values
sampled_cube_state[active_idx, :2] = centered_cube_xy_state + \
2.0 * self.start_position_noise * (
torch.rand_like(sampled_cube_state[active_idx, :2]) - 0.5)
# Check if sampled values are valid
cube_dist = torch.linalg.norm(sampled_cube_state[:, :2] - other_cube_state[:, :2], dim=-1)
active_idx = torch.nonzero(cube_dist < min_dists, as_tuple=True)[0]
num_active_idx = len(active_idx)
# If active idx is empty, then all sampling is valid :D
if num_active_idx == 0:
success = True
break
# Make sure we succeeded at sampling
assert success, "Sampling cube locations was unsuccessful! ):"
else:
# We just directly sample
sampled_cube_state[:, :2] = centered_cube_xy_state.unsqueeze(0) + \
2.0 * self.start_position_noise * (
torch.rand(num_resets, 2, device=self.device) - 0.5)
# Sample rotation value
if self.start_rotation_noise > 0:
aa_rot = torch.zeros(num_resets, 3, device=self.device)
aa_rot[:, 2] = 2.0 * self.start_rotation_noise * (torch.rand(num_resets, device=self.device) - 0.5)
sampled_cube_state[:, 3:7] = quat_mul(axisangle2quat(aa_rot), sampled_cube_state[:, 3:7])
# Lastly, set these sampled values as the new init state
this_cube_state_all[env_ids, :] = sampled_cube_state
def _compute_osc_torques(self, dpose):
# Solve for Operational Space Control # Paper: khatib.stanford.edu/publications/pdfs/Khatib_1987_RA.pdf
# Helpful resource: studywolf.wordpress.com/2013/09/17/robot-control-4-operation-space-control/
q, qd = self._q[:, :7], self._qd[:, :7]
mm_inv = torch.inverse(self._mm)
m_eef_inv = self._j_eef @ mm_inv @ torch.transpose(self._j_eef, 1, 2)
m_eef = torch.inverse(m_eef_inv)
# Transform our cartesian action `dpose` into joint torques `u`
u = torch.transpose(self._j_eef, 1, 2) @ m_eef @ (
self.kp * dpose - self.kd * self.states["eef_vel"]).unsqueeze(-1)
# Nullspace control torques `u_null` prevents large changes in joint configuration
# They are added into the nullspace of OSC so that the end effector orientation remains constant
# roboticsproceedings.org/rss07/p31.pdf
j_eef_inv = m_eef @ self._j_eef @ mm_inv
u_null = self.kd_null * -qd + self.kp_null * (
(self.franka_default_dof_pos[:7] - q + np.pi) % (2 * np.pi) - np.pi)
u_null[:, 7:] *= 0
u_null = self._mm @ u_null.unsqueeze(-1)
u += (torch.eye(7, device=self.device).unsqueeze(0) - torch.transpose(self._j_eef, 1, 2) @ j_eef_inv) @ u_null
# Clip the values to be within valid effort range
u = tensor_clamp(u.squeeze(-1),
-self._franka_effort_limits[:7].unsqueeze(0), self._franka_effort_limits[:7].unsqueeze(0))
return u
def pre_physics_step(self, actions):
self.actions = actions.clone().to(self.device)
# Split arm and gripper command
u_arm, u_gripper = self.actions[:, :-1], self.actions[:, -1]
# print(u_arm, u_gripper)
# print(self.cmd_limit, self.action_scale)
# Control arm (scale value first)
u_arm = u_arm * self.cmd_limit / self.action_scale
if self.control_type == "osc":
u_arm = self._compute_osc_torques(dpose=u_arm)
self._arm_control[:, :] = u_arm
# Control gripper
u_fingers = torch.zeros_like(self._gripper_control)
u_fingers[:, 0] = torch.where(u_gripper >= 0.0, self.franka_dof_upper_limits[-2].item(),
self.franka_dof_lower_limits[-2].item())
u_fingers[:, 1] = torch.where(u_gripper >= 0.0, self.franka_dof_upper_limits[-1].item(),
self.franka_dof_lower_limits[-1].item())
# Write gripper command to appropriate tensor buffer
self._gripper_control[:, :] = u_fingers
# Deploy actions
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(self._pos_control))
self.gym.set_dof_actuation_force_tensor(self.sim, gymtorch.unwrap_tensor(self._effort_control))
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
self.compute_reward(self.actions)
# debug viz
if self.viewer and self.debug_viz:
self.gym.clear_lines(self.viewer)
self.gym.refresh_rigid_body_state_tensor(self.sim)
# Grab relevant states to visualize
eef_pos = self.states["eef_pos"]
eef_rot = self.states["eef_quat"]
cubeA_pos = self.states["cubeA_pos"]
cubeA_rot = self.states["cubeA_quat"]
cubeB_pos = self.states["cubeB_pos"]
cubeB_rot = self.states["cubeB_quat"]
# Plot visualizations
for i in range(self.num_envs):
for pos, rot in zip((eef_pos, cubeA_pos, cubeB_pos), (eef_rot, cubeA_rot, cubeB_rot)):
px = (pos[i] + quat_apply(rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
py = (pos[i] + quat_apply(rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
pz = (pos[i] + quat_apply(rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], px[0], px[1], px[2]], [0.85, 0.1, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], py[0], py[1], py[2]], [0.1, 0.85, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], pz[0], pz[1], pz[2]], [0.1, 0.1, 0.85])
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_franka_reward(
reset_buf, progress_buf, actions, states, reward_settings, max_episode_length
):
# type: (Tensor, Tensor, Tensor, Dict[str, Tensor], Dict[str, float], float) -> Tuple[Tensor, Tensor]
# Compute per-env physical parameters
target_height = states["cubeB_size"] + states["cubeA_size"] / 2.0
cubeA_size = states["cubeA_size"]
cubeB_size = states["cubeB_size"]
# distance from hand to the cubeA
d = torch.norm(states["cubeA_pos_relative"], dim=-1)
d_lf = torch.norm(states["cubeA_pos"] - states["eef_lf_pos"], dim=-1)
d_rf = torch.norm(states["cubeA_pos"] - states["eef_rf_pos"], dim=-1)
dist_reward = 1 - torch.tanh(10.0 * (d + d_lf + d_rf) / 3)
# reward for lifting cubeA
cubeA_height = states["cubeA_pos"][:, 2] - reward_settings["table_height"]
cubeA_lifted = (cubeA_height - cubeA_size) > 0.04
lift_reward = cubeA_lifted
# how closely aligned cubeA is to cubeB (only provided if cubeA is lifted)
offset = torch.zeros_like(states["cubeA_to_cubeB_pos"])
offset[:, 2] = (cubeA_size + cubeB_size) / 2
d_ab = torch.norm(states["cubeA_to_cubeB_pos"] + offset, dim=-1)
align_reward = (1 - torch.tanh(10.0 * d_ab)) * cubeA_lifted
# Dist reward is maximum of dist and align reward
dist_reward = torch.max(dist_reward, align_reward)
# final reward for stacking successfully (only if cubeA is close to target height and corresponding location, and gripper is not grasping)
cubeA_align_cubeB = (torch.norm(states["cubeA_to_cubeB_pos"][:, :2], dim=-1) < 0.02)
cubeA_on_cubeB = torch.abs(cubeA_height - target_height) < 0.02
gripper_away_from_cubeA = (d > 0.04)
stack_reward = cubeA_align_cubeB & cubeA_on_cubeB & gripper_away_from_cubeA
# Compose rewards
# We either provide the stack reward or the align + dist reward
rewards = torch.where(
stack_reward,
reward_settings["r_stack_scale"] * stack_reward,
reward_settings["r_dist_scale"] * dist_reward + reward_settings["r_lift_scale"] * lift_reward + reward_settings[
"r_align_scale"] * align_reward,
)
# Compute resets
reset_buf = torch.where((progress_buf >= max_episode_length - 1) | (stack_reward > 0), torch.ones_like(reset_buf), reset_buf)
return rewards, reset_buf
| 37,426 | Python | 49.036096 | 217 | 0.595816 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/quadcopter.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import os
import torch
import xml.etree.ElementTree as ET
from isaacgym import gymutil, gymtorch, gymapi
from isaacgymenvs.utils.torch_jit_utils import *
from .base.vec_task import VecTask
class Quadcopter(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.max_episode_length = self.cfg["env"]["maxEpisodeLength"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
dofs_per_env = 8
bodies_per_env = 9
# Observations:
# 0:13 - root state
# 13:29 - DOF states
num_obs = 21
# Actions:
# 0:8 - rotor DOF position targets
# 8:12 - rotor thrust magnitudes
num_acts = 12
self.cfg["env"]["numObservations"] = num_obs
self.cfg["env"]["numActions"] = num_acts
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
self.root_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
self.dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
vec_root_tensor = gymtorch.wrap_tensor(self.root_tensor).view(self.num_envs, 13)
vec_dof_tensor = gymtorch.wrap_tensor(self.dof_state_tensor).view(self.num_envs, dofs_per_env, 2)
self.root_states = vec_root_tensor
self.root_positions = vec_root_tensor[..., 0:3]
self.root_quats = vec_root_tensor[..., 3:7]
self.root_linvels = vec_root_tensor[..., 7:10]
self.root_angvels = vec_root_tensor[..., 10:13]
self.dof_states = vec_dof_tensor
self.dof_positions = vec_dof_tensor[..., 0]
self.dof_velocities = vec_dof_tensor[..., 1]
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.initial_root_states = vec_root_tensor.clone()
self.initial_dof_states = vec_dof_tensor.clone()
max_thrust = 2
self.thrust_lower_limits = torch.zeros(4, device=self.device, dtype=torch.float32)
self.thrust_upper_limits = max_thrust * torch.ones(4, device=self.device, dtype=torch.float32)
# control tensors
self.dof_position_targets = torch.zeros((self.num_envs, dofs_per_env), dtype=torch.float32, device=self.device, requires_grad=False)
self.thrusts = torch.zeros((self.num_envs, 4), dtype=torch.float32, device=self.device, requires_grad=False)
self.forces = torch.zeros((self.num_envs, bodies_per_env, 3), dtype=torch.float32, device=self.device, requires_grad=False)
self.all_actor_indices = torch.arange(self.num_envs, dtype=torch.int32, device=self.device)
if self.viewer:
cam_pos = gymapi.Vec3(1.0, 1.0, 1.8)
cam_target = gymapi.Vec3(2.2, 2.0, 1.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# need rigid body states for visualizing thrusts
self.rb_state_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
self.rb_states = gymtorch.wrap_tensor(self.rb_state_tensor).view(self.num_envs, bodies_per_env, 13)
self.rb_positions = self.rb_states[..., 0:3]
self.rb_quats = self.rb_states[..., 3:7]
def create_sim(self):
self.sim_params.up_axis = gymapi.UP_AXIS_Z
self.sim_params.gravity.x = 0
self.sim_params.gravity.y = 0
self.sim_params.gravity.z = -9.81
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self.dt = self.sim_params.dt
self._create_quadcopter_asset()
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
def _create_quadcopter_asset(self):
chassis_radius = 0.1
chassis_thickness = 0.03
rotor_radius = 0.04
rotor_thickness = 0.01
rotor_arm_radius = 0.01
root = ET.Element('mujoco')
root.attrib["model"] = "Quadcopter"
compiler = ET.SubElement(root, "compiler")
compiler.attrib["angle"] = "degree"
compiler.attrib["coordinate"] = "local"
compiler.attrib["inertiafromgeom"] = "true"
worldbody = ET.SubElement(root, "worldbody")
chassis = ET.SubElement(worldbody, "body")
chassis.attrib["name"] = "chassis"
chassis.attrib["pos"] = "%g %g %g" % (0, 0, 0)
chassis_geom = ET.SubElement(chassis, "geom")
chassis_geom.attrib["type"] = "cylinder"
chassis_geom.attrib["size"] = "%g %g" % (chassis_radius, 0.5 * chassis_thickness)
chassis_geom.attrib["pos"] = "0 0 0"
chassis_geom.attrib["density"] = "50"
chassis_joint = ET.SubElement(chassis, "joint")
chassis_joint.attrib["name"] = "root_joint"
chassis_joint.attrib["type"] = "free"
zaxis = gymapi.Vec3(0, 0, 1)
rotor_arm_offset = gymapi.Vec3(chassis_radius + 0.25 * rotor_arm_radius, 0, 0)
pitch_joint_offset = gymapi.Vec3(0, 0, 0)
rotor_offset = gymapi.Vec3(rotor_radius + 0.25 * rotor_arm_radius, 0, 0)
rotor_angles = [0.25 * math.pi, 0.75 * math.pi, 1.25 * math.pi, 1.75 * math.pi]
for i in range(len(rotor_angles)):
angle = rotor_angles[i]
rotor_arm_quat = gymapi.Quat.from_axis_angle(zaxis, angle)
rotor_arm_pos = rotor_arm_quat.rotate(rotor_arm_offset)
pitch_joint_pos = pitch_joint_offset
rotor_pos = rotor_offset
rotor_quat = gymapi.Quat()
rotor_arm = ET.SubElement(chassis, "body")
rotor_arm.attrib["name"] = "rotor_arm" + str(i)
rotor_arm.attrib["pos"] = "%g %g %g" % (rotor_arm_pos.x, rotor_arm_pos.y, rotor_arm_pos.z)
rotor_arm.attrib["quat"] = "%g %g %g %g" % (rotor_arm_quat.w, rotor_arm_quat.x, rotor_arm_quat.y, rotor_arm_quat.z)
rotor_arm_geom = ET.SubElement(rotor_arm, "geom")
rotor_arm_geom.attrib["type"] = "sphere"
rotor_arm_geom.attrib["size"] = "%g" % rotor_arm_radius
rotor_arm_geom.attrib["density"] = "200"
pitch_joint = ET.SubElement(rotor_arm, "joint")
pitch_joint.attrib["name"] = "rotor_pitch" + str(i)
pitch_joint.attrib["type"] = "hinge"
pitch_joint.attrib["pos"] = "%g %g %g" % (0, 0, 0)
pitch_joint.attrib["axis"] = "0 1 0"
pitch_joint.attrib["limited"] = "true"
pitch_joint.attrib["range"] = "-30 30"
rotor = ET.SubElement(rotor_arm, "body")
rotor.attrib["name"] = "rotor" + str(i)
rotor.attrib["pos"] = "%g %g %g" % (rotor_pos.x, rotor_pos.y, rotor_pos.z)
rotor.attrib["quat"] = "%g %g %g %g" % (rotor_quat.w, rotor_quat.x, rotor_quat.y, rotor_quat.z)
rotor_geom = ET.SubElement(rotor, "geom")
rotor_geom.attrib["type"] = "cylinder"
rotor_geom.attrib["size"] = "%g %g" % (rotor_radius, 0.5 * rotor_thickness)
#rotor_geom.attrib["type"] = "box"
#rotor_geom.attrib["size"] = "%g %g %g" % (rotor_radius, rotor_radius, 0.5 * rotor_thickness)
rotor_geom.attrib["density"] = "1000"
roll_joint = ET.SubElement(rotor, "joint")
roll_joint.attrib["name"] = "rotor_roll" + str(i)
roll_joint.attrib["type"] = "hinge"
roll_joint.attrib["pos"] = "%g %g %g" % (0, 0, 0)
roll_joint.attrib["axis"] = "1 0 0"
roll_joint.attrib["limited"] = "true"
roll_joint.attrib["range"] = "-30 30"
gymutil._indent_xml(root)
ET.ElementTree(root).write("quadcopter.xml")
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = "."
asset_file = "quadcopter.xml"
asset_options = gymapi.AssetOptions()
asset_options.fix_base_link = False
asset_options.angular_damping = 0.0
asset_options.max_angular_velocity = 4 * math.pi
asset_options.slices_per_cylinder = 40
asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dofs = self.gym.get_asset_dof_count(asset)
dof_props = self.gym.get_asset_dof_properties(asset)
self.dof_lower_limits = []
self.dof_upper_limits = []
for i in range(self.num_dofs):
self.dof_lower_limits.append(dof_props['lower'][i])
self.dof_upper_limits.append(dof_props['upper'][i])
self.dof_lower_limits = to_torch(self.dof_lower_limits, device=self.device)
self.dof_upper_limits = to_torch(self.dof_upper_limits, device=self.device)
self.dof_ranges = self.dof_upper_limits - self.dof_lower_limits
default_pose = gymapi.Transform()
default_pose.p.z = 1.0
self.envs = []
for i in range(self.num_envs):
# create env instance
env = self.gym.create_env(self.sim, lower, upper, num_per_row)
actor_handle = self.gym.create_actor(env, asset, default_pose, "quadcopter", i, 1, 0)
dof_props = self.gym.get_actor_dof_properties(env, actor_handle)
dof_props['driveMode'].fill(gymapi.DOF_MODE_POS)
dof_props['stiffness'].fill(1000.0)
dof_props['damping'].fill(0.0)
self.gym.set_actor_dof_properties(env, actor_handle, dof_props)
# pretty colors
chassis_color = gymapi.Vec3(0.8, 0.6, 0.2)
rotor_color = gymapi.Vec3(0.1, 0.2, 0.6)
arm_color = gymapi.Vec3(0.0, 0.0, 0.0)
self.gym.set_rigid_body_color(env, actor_handle, 0, gymapi.MESH_VISUAL_AND_COLLISION, chassis_color)
self.gym.set_rigid_body_color(env, actor_handle, 1, gymapi.MESH_VISUAL_AND_COLLISION, arm_color)
self.gym.set_rigid_body_color(env, actor_handle, 3, gymapi.MESH_VISUAL_AND_COLLISION, arm_color)
self.gym.set_rigid_body_color(env, actor_handle, 5, gymapi.MESH_VISUAL_AND_COLLISION, arm_color)
self.gym.set_rigid_body_color(env, actor_handle, 7, gymapi.MESH_VISUAL_AND_COLLISION, arm_color)
self.gym.set_rigid_body_color(env, actor_handle, 2, gymapi.MESH_VISUAL_AND_COLLISION, rotor_color)
self.gym.set_rigid_body_color(env, actor_handle, 4, gymapi.MESH_VISUAL_AND_COLLISION, rotor_color)
self.gym.set_rigid_body_color(env, actor_handle, 6, gymapi.MESH_VISUAL_AND_COLLISION, rotor_color)
self.gym.set_rigid_body_color(env, actor_handle, 8, gymapi.MESH_VISUAL_AND_COLLISION, rotor_color)
#self.gym.set_rigid_body_color(env, actor_handle, 2, gymapi.MESH_VISUAL_AND_COLLISION, gymapi.Vec3(1, 0, 0))
#self.gym.set_rigid_body_color(env, actor_handle, 4, gymapi.MESH_VISUAL_AND_COLLISION, gymapi.Vec3(0, 1, 0))
#self.gym.set_rigid_body_color(env, actor_handle, 6, gymapi.MESH_VISUAL_AND_COLLISION, gymapi.Vec3(0, 0, 1))
#self.gym.set_rigid_body_color(env, actor_handle, 8, gymapi.MESH_VISUAL_AND_COLLISION, gymapi.Vec3(1, 1, 0))
self.envs.append(env)
if self.debug_viz:
# need env offsets for the rotors
self.rotor_env_offsets = torch.zeros((self.num_envs, 4, 3), device=self.device)
for i in range(self.num_envs):
env_origin = self.gym.get_env_origin(self.envs[i])
self.rotor_env_offsets[i, ..., 0] = env_origin.x
self.rotor_env_offsets[i, ..., 1] = env_origin.y
self.rotor_env_offsets[i, ..., 2] = env_origin.z
def reset_idx(self, env_ids):
num_resets = len(env_ids)
self.dof_states[env_ids] = self.initial_dof_states[env_ids]
actor_indices = self.all_actor_indices[env_ids].flatten()
self.root_states[env_ids] = self.initial_root_states[env_ids]
self.root_states[env_ids, 0] += torch_rand_float(-1.5, 1.5, (num_resets, 1), self.device).flatten()
self.root_states[env_ids, 1] += torch_rand_float(-1.5, 1.5, (num_resets, 1), self.device).flatten()
self.root_states[env_ids, 2] += torch_rand_float(-0.2, 1.5, (num_resets, 1), self.device).flatten()
self.gym.set_actor_root_state_tensor_indexed(self.sim, self.root_tensor, gymtorch.unwrap_tensor(actor_indices), num_resets)
self.dof_positions[env_ids] = torch_rand_float(-0.2, 0.2, (num_resets, 8), self.device)
self.dof_velocities[env_ids] = 0.0
self.gym.set_dof_state_tensor_indexed(self.sim, self.dof_state_tensor, gymtorch.unwrap_tensor(actor_indices), num_resets)
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def pre_physics_step(self, _actions):
# resets
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
actions = _actions.to(self.device)
dof_action_speed_scale = 8 * math.pi
self.dof_position_targets += self.dt * dof_action_speed_scale * actions[:, 0:8]
self.dof_position_targets[:] = tensor_clamp(self.dof_position_targets, self.dof_lower_limits, self.dof_upper_limits)
thrust_action_speed_scale = 200
self.thrusts += self.dt * thrust_action_speed_scale * actions[:, 8:12]
self.thrusts[:] = tensor_clamp(self.thrusts, self.thrust_lower_limits, self.thrust_upper_limits)
self.forces[:, 2, 2] = self.thrusts[:, 0]
self.forces[:, 4, 2] = self.thrusts[:, 1]
self.forces[:, 6, 2] = self.thrusts[:, 2]
self.forces[:, 8, 2] = self.thrusts[:, 3]
# clear actions for reset envs
self.thrusts[reset_env_ids] = 0.0
self.forces[reset_env_ids] = 0.0
self.dof_position_targets[reset_env_ids] = self.dof_positions[reset_env_ids]
# apply actions
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(self.dof_position_targets))
self.gym.apply_rigid_body_force_tensors(self.sim, gymtorch.unwrap_tensor(self.forces), None, gymapi.LOCAL_SPACE)
def post_physics_step(self):
self.progress_buf += 1
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.compute_observations()
self.compute_reward()
# debug viz
if self.viewer and self.debug_viz:
# compute start and end positions for visualizing thrust lines
self.gym.refresh_rigid_body_state_tensor(self.sim)
rotor_indices = torch.LongTensor([2, 4, 6, 8])
quats = self.rb_quats[:, rotor_indices]
dirs = -quat_axis(quats.view(self.num_envs * 4, 4), 2).view(self.num_envs, 4, 3)
starts = self.rb_positions[:, rotor_indices] + self.rotor_env_offsets
ends = starts + 0.1 * self.thrusts.view(self.num_envs, 4, 1) * dirs
# submit debug line geometry
verts = torch.stack([starts, ends], dim=2).cpu().numpy()
colors = np.zeros((self.num_envs * 4, 3), dtype=np.float32)
colors[..., 0] = 1.0
self.gym.clear_lines(self.viewer)
self.gym.add_lines(self.viewer, None, self.num_envs * 4, verts, colors)
def compute_observations(self):
target_x = 0.0
target_y = 0.0
target_z = 1.0
self.obs_buf[..., 0] = (target_x - self.root_positions[..., 0]) / 3
self.obs_buf[..., 1] = (target_y - self.root_positions[..., 1]) / 3
self.obs_buf[..., 2] = (target_z - self.root_positions[..., 2]) / 3
self.obs_buf[..., 3:7] = self.root_quats
self.obs_buf[..., 7:10] = self.root_linvels / 2
self.obs_buf[..., 10:13] = self.root_angvels / math.pi
self.obs_buf[..., 13:21] = self.dof_positions
return self.obs_buf
def compute_reward(self):
self.rew_buf[:], self.reset_buf[:] = compute_quadcopter_reward(
self.root_positions,
self.root_quats,
self.root_linvels,
self.root_angvels,
self.reset_buf, self.progress_buf, self.max_episode_length
)
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_quadcopter_reward(root_positions, root_quats, root_linvels, root_angvels, reset_buf, progress_buf, max_episode_length):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, float) -> Tuple[Tensor, Tensor]
# distance to target
target_dist = torch.sqrt(root_positions[..., 0] * root_positions[..., 0] +
root_positions[..., 1] * root_positions[..., 1] +
(1 - root_positions[..., 2]) * (1 - root_positions[..., 2]))
pos_reward = 1.0 / (1.0 + target_dist * target_dist)
# uprightness
ups = quat_axis(root_quats, 2)
tiltage = torch.abs(1 - ups[..., 2])
up_reward = 1.0 / (1.0 + tiltage * tiltage)
# spinning
spinnage = torch.abs(root_angvels[..., 2])
spinnage_reward = 1.0 / (1.0 + spinnage * spinnage)
# combined reward
# uprigness and spinning only matter when close to the target
reward = pos_reward + pos_reward * (up_reward + spinnage_reward)
# resets due to misbehavior
ones = torch.ones_like(reset_buf)
die = torch.zeros_like(reset_buf)
die = torch.where(target_dist > 3.0, ones, die)
die = torch.where(root_positions[..., 2] < 0.3, ones, die)
# resets due to episode length
reset = torch.where(progress_buf >= max_episode_length - 1, ones, die)
return reward, reset
| 19,725 | Python | 46.078759 | 217 | 0.61308 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/ingenuity.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import os
import torch
import xml.etree.ElementTree as ET
from isaacgymenvs.utils.torch_jit_utils import *
from .base.vec_task import VecTask
from isaacgym import gymutil, gymtorch, gymapi
class Ingenuity(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.max_episode_length = self.cfg["env"]["maxEpisodeLength"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
# Observations:
# 0:13 - root state
self.cfg["env"]["numObservations"] = 13
# Actions:
# 0:3 - xyz force vector for lower rotor
# 4:6 - xyz force vector for upper rotor
self.cfg["env"]["numActions"] = 6
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
dofs_per_env = 4
bodies_per_env = 6
self.root_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
self.dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
vec_root_tensor = gymtorch.wrap_tensor(self.root_tensor).view(self.num_envs, 2, 13)
vec_dof_tensor = gymtorch.wrap_tensor(self.dof_state_tensor).view(self.num_envs, dofs_per_env, 2)
self.root_states = vec_root_tensor[:, 0, :]
self.root_positions = self.root_states[:, 0:3]
self.target_root_positions = torch.zeros((self.num_envs, 3), device=self.device, dtype=torch.float32)
self.target_root_positions[:, 2] = 1
self.root_quats = self.root_states[:, 3:7]
self.root_linvels = self.root_states[:, 7:10]
self.root_angvels = self.root_states[:, 10:13]
self.marker_states = vec_root_tensor[:, 1, :]
self.marker_positions = self.marker_states[:, 0:3]
self.dof_states = vec_dof_tensor
self.dof_positions = vec_dof_tensor[..., 0]
self.dof_velocities = vec_dof_tensor[..., 1]
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.initial_root_states = self.root_states.clone()
self.initial_dof_states = self.dof_states.clone()
self.thrust_lower_limit = 0
self.thrust_upper_limit = 2000
self.thrust_lateral_component = 0.2
# control tensors
self.thrusts = torch.zeros((self.num_envs, 2, 3), dtype=torch.float32, device=self.device, requires_grad=False)
self.forces = torch.zeros((self.num_envs, bodies_per_env, 3), dtype=torch.float32, device=self.device, requires_grad=False)
self.all_actor_indices = torch.arange(self.num_envs * 2, dtype=torch.int32, device=self.device).reshape((self.num_envs, 2))
if self.viewer:
cam_pos = gymapi.Vec3(2.25, 2.25, 3.0)
cam_target = gymapi.Vec3(3.5, 4.0, 1.9)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# need rigid body states for visualizing thrusts
self.rb_state_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
self.rb_states = gymtorch.wrap_tensor(self.rb_state_tensor).view(self.num_envs, bodies_per_env, 13)
self.rb_positions = self.rb_states[..., 0:3]
self.rb_quats = self.rb_states[..., 3:7]
def create_sim(self):
self.sim_params.up_axis = gymapi.UP_AXIS_Z
# Mars gravity
self.sim_params.gravity.x = 0
self.sim_params.gravity.y = 0
self.sim_params.gravity.z = -3.721
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self.dt = self.sim_params.dt
self._create_ingenuity_asset()
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
def _create_ingenuity_asset(self):
chassis_size = 0.06
rotor_axis_length = 0.2
rotor_radius = 0.15
rotor_thickness = 0.01
rotor_arm_radius = 0.01
root = ET.Element('mujoco')
root.attrib["model"] = "Ingenuity"
compiler = ET.SubElement(root, "compiler")
compiler.attrib["angle"] = "degree"
compiler.attrib["coordinate"] = "local"
compiler.attrib["inertiafromgeom"] = "true"
mesh_asset = ET.SubElement(root, "asset")
model_path = "../assets/glb/ingenuity/"
mesh = ET.SubElement(mesh_asset, "mesh")
mesh.attrib["file"] = model_path + "chassis.glb"
mesh.attrib["name"] = "ingenuity_mesh"
lower_prop_mesh = ET.SubElement(mesh_asset, "mesh")
lower_prop_mesh.attrib["file"] = model_path + "lower_prop.glb"
lower_prop_mesh.attrib["name"] = "lower_prop_mesh"
upper_prop_mesh = ET.SubElement(mesh_asset, "mesh")
upper_prop_mesh.attrib["file"] = model_path + "upper_prop.glb"
upper_prop_mesh.attrib["name"] = "upper_prop_mesh"
worldbody = ET.SubElement(root, "worldbody")
chassis = ET.SubElement(worldbody, "body")
chassis.attrib["name"] = "chassis"
chassis.attrib["pos"] = "%g %g %g" % (0, 0, 0)
chassis_geom = ET.SubElement(chassis, "geom")
chassis_geom.attrib["type"] = "box"
chassis_geom.attrib["size"] = "%g %g %g" % (chassis_size, chassis_size, chassis_size)
chassis_geom.attrib["pos"] = "0 0 0"
chassis_geom.attrib["density"] = "50"
mesh_quat = gymapi.Quat.from_euler_zyx(0.5 * math.pi, 0, 0)
mesh_geom = ET.SubElement(chassis, "geom")
mesh_geom.attrib["type"] = "mesh"
mesh_geom.attrib["quat"] = "%g %g %g %g" % (mesh_quat.w, mesh_quat.x, mesh_quat.y, mesh_quat.z)
mesh_geom.attrib["mesh"] = "ingenuity_mesh"
mesh_geom.attrib["pos"] = "%g %g %g" % (0, 0, 0)
mesh_geom.attrib["contype"] = "0"
mesh_geom.attrib["conaffinity"] = "0"
chassis_joint = ET.SubElement(chassis, "joint")
chassis_joint.attrib["name"] = "root_joint"
chassis_joint.attrib["type"] = "hinge"
chassis_joint.attrib["limited"] = "true"
chassis_joint.attrib["range"] = "0 0"
zaxis = gymapi.Vec3(0, 0, 1)
low_rotor_pos = gymapi.Vec3(0, 0, 0)
rotor_separation = gymapi.Vec3(0, 0, 0.025)
for i, mesh_name in enumerate(["lower_prop_mesh", "upper_prop_mesh"]):
angle = 0
rotor_quat = gymapi.Quat.from_axis_angle(zaxis, angle)
rotor_pos = low_rotor_pos + (rotor_separation * i)
rotor = ET.SubElement(chassis, "body")
rotor.attrib["name"] = "rotor_physics_" + str(i)
rotor.attrib["pos"] = "%g %g %g" % (rotor_pos.x, rotor_pos.y, rotor_pos.z)
rotor.attrib["quat"] = "%g %g %g %g" % (rotor_quat.w, rotor_quat.x, rotor_quat.y, rotor_quat.z)
rotor_geom = ET.SubElement(rotor, "geom")
rotor_geom.attrib["type"] = "cylinder"
rotor_geom.attrib["size"] = "%g %g" % (rotor_radius, 0.5 * rotor_thickness)
rotor_geom.attrib["density"] = "1000"
roll_joint = ET.SubElement(rotor, "joint")
roll_joint.attrib["name"] = "rotor_roll" + str(i)
roll_joint.attrib["type"] = "hinge"
roll_joint.attrib["limited"] = "true"
roll_joint.attrib["range"] = "0 0"
roll_joint.attrib["pos"] = "%g %g %g" % (0, 0, 0)
rotor_dummy = ET.SubElement(chassis, "body")
rotor_dummy.attrib["name"] = "rotor_visual_" + str(i)
rotor_dummy.attrib["pos"] = "%g %g %g" % (rotor_pos.x, rotor_pos.y, rotor_pos.z)
rotor_dummy.attrib["quat"] = "%g %g %g %g" % (rotor_quat.w, rotor_quat.x, rotor_quat.y, rotor_quat.z)
rotor_mesh_geom = ET.SubElement(rotor_dummy, "geom")
rotor_mesh_geom.attrib["type"] = "mesh"
rotor_mesh_geom.attrib["mesh"] = mesh_name
rotor_mesh_quat = gymapi.Quat.from_euler_zyx(0.5 * math.pi, 0, 0)
rotor_mesh_geom.attrib["quat"] = "%g %g %g %g" % (rotor_mesh_quat.w, rotor_mesh_quat.x, rotor_mesh_quat.y, rotor_mesh_quat.z)
rotor_mesh_geom.attrib["contype"] = "0"
rotor_mesh_geom.attrib["conaffinity"] = "0"
dummy_roll_joint = ET.SubElement(rotor_dummy, "joint")
dummy_roll_joint.attrib["name"] = "rotor_roll" + str(i)
dummy_roll_joint.attrib["type"] = "hinge"
dummy_roll_joint.attrib["axis"] = "0 0 1"
dummy_roll_joint.attrib["pos"] = "%g %g %g" % (0, 0, 0)
gymutil._indent_xml(root)
ET.ElementTree(root).write("ingenuity.xml")
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = "./"
asset_file = "ingenuity.xml"
asset_options = gymapi.AssetOptions()
asset_options.fix_base_link = False
asset_options.angular_damping = 0.0
asset_options.max_angular_velocity = 4 * math.pi
asset_options.slices_per_cylinder = 40
asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
asset_options.fix_base_link = True
marker_asset = self.gym.create_sphere(self.sim, 0.1, asset_options)
default_pose = gymapi.Transform()
default_pose.p.z = 1.0
self.envs = []
self.actor_handles = []
for i in range(self.num_envs):
# create env instance
env = self.gym.create_env(self.sim, lower, upper, num_per_row)
actor_handle = self.gym.create_actor(env, asset, default_pose, "ingenuity", i, 1, 1)
dof_props = self.gym.get_actor_dof_properties(env, actor_handle)
dof_props['stiffness'].fill(0)
dof_props['damping'].fill(0)
self.gym.set_actor_dof_properties(env, actor_handle, dof_props)
marker_handle = self.gym.create_actor(env, marker_asset, default_pose, "marker", i, 1, 1)
self.gym.set_rigid_body_color(env, marker_handle, 0, gymapi.MESH_VISUAL_AND_COLLISION, gymapi.Vec3(1, 0, 0))
self.actor_handles.append(actor_handle)
self.envs.append(env)
if self.debug_viz:
# need env offsets for the rotors
self.rotor_env_offsets = torch.zeros((self.num_envs, 2, 3), device=self.device)
for i in range(self.num_envs):
env_origin = self.gym.get_env_origin(self.envs[i])
self.rotor_env_offsets[i, ..., 0] = env_origin.x
self.rotor_env_offsets[i, ..., 1] = env_origin.y
self.rotor_env_offsets[i, ..., 2] = env_origin.z
def set_targets(self, env_ids):
num_sets = len(env_ids)
# set target position randomly with x, y in (-5, 5) and z in (1, 2)
self.target_root_positions[env_ids, 0:2] = (torch.rand(num_sets, 2, device=self.device) * 10) - 5
self.target_root_positions[env_ids, 2] = torch.rand(num_sets, device=self.device) + 1
self.marker_positions[env_ids] = self.target_root_positions[env_ids]
# copter "position" is at the bottom of the legs, so shift the target up so it visually aligns better
self.marker_positions[env_ids, 2] += 0.4
actor_indices = self.all_actor_indices[env_ids, 1].flatten()
return actor_indices
def reset_idx(self, env_ids):
# set rotor speeds
self.dof_velocities[:, 1] = -50
self.dof_velocities[:, 3] = 50
num_resets = len(env_ids)
target_actor_indices = self.set_targets(env_ids)
actor_indices = self.all_actor_indices[env_ids, 0].flatten()
self.root_states[env_ids] = self.initial_root_states[env_ids]
self.root_states[env_ids, 0] += torch_rand_float(-1.5, 1.5, (num_resets, 1), self.device).flatten()
self.root_states[env_ids, 1] += torch_rand_float(-1.5, 1.5, (num_resets, 1), self.device).flatten()
self.root_states[env_ids, 2] += torch_rand_float(-0.2, 1.5, (num_resets, 1), self.device).flatten()
self.gym.set_dof_state_tensor_indexed(self.sim, self.dof_state_tensor, gymtorch.unwrap_tensor(actor_indices), num_resets)
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
return torch.unique(torch.cat([target_actor_indices, actor_indices]))
def pre_physics_step(self, _actions):
# resets
set_target_ids = (self.progress_buf % 500 == 0).nonzero(as_tuple=False).squeeze(-1)
target_actor_indices = torch.tensor([], device=self.device, dtype=torch.int32)
if len(set_target_ids) > 0:
target_actor_indices = self.set_targets(set_target_ids)
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
actor_indices = torch.tensor([], device=self.device, dtype=torch.int32)
if len(reset_env_ids) > 0:
actor_indices = self.reset_idx(reset_env_ids)
reset_indices = torch.unique(torch.cat([target_actor_indices, actor_indices]))
if len(reset_indices) > 0:
self.gym.set_actor_root_state_tensor_indexed(self.sim, self.root_tensor, gymtorch.unwrap_tensor(reset_indices), len(reset_indices))
actions = _actions.to(self.device)
thrust_action_speed_scale = 2000
vertical_thrust_prop_0 = torch.clamp(actions[:, 2] * thrust_action_speed_scale, -self.thrust_upper_limit, self.thrust_upper_limit)
vertical_thrust_prop_1 = torch.clamp(actions[:, 5] * thrust_action_speed_scale, -self.thrust_upper_limit, self.thrust_upper_limit)
lateral_fraction_prop_0 = torch.clamp(actions[:, 0:2], -self.thrust_lateral_component, self.thrust_lateral_component)
lateral_fraction_prop_1 = torch.clamp(actions[:, 3:5], -self.thrust_lateral_component, self.thrust_lateral_component)
self.thrusts[:, 0, 2] = self.dt * vertical_thrust_prop_0
self.thrusts[:, 0, 0:2] = self.thrusts[:, 0, 2, None] * lateral_fraction_prop_0
self.thrusts[:, 1, 2] = self.dt * vertical_thrust_prop_1
self.thrusts[:, 1, 0:2] = self.thrusts[:, 1, 2, None] * lateral_fraction_prop_1
self.forces[:, 1] = self.thrusts[:, 0]
self.forces[:, 3] = self.thrusts[:, 1]
# clear actions for reset envs
self.thrusts[reset_env_ids] = 0.0
self.forces[reset_env_ids] = 0.0
# apply actions
self.gym.apply_rigid_body_force_tensors(self.sim, gymtorch.unwrap_tensor(self.forces), None, gymapi.LOCAL_SPACE)
def post_physics_step(self):
self.progress_buf += 1
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.compute_observations()
self.compute_reward()
# debug viz
if self.viewer and self.debug_viz:
# compute start and end positions for visualizing thrust lines
self.gym.refresh_rigid_body_state_tensor(self.sim)
rotor_indices = torch.LongTensor([2, 4, 6, 8])
quats = self.rb_quats[:, rotor_indices]
dirs = -quat_axis(quats.view(self.num_envs * 4, 4), 2).view(self.num_envs, 4, 3)
starts = self.rb_positions[:, rotor_indices] + self.rotor_env_offsets
ends = starts + 0.1 * self.thrusts.view(self.num_envs, 4, 1) * dirs
# submit debug line geometry
verts = torch.stack([starts, ends], dim=2).cpu().numpy()
colors = np.zeros((self.num_envs * 4, 3), dtype=np.float32)
colors[..., 0] = 1.0
self.gym.clear_lines(self.viewer)
self.gym.add_lines(self.viewer, None, self.num_envs * 4, verts, colors)
def compute_observations(self):
self.obs_buf[..., 0:3] = (self.target_root_positions - self.root_positions) / 3
self.obs_buf[..., 3:7] = self.root_quats
self.obs_buf[..., 7:10] = self.root_linvels / 2
self.obs_buf[..., 10:13] = self.root_angvels / math.pi
return self.obs_buf
def compute_reward(self):
self.rew_buf[:], self.reset_buf[:] = compute_ingenuity_reward(
self.root_positions,
self.target_root_positions,
self.root_quats,
self.root_linvels,
self.root_angvels,
self.reset_buf, self.progress_buf, self.max_episode_length
)
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_ingenuity_reward(root_positions, target_root_positions, root_quats, root_linvels, root_angvels, reset_buf, progress_buf, max_episode_length):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, float) -> Tuple[Tensor, Tensor]
# distance to target
target_dist = torch.sqrt(torch.square(target_root_positions - root_positions).sum(-1))
pos_reward = 1.0 / (1.0 + target_dist * target_dist)
# uprightness
ups = quat_axis(root_quats, 2)
tiltage = torch.abs(1 - ups[..., 2])
up_reward = 5.0 / (1.0 + tiltage * tiltage)
# spinning
spinnage = torch.abs(root_angvels[..., 2])
spinnage_reward = 1.0 / (1.0 + spinnage * spinnage)
# combined reward
# uprigness and spinning only matter when close to the target
reward = pos_reward + pos_reward * (up_reward + spinnage_reward)
# resets due to misbehavior
ones = torch.ones_like(reset_buf)
die = torch.zeros_like(reset_buf)
die = torch.where(target_dist > 8.0, ones, die)
die = torch.where(root_positions[..., 2] < 0.5, ones, die)
# resets due to episode length
reset = torch.where(progress_buf >= max_episode_length - 1, ones, die)
return reward, reset
| 19,671 | Python | 43.60771 | 217 | 0.614763 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/torquepole.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymutil, gymtorch, gymapi
from .base.vec_task import VecTask
from .keyboard import Keyboard
class TorquePole(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.reset_dist = self.cfg["env"]["resetDist"]
self.max_push_effort = self.cfg["env"]["maxEffort"]
self.max_episode_length = self.cfg["env"]["maxEpisodeLen"]
self.cfg["env"]["numObservations"] = 3
self.cfg["env"]["numActions"] = 1
# randomization
self.randomization_params = self.cfg["task"]["randomization_params"]
self.randomize = self.cfg["task"]["randomize"]
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
self.keys = Keyboard()
def create_sim(self):
# set the up axis to be z-up given that assets are y-up by default
self.up_axis = self.cfg["sim"]["up_axis"]
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
# self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
# set the normal force to be z dimension
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0) if self.up_axis == 'z' else gymapi.Vec3(0.0, 1.0, 0.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
# define plane on which environments are initialized
lower = gymapi.Vec3(-spacing, -spacing, 0.0) if self.up_axis == 'z' else gymapi.Vec3(0.5 * -spacing, 0.0, -spacing)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../assets")
asset_file = "urdf/TorquePole/urdf/TorquePole.urdf"
if "asset" in self.cfg["env"]:
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), self.cfg["env"]["asset"].get("assetRoot", asset_root))
asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)
asset_path = os.path.join(asset_root, asset_file)
asset_root = os.path.dirname(asset_path)
asset_file = os.path.basename(asset_path)
asset_options = gymapi.AssetOptions()
asset_options.fix_base_link = True
torquepole_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dof = self.gym.get_asset_dof_count(torquepole_asset)
pose = gymapi.Transform()
if self.up_axis == 'z':
pose.p.z = 2.0
# asset is rotated z-up by default, no additional rotations needed
# pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
pose.r = gymapi.Quat.from_euler_zyx(1.5708, 0.0, 0.0)
else:
pose.p.y = 2.0
pose.r = gymapi.Quat(-np.sqrt(2)/2, 0.0, 0.0, np.sqrt(2)/2)
self.torquepole_handles = []
self.envs = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
torquepole_handle = self.gym.create_actor(env_ptr, torquepole_asset, pose, "torquepole", i, 1, 0)
rand_color = torch.rand((3), device=self.device)
self.gym.set_rigid_body_color(env_ptr, torquepole_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(rand_color[0],rand_color[1],rand_color[2]))
rand_color = torch.rand((3), device=self.device)
self.gym.set_rigid_body_color(env_ptr, torquepole_handle, 1, gymapi.MESH_VISUAL, gymapi.Vec3(rand_color[0],rand_color[1],rand_color[2]))
dof_props = self.gym.get_actor_dof_properties(env_ptr, torquepole_handle)
dof_props['driveMode'][:] = gymapi.DOF_MODE_EFFORT
dof_props['stiffness'][:] = 0.0
dof_props['damping'][:] = 0.0
dof_props['velocity'].fill(100.0)
dof_props['effort'].fill(0.0)
dof_props['friction'].fill(0.00)
self.gym.set_actor_dof_properties(env_ptr, torquepole_handle, dof_props)
self.envs.append(env_ptr)
self.torquepole_handles.append(torquepole_handle)
def compute_reward(self):
# retrieve environment observations from buffer
pole_angle = self.pole_angle
pole_vel = self.obs_buf[:, 2]
self.rew_buf[:], self.reset_buf[:] = compute_torquepole_reward(
pole_angle, pole_vel,
self.reset_dist, self.reset_buf, self.progress_buf, self.max_episode_length
)
# print(pole_vel[0])
# rew = reward = 1.0 - pole_angle * pole_angle - 0.005 * torch.abs(pole_vel)
# rew = torch.where(torch.abs(pole_angle) > np.pi / 2, torch.ones_like(rew) * -2.0, rew)
# print(self.rew_buf[0])
# print(self.obs_buf[0])
def convert_angle(self, angle):
# Apply sine and cosine functions
sin_component = torch.sin(angle)
cos_component = torch.cos(angle)
# Normalize angle to [-pi, pi]
normalized_angle = torch.remainder(angle + np.pi, 2 * np.pi) - np.pi
# Apply offset
# normalized_angle += np.pi
# Normalize again if needed
# normalized_angle = torch.remainder(normalized_angle + np.pi, 2 * np.pi) - np.pi
# Normalize angle to [-1, 1]
normalized_angle /= torch.pi
return sin_component, cos_component, normalized_angle
def compute_observations(self, env_ids=None):
if env_ids is None:
env_ids = np.arange(self.num_envs)
self.gym.refresh_dof_state_tensor(self.sim)
self.obs_buf[env_ids, 0], self.obs_buf[env_ids, 1], self.pole_angle = self.convert_angle(self.dof_pos[env_ids, 0].squeeze())
self.obs_buf[env_ids, 2] = self.dof_vel[env_ids, 0].squeeze()/20.0
# print(self.obs_buf[0,...])
return self.obs_buf
def reset_idx(self, env_ids):
# Randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
positions = 2*np.pi * (torch.rand((len(env_ids), self.num_dof), device=self.device) - 0.5)
velocities = 10.0 * (torch.rand((len(env_ids), self.num_dof), device=self.device) - 0.5)
self.dof_pos[env_ids, :] = positions[:]
self.dof_vel[env_ids, :] = velocities[:]
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def pre_physics_step(self, actions):
actions_tensor = torch.zeros(self.num_envs * self.num_dof, device=self.device, dtype=torch.float)
actions_tensor[::self.num_dof] = actions.to(self.device).squeeze() * self.max_push_effort
a = self.keys.get_keys()
if(a[0] != 0):
actions_tensor[0] = 0
forces = gymtorch.unwrap_tensor(actions_tensor)
self.gym.set_dof_actuation_force_tensor(self.sim, forces)
# print(actions_tensor[0])
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
self.compute_reward()
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_torquepole_reward(pole_angle, pole_vel,
reset_dist, reset_buf, progress_buf, max_episode_length):
# type: (Tensor, Tensor, float, Tensor, Tensor, float) -> Tuple[Tensor, Tensor]
# reward is combo of angle deviated from upright, velocity of cart, and velocity of pole moving
reward = (1.0 - (pole_angle * pole_angle)) - (pole_vel * pole_vel)*0.1
# reward = 1.0 - pole_angle * pole_angle
# adjust reward for reset agents
# reward = torch.where(torch.abs(pole_angle) > 0.5, torch.ones_like(reward) * -2.0, reward)
# reset = torch.where(torch.abs(pole_angle) > np.pi*1.9, torch.ones_like(reset_buf), reset_buf)
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset_buf)
return reward, reset
| 10,972 | Python | 43.605691 | 217 | 0.62304 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/stm32_comms.py | # -*- coding: utf-8 -*-
"""
Created on Thu Jan 4 12:52:12 2024
@author: tylerbarkin
"""
import serial
import struct
import time
import numpy as np
import platform
class MCU_Comms():
def __init__(self, enabled=1):
self.enabled = enabled
if(self.enabled):
self.act_data = np.zeros(12)
self.obs_data = np.zeros(48)
if platform.system() == 'Windows':
self.port = 'COM6'
else:
self.port = '/dev/ttyACM1'
print('Using Port : {}'.format(self.port))
self.open_port()
def open_port(self):
# Configure the serial connection
if(self.enabled):
try:
self.ser = serial.Serial(
port=self.port, # Serial port
baudrate=460800, # Baud rate, should match STM32 setting
parity=serial.PARITY_NONE,
stopbits=serial.STOPBITS_ONE,
bytesize=serial.EIGHTBITS,
timeout=1 # Read timeout in seconds
)
except:
print("No Comms Object Found")
def close_port(self):
if(self.enabled):
if self.ser.is_open:
self.ser.close()
print("Serial port closed")
def read_data(self):
if(self.enabled):
try:
# Read 16 bytes from the serial port (size of 4 floats)
data = self.ser.read(12 * 4)
# Check if we received 48 bytes
if len(data) == 48:
# Unpack the bytes to four floats
float_values = struct.unpack('12f', data)
self.act_data = np.array(float_values)
# print(f"Received floats: {float_values}")
else:
print("Incomplete data received")
except KeyboardInterrupt:
print("Exiting...")
else:
self.act_data = np.zeros(12)
def write_data(self):
if(self.enabled):
# Pack the floats into bytes
data_to_send = struct.pack('48f', *self.obs_data)
try:
# Send the packed bytes over the serial connection
self.ser.write(data_to_send)
# print("Data sent")
except Exception as e:
print(f"Error: {e}")
def __del__(self):
# Destructor: close the serial port
self.close_port()
# comm_obj = MCU_Comms()
# comm_obj.obs_data = np.zeros((48))
# for i in range(48):
# comm_obj.obs_data[i] = i
# for _ in range(1):
# start_time = time.perf_counter()
# comm_obj.write_data()
# comm_obj.read_data()
# elapsed_time = time.perf_counter() - start_time
# print('Total Time = {}'.format(elapsed_time))
# comm_obj.close_port()
| 3,192 | Python | 28.027272 | 108 | 0.45614 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/keyboard.py | import pygame
import numpy as np
import torch
class Keyboard():
def __init__(self, num_actions=1):
pygame.quit()
# Initialize Pygame
pygame.init()
# Set up the screen
screen = pygame.display.set_mode((400, 300))
self.num_actions = num_actions
def refresh(self):
pygame.event.pump()
def get_keys(self):
pygame.event.pump()
# Check for arrow key presses
a = torch.zeros(self.num_actions)
keys = pygame.key.get_pressed()
if keys[pygame.K_UP]:
a[0] = 1.0
if keys[pygame.K_DOWN]:
a[0] = -1.0
if keys[pygame.K_LEFT]:
a[1] = 1.0
if keys[pygame.K_RIGHT]:
a[1] = -1.0
if keys[pygame.K_a]:
a[2] = 1.0
if keys[pygame.K_d]:
a[2] = -1.0
return a
| 884 | Python | 21.692307 | 52 | 0.49095 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/anymal.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import os
import torch
from isaacgym import gymtorch
from isaacgym import gymapi
from isaacgymenvs.utils.torch_jit_utils import to_torch, get_axis_params, torch_rand_float, quat_rotate, quat_rotate_inverse
from isaacgymenvs.tasks.base.vec_task import VecTask
from typing import Tuple, Dict
from isaacgymenvs.tasks.stm32_comms import MCU_Comms
class Anymal(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.comm_obj = MCU_Comms(enabled=False)
# normalization
self.lin_vel_scale = self.cfg["env"]["learn"]["linearVelocityScale"]
self.ang_vel_scale = self.cfg["env"]["learn"]["angularVelocityScale"]
self.dof_pos_scale = self.cfg["env"]["learn"]["dofPositionScale"]
self.dof_vel_scale = self.cfg["env"]["learn"]["dofVelocityScale"]
self.action_scale = self.cfg["env"]["control"]["actionScale"]
# reward scales
self.rew_scales = {}
self.rew_scales["lin_vel_xy"] = self.cfg["env"]["learn"]["linearVelocityXYRewardScale"]
self.rew_scales["ang_vel_z"] = self.cfg["env"]["learn"]["angularVelocityZRewardScale"]
self.rew_scales["torque"] = self.cfg["env"]["learn"]["torqueRewardScale"]
# randomization
self.randomization_params = self.cfg["task"]["randomization_params"]
self.randomize = self.cfg["task"]["randomize"]
# command ranges
self.command_x_range = self.cfg["env"]["randomCommandVelocityRanges"]["linear_x"]
self.command_y_range = self.cfg["env"]["randomCommandVelocityRanges"]["linear_y"]
self.command_yaw_range = self.cfg["env"]["randomCommandVelocityRanges"]["yaw"]
# plane params
self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"]
self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"]
self.plane_restitution = self.cfg["env"]["plane"]["restitution"]
# base init state
pos = self.cfg["env"]["baseInitState"]["pos"]
rot = self.cfg["env"]["baseInitState"]["rot"]
v_lin = self.cfg["env"]["baseInitState"]["vLinear"]
v_ang = self.cfg["env"]["baseInitState"]["vAngular"]
state = pos + rot + v_lin + v_ang
self.base_init_state = state
# default joint positions
self.named_default_joint_angles = self.cfg["env"]["defaultJointAngles"]
self.cfg["env"]["numObservations"] = 48
self.cfg["env"]["numActions"] = 12
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)
# other
self.dt = self.sim_params.dt
self.max_episode_length_s = self.cfg["env"]["learn"]["episodeLength_s"]
self.max_episode_length = int(self.max_episode_length_s / self.dt + 0.5)
self.Kp = self.cfg["env"]["control"]["stiffness"]
self.Kd = self.cfg["env"]["control"]["damping"]
for key in self.rew_scales.keys():
self.rew_scales[key] *= self.dt
if self.viewer != None:
p = self.cfg["env"]["viewer"]["pos"]
lookat = self.cfg["env"]["viewer"]["lookat"]
cam_pos = gymapi.Vec3(p[0], p[1], p[2])
cam_target = gymapi.Vec3(lookat[0], lookat[1], lookat[2])
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym state tensors
actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
net_contact_forces = self.gym.acquire_net_contact_force_tensor(self.sim)
torques = self.gym.acquire_dof_force_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
# create some wrapper tensors for different slices
self.root_states = gymtorch.wrap_tensor(actor_root_state)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
self.contact_forces = gymtorch.wrap_tensor(net_contact_forces).view(self.num_envs, -1, 3) # shape: num_envs, num_bodies, xyz axis
self.torques = gymtorch.wrap_tensor(torques).view(self.num_envs, self.num_dof)
self.commands = torch.zeros(self.num_envs, 3, dtype=torch.float, device=self.device, requires_grad=False)
self.commands_y = self.commands.view(self.num_envs, 3)[..., 1]
self.commands_x = self.commands.view(self.num_envs, 3)[..., 0]
self.commands_yaw = self.commands.view(self.num_envs, 3)[..., 2]
self.default_dof_pos = torch.zeros_like(self.dof_pos, dtype=torch.float, device=self.device, requires_grad=False)
for i in range(self.cfg["env"]["numActions"]):
name = self.dof_names[i]
angle = self.named_default_joint_angles[name]
self.default_dof_pos[:, i] = angle
# initialize some data used later on
self.extras = {}
self.initial_root_states = self.root_states.clone()
self.initial_root_states[:] = to_torch(self.base_init_state, device=self.device, requires_grad=False)
self.gravity_vec = to_torch(get_axis_params(-1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
self.actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.reset_idx(torch.arange(self.num_envs, device=self.device))
def create_sim(self):
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
# If randomizing, apply once immediately on startup before the fist sim step
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
plane_params.static_friction = self.plane_static_friction
plane_params.dynamic_friction = self.plane_dynamic_friction
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../assets')
asset_file = "urdf/anymal_c/urdf/anymal.urdf"
asset_options = gymapi.AssetOptions()
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
asset_options.collapse_fixed_joints = True
asset_options.replace_cylinder_with_capsule = True
asset_options.flip_visual_attachments = True
asset_options.fix_base_link = self.cfg["env"]["urdfAsset"]["fixBaseLink"]
asset_options.density = 0.001
asset_options.angular_damping = 0.0
asset_options.linear_damping = 0.0
asset_options.armature = 0.0
asset_options.thickness = 0.01
asset_options.disable_gravity = False
anymal_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
self.num_dof = self.gym.get_asset_dof_count(anymal_asset)
self.num_bodies = self.gym.get_asset_rigid_body_count(anymal_asset)
start_pose = gymapi.Transform()
start_pose.p = gymapi.Vec3(*self.base_init_state[:3])
body_names = self.gym.get_asset_rigid_body_names(anymal_asset)
self.dof_names = self.gym.get_asset_dof_names(anymal_asset)
extremity_name = "SHANK" if asset_options.collapse_fixed_joints else "FOOT"
feet_names = [s for s in body_names if extremity_name in s]
self.feet_indices = torch.zeros(len(feet_names), dtype=torch.long, device=self.device, requires_grad=False)
knee_names = [s for s in body_names if "THIGH" in s]
self.knee_indices = torch.zeros(len(knee_names), dtype=torch.long, device=self.device, requires_grad=False)
self.base_index = 0
dof_props = self.gym.get_asset_dof_properties(anymal_asset)
for i in range(self.num_dof):
dof_props['driveMode'][i] = gymapi.DOF_MODE_POS
dof_props['stiffness'][i] = self.cfg["env"]["control"]["stiffness"] #self.Kp
dof_props['damping'][i] = self.cfg["env"]["control"]["damping"] #self.Kd
env_lower = gymapi.Vec3(-spacing, -spacing, 0.0)
env_upper = gymapi.Vec3(spacing, spacing, spacing)
self.anymal_handles = []
self.envs = []
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(self.sim, env_lower, env_upper, num_per_row)
anymal_handle = self.gym.create_actor(env_ptr, anymal_asset, start_pose, "anymal", i, 1, 0)
self.gym.set_actor_dof_properties(env_ptr, anymal_handle, dof_props)
self.gym.enable_actor_dof_force_sensors(env_ptr, anymal_handle)
self.envs.append(env_ptr)
self.anymal_handles.append(anymal_handle)
for i in range(len(feet_names)):
self.feet_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.anymal_handles[0], feet_names[i])
for i in range(len(knee_names)):
self.knee_indices[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.anymal_handles[0], knee_names[i])
self.base_index = self.gym.find_actor_rigid_body_handle(self.envs[0], self.anymal_handles[0], "base")
def pre_physics_step(self, actions):
self.actions = actions.clone().to(self.device)
targets = self.action_scale * self.actions + self.default_dof_pos
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(targets))
self.comm_obj.obs_data = self.obs_buf[0].detach().cpu().numpy()
self.comm_obj.write_data()
self.comm_obj.read_data()
self.actions[0,:] = torch.tensor(self.comm_obj.act_data)
def post_physics_step(self):
self.progress_buf += 1
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.compute_observations()
self.compute_reward(self.actions)
def compute_reward(self, actions):
self.rew_buf[:], self.reset_buf[:] = compute_anymal_reward(
# tensors
self.root_states,
self.commands,
self.torques,
self.contact_forces,
self.knee_indices,
self.progress_buf,
# Dict
self.rew_scales,
# other
self.base_index,
self.max_episode_length,
)
def compute_observations(self):
self.gym.refresh_dof_state_tensor(self.sim) # done in step
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.obs_buf[:] = compute_anymal_observations( # tensors
self.root_states,
self.commands,
self.dof_pos,
self.default_dof_pos,
self.dof_vel,
self.gravity_vec,
self.actions,
# scales
self.lin_vel_scale,
self.ang_vel_scale,
self.dof_pos_scale,
self.dof_vel_scale
)
def reset_idx(self, env_ids):
# Randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
positions_offset = torch_rand_float(0.5, 1.5, (len(env_ids), self.num_dof), device=self.device)
velocities = torch_rand_float(-0.1, 0.1, (len(env_ids), self.num_dof), device=self.device)
self.dof_pos[env_ids] = self.default_dof_pos[env_ids] * positions_offset
self.dof_vel[env_ids] = velocities
env_ids_int32 = env_ids.to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.initial_root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
self.commands_x[env_ids] = torch_rand_float(self.command_x_range[0], self.command_x_range[1], (len(env_ids), 1), device=self.device).squeeze()
self.commands_y[env_ids] = torch_rand_float(self.command_y_range[0], self.command_y_range[1], (len(env_ids), 1), device=self.device).squeeze()
self.commands_yaw[env_ids] = torch_rand_float(self.command_yaw_range[0], self.command_yaw_range[1], (len(env_ids), 1), device=self.device).squeeze()
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 1
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_anymal_reward(
# tensors
root_states,
commands,
torques,
contact_forces,
knee_indices,
episode_lengths,
# Dict
rew_scales,
# other
base_index,
max_episode_length
):
# (reward, reset, feet_in air, feet_air_time, episode sums)
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Dict[str, float], int, int) -> Tuple[Tensor, Tensor]
# prepare quantities (TODO: return from obs ?)
base_quat = root_states[:, 3:7]
base_lin_vel = quat_rotate_inverse(base_quat, root_states[:, 7:10])
base_ang_vel = quat_rotate_inverse(base_quat, root_states[:, 10:13])
# velocity tracking reward
lin_vel_error = torch.sum(torch.square(commands[:, :2] - base_lin_vel[:, :2]), dim=1)
ang_vel_error = torch.square(commands[:, 2] - base_ang_vel[:, 2])
rew_lin_vel_xy = torch.exp(-lin_vel_error/0.25) * rew_scales["lin_vel_xy"]
rew_ang_vel_z = torch.exp(-ang_vel_error/0.25) * rew_scales["ang_vel_z"]
# torque penalty
rew_torque = torch.sum(torch.square(torques), dim=1) * rew_scales["torque"]
total_reward = rew_lin_vel_xy + rew_ang_vel_z + rew_torque
total_reward = torch.clip(total_reward, 0., None)
# reset agents
reset = torch.norm(contact_forces[:, base_index, :], dim=1) > 1.
reset = reset | torch.any(torch.norm(contact_forces[:, knee_indices, :], dim=2) > 1., dim=1)
time_out = episode_lengths >= max_episode_length - 1 # no terminal reward for time-outs
reset = reset | time_out
return total_reward.detach(), reset
@torch.jit.script
def compute_anymal_observations(root_states,
commands,
dof_pos,
default_dof_pos,
dof_vel,
gravity_vec,
actions,
lin_vel_scale,
ang_vel_scale,
dof_pos_scale,
dof_vel_scale
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, float, float, float, float) -> Tensor
base_quat = root_states[:, 3:7]
base_lin_vel = quat_rotate_inverse(base_quat, root_states[:, 7:10]) * lin_vel_scale
base_ang_vel = quat_rotate_inverse(base_quat, root_states[:, 10:13]) * ang_vel_scale
projected_gravity = quat_rotate(base_quat, gravity_vec)
dof_pos_scaled = (dof_pos - default_dof_pos) * dof_pos_scale
commands_scaled = commands*torch.tensor([lin_vel_scale, lin_vel_scale, ang_vel_scale], requires_grad=False, device=commands.device)
obs = torch.cat((base_lin_vel,
base_ang_vel,
projected_gravity,
commands_scaled,
dof_pos_scaled,
dof_vel*dof_vel_scale,
actions
), dim=-1)
return obs
| 18,854 | Python | 46.977099 | 217 | 0.60279 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/dextreme/allegro_hand_dextreme.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import os
from typing import Tuple, List
import itertools
from itertools import permutations
from tkinter import W
from typing import Tuple, Dict, List, Set
import numpy as np
import torch
from isaacgym import gymapi
from isaacgym import gymtorch
from isaacgymenvs.utils.torch_jit_utils import scale, unscale, quat_mul, quat_conjugate, quat_from_angle_axis, \
to_torch, get_axis_params, torch_rand_float, tensor_clamp
from torch import Tensor
from isaacgymenvs.tasks.dextreme.adr_vec_task import ADRVecTask
from isaacgymenvs.utils.torch_jit_utils import quaternion_to_matrix, matrix_to_quaternion
from isaacgymenvs.utils.rna_util import RandomNetworkAdversary
class AllegroHandDextreme(ADRVecTask):
dict_obs_cls = True
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
'''
obligatory constructor to fill-in class variables and setting
up the simulation.
self._read_cfg() is about initialising class variables from a
config file.
self._init_pre_sim_buffers() initialises particular tensors
that are useful in storing various states
randomised or otherwise
self._init_post_sim_buffers() initialises the root tensors and
other auxiliary variables that can be provided
as input to the controller or the value function
'''
self.cfg = cfg
# Read the task config file and store all the relevant variables in the class
self._read_cfg()
self.fingertips = [s+"_link_3" for s in ["index", "middle", "ring", "thumb"]]
self.num_fingertips = len(self.fingertips)
num_dofs = 16
self.num_obs_dict = self.get_num_obs_dict(num_dofs)
self.cfg["env"]["obsDims"] = {}
for o in self.num_obs_dict.keys():
if o not in self.num_obs_dict:
raise Exception(f"Unknown type of observation {o}!")
self.cfg["env"]["obsDims"][o] = (self.num_obs_dict[o],)
self.up_axis = 'z'
self.use_vel_obs = False
self.fingertip_obs = True
self.asymmetric_obs = self.cfg["env"]["asymmetric_observations"]
self.cfg["env"]["numActions"] = 16
self.sim_device = sim_device
rl_device = self.cfg.get("rl_device", "cuda:0")
self._init_pre_sim_buffers()
super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, use_dict_obs=True)
self._init_post_sim_buffers()
reward_keys = ['dist_rew', 'rot_rew', 'action_penalty', 'action_delta_penalty',
'velocity_penalty', 'reach_goal_rew', 'fall_rew', 'timeout_rew']
self.rewards_episode = {key: torch.zeros(self.num_envs, dtype=torch.float, device=self.device) for key in reward_keys}
if self.use_adr:
self.apply_reset_buf = torch.zeros(self.num_envs, dtype=torch.long, device=self.device)
if self.print_success_stat:
self.last_success_step = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.success_time = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.last_ep_successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.total_num_resets = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.successes_count = torch.zeros(self.max_consecutive_successes + 1, dtype=torch.float, device=self.device)
from tensorboardX import SummaryWriter
self.eval_summary_dir = './eval_summaries'
# remove the old directory if it exists
if os.path.exists(self.eval_summary_dir):
import shutil
shutil.rmtree(self.eval_summary_dir)
self.eval_summaries = SummaryWriter(self.eval_summary_dir, flush_secs=3)
def get_env_state(self):
env_dict=dict(act_moving_average=self.act_moving_average)
if self.use_adr:
env_dict = dict(**env_dict, **super().get_env_state())
return env_dict
def get_save_tensors(self):
if hasattr(self, 'actions'):
actions = self.actions
else:
actions = torch.zeros((self.num_envs, self.cfg["env"]["numActions"])).to(self.device)
# scale is [-1, 1] -> [low, upper]
# unscale is [low, upper] -> [-1, 1]
# self.actions are in [-1, 1] as they are raw
# actions returned by the policy
return {
# 'observations': self.obs_buf,
'actions': actions,
'cube_state': self.root_state_tensor[self.object_indices],
'goal_state': self.goal_states,
'joint_positions': self.dof_pos,
'joint_velocities': self.dof_vel,
'root_state': self.root_state_tensor[self.hand_indices],
}
def save_step(self):
self.capture.append_experience(self.get_save_tensors())
def get_num_obs_dict(self, num_dofs):
# This is what we use for ADR
num_obs = {
"dof_pos": num_dofs,
"dof_pos_randomized": num_dofs,
"dof_vel": num_dofs,
"dof_force": num_dofs, # generalised forces
"object_vels": 6,
"last_actions": num_dofs,
"cube_random_params": 3,
"hand_random_params": 1,
"gravity_vec": 3,
"ft_states": 13 * self.num_fingertips, # (pos, quat, linvel, angvel) per fingertip
"ft_force_torques": 6 * self.num_fingertips, # wrenches
"rb_forces": 3, # random forces being applied to the cube
"rot_dist": 2,
"stochastic_delay_params": 4, # cube obs + action delay prob, action fixed latency, pose refresh rate
"affine_params": 16*2 + 7*2 + 16*2,
"object_pose": 7,
"goal_pose": 7,
"goal_relative_rot": 4,
"object_pose_cam_randomized": 7,
"goal_relative_rot_cam_randomized": 4,
}
return num_obs
def create_sim(self):
self.dt = self.sim_params.dt
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../../assets')
hand_asset_file = "urdf/kuka_allegro_description/allegro.urdf"
if "asset" in self.cfg["env"]:
asset_root = self.cfg["env"]["asset"].get("assetRoot", asset_root)
hand_asset_file = self.cfg["env"]["asset"].get("assetFileName", hand_asset_file)
object_asset_file = self.asset_files_dict[self.object_type]
# load allegro hand_ asset
asset_options = gymapi.AssetOptions()
asset_options.flip_visual_attachments = False
asset_options.fix_base_link = True
asset_options.collapse_fixed_joints = False
asset_options.disable_gravity = False
asset_options.thickness = 0.001
asset_options.angular_damping = 0.01
if self.physics_engine == gymapi.SIM_PHYSX:
asset_options.use_physx_armature = True
# The control interface i.e. we will be sending target positions to the robot
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS
hand_asset = self.gym.load_asset(self.sim, asset_root, hand_asset_file, asset_options)
self.num_hand_bodies = self.gym.get_asset_rigid_body_count(hand_asset)
self.num_hand_shapes = self.gym.get_asset_rigid_shape_count(hand_asset)
self.num_hand_dofs = self.gym.get_asset_dof_count(hand_asset)
print("Num dofs: ", self.num_hand_dofs)
self.num_hand_actuators = self.num_hand_dofs
self.actuated_dof_indices = [i for i in range(self.num_hand_dofs)]
# set allegro_hand dof properties
hand_dof_props = self.gym.get_asset_dof_properties(hand_asset)
self.hand_dof_lower_limits = []
self.hand_dof_upper_limits = []
self.hand_dof_default_pos = []
self.hand_dof_default_vel = []
self.sensors = []
sensor_pose = gymapi.Transform()
self.fingertip_handles = [self.gym.find_asset_rigid_body_index(hand_asset, name) for name in self.fingertips]
# create fingertip force sensors
sensor_pose = gymapi.Transform()
for ft_handle in self.fingertip_handles:
self.gym.create_asset_force_sensor(hand_asset, ft_handle, sensor_pose)
for i in range(self.num_hand_dofs):
self.hand_dof_lower_limits.append(hand_dof_props['lower'][i])
self.hand_dof_upper_limits.append(hand_dof_props['upper'][i])
self.hand_dof_default_pos.append(0.0)
self.hand_dof_default_vel.append(0.0)
hand_dof_props['effort'][i] = self.max_effort
hand_dof_props['stiffness'][i] = 2
hand_dof_props['damping'][i] = 0.1
hand_dof_props['friction'][i] = 0.01
hand_dof_props['armature'][i] = 0.002
self.actuated_dof_indices = to_torch(self.actuated_dof_indices, dtype=torch.long, device=self.device)
self.hand_dof_lower_limits = to_torch(self.hand_dof_lower_limits, device=self.device)
self.hand_dof_upper_limits = to_torch(self.hand_dof_upper_limits, device=self.device)
self.hand_dof_default_pos = to_torch(self.hand_dof_default_pos, device=self.device)
self.hand_dof_default_vel = to_torch(self.hand_dof_default_vel, device=self.device)
# load manipulated object and goal assets
object_asset_options = gymapi.AssetOptions()
object_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options)
object_asset_options.disable_gravity = True
goal_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options)
hand_start_pose = gymapi.Transform()
hand_start_pose.p = gymapi.Vec3(*get_axis_params(0.5, self.up_axis_idx))
hand_start_pose.r = gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 1, 0), np.pi) * \
gymapi.Quat.from_axis_angle(gymapi.Vec3(1, 0, 0), 0.47 * np.pi) * \
gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 0, 1), 0.25 * np.pi)
object_start_pose = gymapi.Transform()
object_start_pose.p = gymapi.Vec3()
object_start_pose.p.x = hand_start_pose.p.x
pose_dy, pose_dz = self.start_object_pose_dy, self.start_object_pose_dz
object_start_pose.p.y = hand_start_pose.p.y + pose_dy
object_start_pose.p.z = hand_start_pose.p.z + pose_dz
self.goal_displacement = gymapi.Vec3(-0.2, -0.06, 0.12)
self.goal_displacement_tensor = to_torch(
[self.goal_displacement.x, self.goal_displacement.y, self.goal_displacement.z], device=self.device)
goal_start_pose = gymapi.Transform()
goal_start_pose.p = object_start_pose.p + self.goal_displacement
goal_start_pose.p.y -= 0.02
goal_start_pose.p.z -= 0.04
# compute aggregate size
max_agg_bodies = self.num_hand_bodies + 2
max_agg_shapes = self.num_hand_shapes + 2
self.allegro_hands = []
self.object_handles = []
self.envs = []
self.object_init_state = []
self.hand_start_states = []
self.hand_indices = []
self.fingertip_indices = []
self.object_indices = []
self.goal_object_indices = []
self.fingertip_handles = [self.gym.find_asset_rigid_body_index(hand_asset, name) for name in self.fingertips]
hand_rb_count = self.gym.get_asset_rigid_body_count(hand_asset)
object_rb_count = self.gym.get_asset_rigid_body_count(object_asset)
self.object_rb_handles = list(range(hand_rb_count, hand_rb_count + object_rb_count))
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
if self.aggregate_mode >= 1:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
# add hand - collision filter = -1 to use asset collision filters set in mjcf loader
hand_actor = self.gym.create_actor(env_ptr, hand_asset, hand_start_pose, "hand", i, -1, 0)
self.hand_start_states.append([hand_start_pose.p.x, hand_start_pose.p.y, hand_start_pose.p.z,
hand_start_pose.r.x, hand_start_pose.r.y, hand_start_pose.r.z, hand_start_pose.r.w,
0, 0, 0, 0, 0, 0])
self.gym.set_actor_dof_properties(env_ptr, hand_actor, hand_dof_props)
hand_idx = self.gym.get_actor_index(env_ptr, hand_actor, gymapi.DOMAIN_SIM)
self.hand_indices.append(hand_idx)
self.gym.enable_actor_dof_force_sensors(env_ptr, hand_actor)
# add object
object_handle = self.gym.create_actor(env_ptr, object_asset, object_start_pose, "object", i, 0, 0)
self.object_init_state.append([object_start_pose.p.x, object_start_pose.p.y, object_start_pose.p.z,
object_start_pose.r.x, object_start_pose.r.y, object_start_pose.r.z, object_start_pose.r.w,
0, 0, 0, 0, 0, 0])
object_idx = self.gym.get_actor_index(env_ptr, object_handle, gymapi.DOMAIN_SIM)
self.object_indices.append(object_idx)
# add goal object
goal_handle = self.gym.create_actor(env_ptr, goal_asset, goal_start_pose, "goal_object", i + self.num_envs, 0, 0)
goal_object_idx = self.gym.get_actor_index(env_ptr, goal_handle, gymapi.DOMAIN_SIM)
self.goal_object_indices.append(goal_object_idx)
if self.object_type != "block":
self.gym.set_rigid_body_color(
env_ptr, object_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
self.gym.set_rigid_body_color(
env_ptr, goal_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
if self.aggregate_mode > 0:
self.gym.end_aggregate(env_ptr)
self.envs.append(env_ptr)
self.allegro_hands.append(hand_actor)
self.object_handles.append(object_handle)
self.palm_link_handle = self.gym.find_actor_rigid_body_handle(env_ptr, hand_actor, "palm_link"),
object_rb_props = self.gym.get_actor_rigid_body_properties(env_ptr, object_handle)
self.object_rb_masses = [prop.mass for prop in object_rb_props]
self.object_init_state = to_torch(self.object_init_state, device=self.device, dtype=torch.float).view(self.num_envs, 13)
self.goal_states = self.object_init_state.clone()
self.goal_states[:, self.up_axis_idx] -= 0.04
self.goal_init_state = self.goal_states.clone()
self.hand_start_states = to_torch(self.hand_start_states, device=self.device).view(self.num_envs, 13)
self.goal_pose = self.goal_states[:, 0:7]
self.goal_pos = self.goal_states[:, 0:3]
self.goal_rot = self.goal_states[:, 3:7]
self.object_rb_handles = to_torch(self.object_rb_handles, dtype=torch.long, device=self.device)
self.object_rb_masses = to_torch(self.object_rb_masses, dtype=torch.float, device=self.device)
self.hand_indices = to_torch(self.hand_indices, dtype=torch.long, device=self.device)
self.object_indices = to_torch(self.object_indices, dtype=torch.long, device=self.device)
self.goal_object_indices = to_torch(self.goal_object_indices, dtype=torch.long, device=self.device)
# Random Network Adversary
# As mentioned in OpenAI et al. 2019 (Appendix B.3) https://arxiv.org/abs/1910.07113
# and DeXtreme, 2022 (Section 2.6.2) https://arxiv.org/abs/2210.13702
if self.enable_rna:
softmax_bins = 32
num_dofs = len(self.hand_dof_lower_limits)
self.discretised_dofs = torch.zeros((num_dofs, softmax_bins)).to(self.device)
# Discretising the joing angles into 32 bins
for i in range(0, len(self.hand_dof_lower_limits)):
self.discretised_dofs[i] = torch.linspace(self.hand_dof_lower_limits[i],
self.hand_dof_upper_limits[i], steps=softmax_bins).to(self.device)
# input is the joint angles and cube pose (pos: 3 + quat: 4), therefore a total of 16+7 dimensions
self.rna_network = RandomNetworkAdversary(num_envs=self.num_envs, in_dims=num_dofs+7, \
out_dims=num_dofs, softmax_bins=softmax_bins, device=self.device)
# Random cube observations. Need this tensor for Random Cube Pose Injection
self.random_cube_poses = torch.zeros(self.num_envs, 7, device=self.device)
def compute_reward(self, actions):
self.rew_buf[:], self.reset_buf[:], self.reset_goal_buf[:], self.progress_buf[:], \
self.hold_count_buf[:], self.successes[:], self.consecutive_successes[:], \
dist_rew, rot_rew, action_penalty, action_delta_penalty, velocity_penalty, reach_goal_rew, fall_rew, timeout_rew = compute_hand_reward(
self.rew_buf, self.reset_buf, self.reset_goal_buf, self.progress_buf, self.hold_count_buf, self.cur_targets, self.prev_targets,
self.dof_vel, self.successes, self.consecutive_successes, self.max_episode_length,
self.object_pos, self.object_rot, self.goal_pos, self.goal_rot, self.dist_reward_scale, self.rot_reward_scale, self.rot_eps,
self.actions, self.action_penalty_scale, self.action_delta_penalty_scale,
self.success_tolerance, self.reach_goal_bonus, self.fall_dist, self.fall_penalty,
self.max_consecutive_successes, self.av_factor, self.num_success_hold_steps
)
# update best rotation distance in the current episode
self.best_rotation_dist = torch.minimum(self.best_rotation_dist, self.curr_rotation_dist)
self.extras['consecutive_successes'] = self.consecutive_successes.mean()
self.extras['true_objective'] = self.successes
episode_cumulative = dict()
episode_cumulative['dist_rew'] = dist_rew
episode_cumulative['rot_rew'] = rot_rew
episode_cumulative['action_penalty'] = action_penalty
episode_cumulative['action_delta_penalty'] = action_delta_penalty
episode_cumulative['velocity_penalty'] = velocity_penalty
episode_cumulative['reach_goal_rew'] = reach_goal_rew
episode_cumulative['fall_rew'] = fall_rew
episode_cumulative['timeout_rew'] = timeout_rew
self.extras['episode_cumulative'] = episode_cumulative
if self.print_success_stat:
is_success = self.reset_goal_buf.to(torch.bool)
frame_ = torch.empty_like(self.last_success_step).fill_(self.frame)
self.success_time = torch.where(is_success, frame_ - self.last_success_step, self.success_time)
self.last_success_step = torch.where(is_success, frame_, self.last_success_step)
mask_ = self.success_time > 0
if any(mask_):
avg_time_mean = ((self.success_time * mask_).sum(dim=0) / mask_.sum(dim=0)).item()
else:
avg_time_mean = math.nan
envs_reset = self.reset_buf
if self.use_adr:
envs_reset = self.reset_buf & ~self.apply_reset_buf
self.total_resets = self.total_resets + envs_reset.sum()
direct_average_successes = self.total_successes + self.successes.sum()
self.total_successes = self.total_successes + (self.successes * envs_reset).sum()
self.total_num_resets += envs_reset
self.last_ep_successes = torch.where(envs_reset > 0, self.successes, self.last_ep_successes)
reset_ids = envs_reset.nonzero().squeeze()
last_successes = self.successes[reset_ids].long()
self.successes_count[last_successes] += 1
if self.frame % 100 == 0:
# The direct average shows the overall result more quickly, but slightly undershoots long term
# policy performance.
print("Direct average consecutive successes = {:.1f}".format(direct_average_successes/(self.total_resets + self.num_envs)))
if self.total_resets > 0:
print("Post-Reset average consecutive successes = {:.1f}".format(self.total_successes/self.total_resets))
print(f"Max num successes: {self.successes.max().item()}")
print(f"Average consecutive successes: {self.consecutive_successes.mean().item():.2f}")
print(f"Total num resets: {self.total_num_resets.sum().item()} --> {self.total_num_resets}")
print(f"Reset percentage: {(self.total_num_resets > 0).sum() / self.num_envs:.2%}")
print(f"Last ep successes: {self.last_ep_successes.mean().item():.2f} {self.last_ep_successes}")
self.eval_summaries.add_scalar("consecutive_successes", self.consecutive_successes.mean().item(), self.frame)
self.eval_summaries.add_scalar("last_ep_successes", self.last_ep_successes.mean().item(), self.frame)
self.eval_summaries.add_scalar("reset_stats/reset_percentage", (self.total_num_resets > 0).sum() / self.num_envs, self.frame)
self.eval_summaries.add_scalar("reset_stats/min_num_resets", self.total_num_resets.min().item(), self.frame)
self.eval_summaries.add_scalar("policy_speed/avg_success_time_frames", avg_time_mean, self.frame)
frame_time = self.control_freq_inv * self.dt
self.eval_summaries.add_scalar("policy_speed/avg_success_time_seconds", avg_time_mean * frame_time, self.frame)
self.eval_summaries.add_scalar("policy_speed/avg_success_per_minute", 60.0 / (avg_time_mean * frame_time), self.frame)
print(f"Policy speed (successes per minute): {60.0 / (avg_time_mean * frame_time):.2f}")
dof_delta = self.dof_delta.abs()
print(f"Max dof deltas: {dof_delta.max(dim=0).values}, max across dofs: {self.dof_delta.abs().max().item():.2f}, mean: {self.dof_delta.abs().mean().item():.2f}")
print(f"Max dof delta radians per sec: {dof_delta.max().item() / frame_time:.2f}, mean: {dof_delta.mean().item() / frame_time:.2f}")
# create a matplotlib bar chart of the self.successes_count
import matplotlib.pyplot as plt
plt.bar(list(range(self.max_consecutive_successes + 1)), self.successes_count.cpu().numpy())
plt.title("Successes histogram")
plt.xlabel("Successes")
plt.ylabel("Frequency")
plt.savefig(f"{self.eval_summary_dir}/successes_histogram.png")
plt.clf()
def compute_poses_wrt_wrist(self, object_pose, palm_link_pose, goal_pose=None):
object_pos = object_pose[:, 0:3]
object_rot = object_pose[:, 3:7]
palm_link_pos = palm_link_pose[:, 0:3]
palm_link_quat_xyzw = palm_link_pose[:, 3:7]
palm_link_quat_wxyz = palm_link_quat_xyzw[:, [3, 0, 1, 2]]
R_W_P = quaternion_to_matrix(palm_link_quat_wxyz)
T_W_P = torch.eye(4).repeat(R_W_P.shape[0], 1, 1).to(R_W_P.device)
T_W_P[:, 0:3, 0:3] = R_W_P
T_W_P[:, 0:3, 3] = palm_link_pos
object_quat_xyzw = object_rot
object_quat_wxyz = object_quat_xyzw[:, [3, 0, 1, 2]]
R_W_O = quaternion_to_matrix(object_quat_wxyz)
T_W_O = torch.eye(4).repeat(R_W_O.shape[0], 1, 1).to(R_W_O.device)
T_W_O[:, 0:3, 0:3] = R_W_O
T_W_O[:, 0:3, 3] = object_pos
relative_pose = torch.matmul(torch.inverse(T_W_P), T_W_O)
relative_translation = relative_pose[:, 0:3, 3]
relative_quat_wxyz = matrix_to_quaternion(relative_pose[:, 0:3, 0:3])
relative_quat_xyzw = relative_quat_wxyz[:, [1, 2, 3, 0]]
object_pos_wrt_wrist = relative_translation
object_quat_wrt_wrist = relative_quat_xyzw
object_pose_wrt_wrist = torch.cat((object_pos_wrt_wrist, object_quat_wrt_wrist), axis=-1)
if goal_pose == None:
return object_pose_wrt_wrist
goal_pos = goal_pose[:, 0:3]
goal_quat_xyzw = goal_pose[:, 3:7]
goal_quat_wxyz = goal_quat_xyzw[:, [3, 0, 1, 2]]
R_W_G = quaternion_to_matrix(goal_quat_wxyz)
T_W_G = torch.eye(4).repeat(R_W_G.shape[0], 1, 1).to(R_W_G.device)
T_W_G[:, 0:3, 0:3] = R_W_G
T_W_G[:, 0:3, 3] = goal_pos
relative_goal_pose = torch.matmul(torch.inverse(T_W_P), T_W_G)
relative_goal_translation = relative_goal_pose[:, 0:3, 3]
relative_goal_quat_wxyz = matrix_to_quaternion(relative_goal_pose[:, 0:3, 0:3])
relative_goal_quat_xyzw = relative_goal_quat_wxyz[:, [1, 2, 3, 0]]
goal_pose_wrt_wrist = torch.cat((relative_goal_translation, relative_goal_quat_xyzw), axis=-1)
return object_pose_wrt_wrist, goal_pose_wrt_wrist
def convert_pos_quat_to_mat(self, obj_pose_pos_quat):
pos = obj_pose_pos_quat[:, 0:3]
quat_xyzw = obj_pose_pos_quat[:, 3:7]
quat_wxyz = quat_xyzw[:, [3, 0, 1, 2]]
R = quaternion_to_matrix(quat_wxyz)
T = torch.eye(4).repeat(R.shape[0], 1, 1).to(R.device)
T[:, 0:3, 0:3] = R
T[:, 0:3, 3] = pos
return T
def compute_observations(self):
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
self.gym.refresh_force_sensor_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.object_pose = self.root_state_tensor[self.object_indices, 0:7]
self.object_pos = self.root_state_tensor[self.object_indices, 0:3]
self.object_rot = self.root_state_tensor[self.object_indices, 3:7]
self.object_linvel = self.root_state_tensor[self.object_indices, 7:10]
self.object_angvel = self.root_state_tensor[self.object_indices, 10:13]
self.goal_pose = self.goal_states[:, 0:7]
self.goal_pos = self.goal_states[:, 0:3]
self.goal_rot = self.goal_states[:, 3:7]
# Need to update the pose of the cube so that it is represented wrt wrist
self.palm_link_pose = self.rigid_body_states[:, self.palm_link_handle, 0:7].view(-1, 7)
self.object_pose_wrt_wrist, self.goal_pose_wrt_wrist = self.compute_poses_wrt_wrist(self.object_pose,
self.palm_link_pose,
self.goal_pose)
self.goal_wrt_wrist_rot = self.goal_pose_wrt_wrist[:, 3:7]
self.fingertip_state = self.rigid_body_states[:, self.fingertip_handles][:, :, 0:13]
self.fingertip_pos = self.rigid_body_states[:, self.fingertip_handles][:, :, 0:3]
if not self.use_adr and self.randomize:
update_freq = torch.remainder(self.frame + self.cube_pose_refresh_offset, self.cube_pose_refresh_rates) == 0
self.obs_object_pose_freq[update_freq] = self.object_pose_wrt_wrist[update_freq]
# simulate adding delay
update_delay = torch.randn(self.num_envs, device=self.device) > self.cube_obs_delay_prob
self.obs_object_pose[update_delay] = self.obs_object_pose_freq[update_delay]
# increment the frame counter both for manual DR and ADR
self.frame += 1
cube_scale = self.cube_random_params[:, 0]
cube_scale = cube_scale.reshape(-1, 1)
# unscale is [low, upper] -> [-1, 1]
self.obs_dict["dof_pos"][:] = unscale(self.dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits)
self.obs_dict["dof_vel"][:] = self.dof_vel
self.obs_dict["dof_force"][:] = self.force_torque_obs_scale * self.dof_force_tensor
self.obs_dict["object_pose"][:] = self.object_pose_wrt_wrist
self.obs_dict["object_vels"][:, 0:3] = self.object_linvel
self.obs_dict["object_vels"][:, 3:6] = self.vel_obs_scale * self.object_angvel
self.obs_dict["goal_pose"][:] = self.goal_pose_wrt_wrist
self.obs_dict["goal_relative_rot"][:] = quat_mul(self.object_pose_wrt_wrist[:, 3:7], quat_conjugate(self.goal_wrt_wrist_rot))
# This is only needed for manul DR experiments
if not self.use_adr:
self.obs_dict["object_pose_cam"][:] = self.obs_object_pose
self.obs_dict["goal_relative_rot_cam"][:] = quat_mul(self.obs_object_pose[:, 3:7], quat_conjugate(self.goal_wrt_wrist_rot))
self.obs_dict["ft_states"][:] = self.fingertip_state.reshape(self.num_envs, 13 * self.num_fingertips)
self.obs_dict["ft_force_torques"][:] = self.force_torque_obs_scale * self.vec_sensor_tensor # wrenches
self.obs_dict["rb_forces"] = self.rb_forces[:, self.object_rb_handles, :].view(-1, 3)
self.obs_dict["last_actions"][:] = self.actions
if self.randomize:
self.obs_dict["cube_random_params"][:] = self.cube_random_params
self.obs_dict["hand_random_params"][:] = self.hand_random_params
self.obs_dict["gravity_vec"][:] = self.gravity_vec
quat_diff = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
self.curr_rotation_dist = 2.0 * torch.asin(torch.clamp(torch.norm(quat_diff[:, 0:3], p=2, dim=-1), max=1.0))
self.best_rotation_dist = torch.where(self.best_rotation_dist < 0.0, self.curr_rotation_dist, self.best_rotation_dist)
# add rotation distances to the observations so that critic could predict the rewards better
self.obs_dict["rot_dist"][:, 0] = self.curr_rotation_dist
self.obs_dict["rot_dist"][:, 1] = self.best_rotation_dist
def get_random_quat(self, env_ids):
# https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py
# https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L261
uvw = torch_rand_float(0, 1.0, (len(env_ids), 3), device=self.device)
q_w = torch.sqrt(1.0 - uvw[:, 0]) * (torch.sin(2 * np.pi * uvw[:, 1]))
q_x = torch.sqrt(1.0 - uvw[:, 0]) * (torch.cos(2 * np.pi * uvw[:, 1]))
q_y = torch.sqrt(uvw[:, 0]) * (torch.sin(2 * np.pi * uvw[:, 2]))
q_z = torch.sqrt(uvw[:, 0]) * (torch.cos(2 * np.pi * uvw[:, 2]))
new_rot = torch.cat((q_x.unsqueeze(-1), q_y.unsqueeze(-1), q_z.unsqueeze(-1), q_w.unsqueeze(-1)), dim=-1)
return new_rot
def reset_target_pose(self, env_ids, apply_reset=False):
rand_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), 4), device=self.device)
if self.apply_random_quat:
new_rot = self.get_random_quat(env_ids)
else:
new_rot = randomize_rotation(rand_floats[:, 0], rand_floats[:, 1], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids])
self.goal_states[env_ids, 0:3] = self.goal_init_state[env_ids, 0:3]
self.goal_states[env_ids, 3:7] = new_rot
self.root_state_tensor[self.goal_object_indices[env_ids], 0:3] = self.goal_states[env_ids, 0:3] + self.goal_displacement_tensor
self.root_state_tensor[self.goal_object_indices[env_ids], 3:7] = self.goal_states[env_ids, 3:7]
self.root_state_tensor[self.goal_object_indices[env_ids], 7:13] = torch.zeros_like(self.root_state_tensor[self.goal_object_indices[env_ids], 7:13])
if apply_reset:
goal_object_indices = self.goal_object_indices[env_ids].to(torch.int32)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state_tensor),
gymtorch.unwrap_tensor(goal_object_indices), len(env_ids))
self.reset_goal_buf[env_ids] = 0
# change back to non-initialized state
self.best_rotation_dist[env_ids] = -1
def get_relative_rot(self, obj_rot, goal_rot):
return quat_mul(obj_rot, quat_conjugate(goal_rot))
def get_random_cube_observation(self, current_cube_pose):
'''
This function replaces cube pose in some environments
with a random cube pose to simulate noisy perception
estimates in the real world.
It is also called random cube pose injection.
'''
env_ids = np.arange(0, self.num_envs)
rand_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), 5), device=self.device)
if self.apply_random_quat:
new_object_rot = self.get_random_quat(env_ids)
else:
new_object_rot = randomize_rotation(rand_floats[:, 3], rand_floats[:, 4],
self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids])
self.random_cube_poses[:, 0:2] = self.object_init_state[env_ids, 0:2] +\
0.5 * rand_floats[:, 0:2]
self.random_cube_poses[:, 2] = self.object_init_state[env_ids, 2] + \
0.5 * rand_floats[:, 2]
self.random_cube_poses[:, 3:7] = new_object_rot
random_cube_pose_mask = torch.rand(len(env_ids), 1, device=self.device) < self.random_cube_pose_prob
current_cube_pose = current_cube_pose * ~random_cube_pose_mask + self.random_cube_poses * random_cube_pose_mask
return current_cube_pose
def reset_idx(self, env_ids, goal_env_ids):
# generate random values
rand_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), self.num_hand_dofs * 2 + 5), device=self.device)
# randomize start object poses
self.reset_target_pose(env_ids)
# reset rigid body forces
self.rb_forces[env_ids, :, :] = 0.0
# reset object
self.root_state_tensor[self.object_indices[env_ids]] = self.object_init_state[env_ids].clone()
self.root_state_tensor[self.object_indices[env_ids], 0:2] = self.object_init_state[env_ids, 0:2] + \
self.reset_position_noise * rand_floats[:, 0:2]
self.root_state_tensor[self.object_indices[env_ids], self.up_axis_idx] = self.object_init_state[env_ids, self.up_axis_idx] + \
self.reset_position_noise_z * rand_floats[:, self.up_axis_idx]
if self.apply_random_quat:
new_object_rot = self.get_random_quat(env_ids)
else:
new_object_rot = randomize_rotation(rand_floats[:, 3], rand_floats[:, 4], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids])
self.root_state_tensor[self.object_indices[env_ids], 3:7] = new_object_rot
self.root_state_tensor[self.object_indices[env_ids], 7:13] = torch.zeros_like(self.root_state_tensor[self.object_indices[env_ids], 7:13])
object_indices = torch.unique(torch.cat([self.object_indices[env_ids],
self.goal_object_indices[env_ids],
self.goal_object_indices[goal_env_ids]]).to(torch.int32))
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state_tensor),
gymtorch.unwrap_tensor(object_indices), len(object_indices))
# reset random force probabilities
self.random_force_prob[env_ids] = torch.exp((torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(len(env_ids), device=self.device) + torch.log(self.force_prob_range[1]))
# reset allegro hand
delta_max = self.hand_dof_upper_limits - self.hand_dof_default_pos
delta_min = self.hand_dof_lower_limits - self.hand_dof_default_pos
rand_floats_dof_pos = (rand_floats[:, 5:5+self.num_hand_dofs] + 1) / 2
rand_delta = delta_min + (delta_max - delta_min) * rand_floats_dof_pos
pos = self.hand_default_dof_pos + self.reset_dof_pos_noise * rand_delta
self.dof_pos[env_ids, :] = pos
self.dof_vel[env_ids, :] = self.hand_dof_default_vel + \
self.reset_dof_vel_noise * rand_floats[:, 5+self.num_hand_dofs:5+self.num_hand_dofs*2]
self.prev_targets[env_ids, :self.num_hand_dofs] = pos
self.cur_targets[env_ids, :self.num_hand_dofs] = pos
self.prev_prev_targets[env_ids, :self.num_hand_dofs] = pos
hand_indices = self.hand_indices[env_ids].to(torch.int32)
self.gym.set_dof_position_target_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.prev_targets),
gymtorch.unwrap_tensor(hand_indices), len(env_ids))
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(hand_indices), len(env_ids))
# Need to update the pose of the cube so that it is represented wrt wrist
self.palm_link_pose = self.rigid_body_states[:, self.palm_link_handle, 0:7].view(-1, 7)
self.object_pose_wrt_wrist = self.compute_poses_wrt_wrist(self.object_pose,
self.palm_link_pose)
# object pose is represented with respect to the wrist
self.obs_object_pose[env_ids] = self.object_pose_wrt_wrist[env_ids].clone()
self.obs_object_pose_freq[env_ids] = self.object_pose_wrt_wrist[env_ids].clone()
if self.use_adr and len(env_ids) == self.num_envs:
self.progress_buf = torch.randint(0, self.max_episode_length, size=(self.num_envs,), dtype=torch.long, device=self.device)
else:
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
if self.use_adr:
self.apply_reset_buf[env_ids] = 0
self.successes[env_ids] = 0
self.best_rotation_dist[env_ids] = -1
self.hold_count_buf[env_ids] = 0
def get_rna_alpha(self):
"""Function to get RNA alpha value."""
raise NotImplementedError
def get_random_network_adversary_action(self, canonical_action):
if self.enable_rna:
if self.last_step > 0 and self.last_step % self.random_adversary_weight_sample_freq == 0:
self.rna_network._refresh()
rand_action_softmax = self.rna_network(torch.cat([self.dof_pos, self.object_pose_wrt_wrist], axis=-1))
rand_action_inds = torch.argmax(rand_action_softmax, axis=-1)
rand_action_inds = torch.permute(rand_action_inds, (1, 0))
rand_perturbation = torch.gather(self.discretised_dofs, 1, rand_action_inds)
rand_perturbation = torch.permute(rand_perturbation, (1, 0))
# unscale it first (normalise it to [-1, 1])
rand_perturbation = unscale(rand_perturbation,
self.hand_dof_lower_limits[self.actuated_dof_indices],
self.hand_dof_upper_limits[self.actuated_dof_indices])
if not self.use_adr:
action_perturb_mask = torch.rand(self.num_envs, 1, device=self.device) < self.action_perturb_prob
rand_perturbation = ~action_perturb_mask * canonical_action + action_perturb_mask * rand_perturbation
rna_alpha = self.get_rna_alpha()
rand_perturbation = rna_alpha * rand_perturbation + (1 - rna_alpha) * canonical_action
return rand_perturbation
else:
return canonical_action
def update_action_moving_average(self):
# scheduling action moving average
if self.last_step > 0 and self.last_step % self.act_moving_average_scheduled_freq == 0:
sched_scaling = 1.0 / self.act_moving_average_scheduled_steps * min(self.last_step, self.act_moving_average_scheduled_steps)
self.act_moving_average = self.act_moving_average_upper + (self.act_moving_average_lower - self.act_moving_average_upper) * \
sched_scaling
print('action moving average: {}'.format(self.act_moving_average))
print('last_step: {}'.format(self.last_step), ' scheduled steps: {}'.format(self.act_moving_average_scheduled_steps))
self.extras['annealing/action_moving_average_scalar'] = self.act_moving_average
def pre_physics_step(self, actions):
# Anneal action moving average
self.update_action_moving_average()
env_ids_reset = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
goal_env_ids = self.reset_goal_buf.nonzero(as_tuple=False).squeeze(-1)
if self.randomize and not self.use_adr:
self.apply_randomizations(dr_params=self.randomization_params, randomisation_callback=self.randomisation_callback)
elif self.randomize and self.use_adr:
# NB - when we are daing ADR, we must calculate the ADR or new DR vals one step BEFORE applying randomisations
# this is because reset needs to be applied on the next step for it to take effect
env_mask_randomize = (self.reset_buf & ~self.apply_reset_buf).bool()
env_ids_reset = self.apply_reset_buf.nonzero(as_tuple=False).flatten()
if len(env_mask_randomize.nonzero(as_tuple=False).flatten()) > 0:
self.apply_randomizations(dr_params=self.randomization_params,
randomize_buf=env_mask_randomize,
adr_objective=self.successes,
randomisation_callback=self.randomisation_callback)
self.apply_reset_buf[env_mask_randomize] = 1
# if only goals need reset, then call set API
if len(goal_env_ids) > 0 and len(env_ids_reset) == 0:
self.reset_target_pose(goal_env_ids, apply_reset=True)
# if goals need reset in addition to other envs, call set API in reset()
elif len(goal_env_ids) > 0:
self.reset_target_pose(goal_env_ids)
if len(env_ids_reset) > 0:
self.reset_idx(env_ids_reset, goal_env_ids)
self.apply_actions(actions)
self.apply_random_forces()
def apply_action_noise_latency(self):
return self.actions
def apply_actions(self, actions):
self.actions = actions.clone().to(self.device)
refreshed = self.progress_buf == 0
self.prev_actions_queue[refreshed] = unscale(self.dof_pos[refreshed], self.hand_dof_lower_limits,
self.hand_dof_upper_limits).view(-1, 1, self.num_actions)
# Needed for the first step and every refresh
# you don't want to mix with zeros
self.prev_actions[refreshed] = unscale(self.dof_pos[refreshed], self.hand_dof_lower_limits,
self.hand_dof_upper_limits).view(-1, self.num_actions)
# update the actions queue
self.prev_actions_queue[:, 1:] = self.prev_actions_queue[:, :-1].detach()
self.prev_actions_queue[:, 0, :] = self.actions
# apply action delay
actions_delayed = self.apply_action_noise_latency()
# apply random network adversary
actions_delayed = self.get_random_network_adversary_action(actions_delayed)
if self.use_relative_control:
targets = self.prev_targets[:, self.actuated_dof_indices] + self.hand_dof_speed_scale * self.dt * actions_delayed
self.cur_targets[:, self.actuated_dof_indices] = targets
elif self.use_capped_dof_control:
# This is capping the maximum dof velocity
targets = scale(actions_delayed, self.hand_dof_lower_limits[self.actuated_dof_indices],
self.hand_dof_upper_limits[self.actuated_dof_indices])
delta = targets[:, self.actuated_dof_indices] - self.prev_targets[:, self.actuated_dof_indices]
max_dof_delta = self.max_dof_radians_per_second * self.dt * self.control_freq_inv
delta = torch.clamp_(delta, -max_dof_delta, max_dof_delta)
self.cur_targets[:, self.actuated_dof_indices] = self.prev_targets[:, self.actuated_dof_indices] + delta
else:
self.cur_targets[:, self.actuated_dof_indices] = scale(actions_delayed,
self.hand_dof_lower_limits[self.actuated_dof_indices],
self.hand_dof_upper_limits[self.actuated_dof_indices])
self.cur_targets[:, self.actuated_dof_indices] = self.act_moving_average * self.cur_targets[:,self.actuated_dof_indices] + \
(1.0 - self.act_moving_average) * self.prev_targets[:, self.actuated_dof_indices]
self.cur_targets[:, self.actuated_dof_indices] = tensor_clamp(self.cur_targets[:, self.actuated_dof_indices],
self.hand_dof_lower_limits[self.actuated_dof_indices], self.hand_dof_upper_limits[self.actuated_dof_indices])
self.dof_delta = self.cur_targets[:, self.actuated_dof_indices] - self.prev_targets[:, self.actuated_dof_indices]
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(self.cur_targets))
self.prev_actions[:] = self.actions.clone()
def apply_random_forces(self):
"""Applies random forces to the object.
Forces are applied as in https://arxiv.org/abs/1808.00177
"""
if self.force_scale > 0.0:
self.rb_forces *= torch.pow(self.force_decay, self.dt / self.force_decay_interval)
# apply new forces
force_indices = (torch.rand(self.num_envs, device=self.device) < self.random_force_prob).nonzero()
self.rb_forces[force_indices, self.object_rb_handles, :] = torch.randn(
self.rb_forces[force_indices, self.object_rb_handles, :].shape, device=self.device) * self.object_rb_masses * self.force_scale
self.gym.apply_rigid_body_force_tensors(self.sim, gymtorch.unwrap_tensor(self.rb_forces), None, gymapi.LOCAL_SPACE)
def post_physics_step(self):
self.progress_buf += 1
# This is for manual DR so ADR has to be OFF
if self.randomize and not self.use_adr:
# This buffer is needed for manual DR randomisation
self.randomize_buf += 1
self.compute_observations()
self.compute_reward(self.actions)
# update the previous targets
self.prev_targets[:, self.actuated_dof_indices] = self.cur_targets[:, self.actuated_dof_indices]
# save and viz dr params changing on the fly
self.track_dr_params()
if self.viewer and self.debug_viz:
# draw axes on target object
self.gym.clear_lines(self.viewer)
self.gym.refresh_rigid_body_state_tensor(self.sim)
for i in range(self.num_envs):
targetx = (self.goal_pos[i] + quat_apply(self.goal_rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
targety = (self.goal_pos[i] + quat_apply(self.goal_rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
targetz = (self.goal_pos[i] + quat_apply(self.goal_rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.goal_pos[i].cpu().numpy() + self.goal_displacement_tensor.cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], targetx[0], targetx[1], targetx[2]], [0.85, 0.1, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], targety[0], targety[1], targety[2]], [0.1, 0.85, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], targetz[0], targetz[1], targetz[2]], [0.1, 0.1, 0.85])
objectx = (self.object_pos[i] + quat_apply(self.object_rot[i], to_torch([1, 0, 0], device=self.device) * 0.2)).cpu().numpy()
objecty = (self.object_pos[i] + quat_apply(self.object_rot[i], to_torch([0, 1, 0], device=self.device) * 0.2)).cpu().numpy()
objectz = (self.object_pos[i] + quat_apply(self.object_rot[i], to_torch([0, 0, 1], device=self.device) * 0.2)).cpu().numpy()
p0 = self.object_pos[i].cpu().numpy()
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], objectx[0], objectx[1], objectx[2]], [0.85, 0.1, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], objecty[0], objecty[1], objecty[2]], [0.1, 0.85, 0.1])
self.gym.add_lines(self.viewer, self.envs[i], 1, [p0[0], p0[1], p0[2], objectz[0], objectz[1], objectz[2]], [0.1, 0.1, 0.85])
def track_dr_params(self):
'''
Track the parameters you wish to here
'''
pass
def _read_cfg(self):
'''
reads various variables from the config file
'''
self.randomize = self.cfg["task"]["randomize"]
self.randomization_params = self.cfg["task"]["randomization_params"]
self.aggregate_mode = self.cfg["env"]["aggregateMode"]
self.dist_reward_scale = self.cfg["env"]["distRewardScale"]
self.rot_reward_scale = self.cfg["env"]["rotRewardScale"]
self.action_penalty_scale = self.cfg["env"]["actionPenaltyScale"]
self.action_delta_penalty_scale = self.cfg["env"]["actionDeltaPenaltyScale"]
self.success_tolerance = self.cfg["env"]["successTolerance"]
self.reach_goal_bonus = self.cfg["env"]["reachGoalBonus"]
self.fall_dist = self.cfg["env"]["fallDistance"]
self.fall_penalty = self.cfg["env"]["fallPenalty"]
self.rot_eps = self.cfg["env"]["rotEps"]
self.vel_obs_scale = 0.2 # scale factor of velocity based observations
self.force_torque_obs_scale = 10.0 # scale factor of velocity based observations
if "max_effort" in self.cfg["env"]:
self.max_effort = self.cfg["env"]["max_effort"]
else:
self.max_effort = 0.35
self.reset_position_noise = self.cfg["env"]["resetPositionNoise"]
self.reset_position_noise_z = self.cfg["env"]["resetPositionNoiseZ"]
self.reset_rotation_noise = self.cfg["env"]["resetRotationNoise"]
self.reset_dof_pos_noise = self.cfg["env"]["resetDofPosRandomInterval"]
self.reset_dof_vel_noise = self.cfg["env"]["resetDofVelRandomInterval"]
self.start_object_pose_dy = self.cfg["env"]["startObjectPoseDY"]
self.start_object_pose_dz = self.cfg["env"]["startObjectPoseDZ"]
self.force_scale = self.cfg["env"].get("forceScale", 0.0)
self.force_prob_range = self.cfg["env"].get("forceProbRange", [0.001, 0.1])
self.force_decay = self.cfg["env"].get("forceDecay", 0.99)
self.force_decay_interval = self.cfg["env"].get("forceDecayInterval", 0.08)
self.dof_speed_scale = self.cfg["env"]["dofSpeedScale"]
self.use_relative_control = self.cfg["env"]["useRelativeControl"]
self.use_capped_dof_control = self.cfg["env"]["use_capped_dof_control"]
self.max_dof_radians_per_second = self.cfg["env"]["max_dof_radians_per_second"]
self.num_success_hold_steps = self.cfg["env"].get("num_success_hold_steps", 1)
# Moving average related
self.act_moving_average_range = self.cfg["env"]["actionsMovingAverage"]["range"]
self.act_moving_average_scheduled_steps = self.cfg["env"]["actionsMovingAverage"]["schedule_steps"]
self.act_moving_average_scheduled_freq = self.cfg["env"]["actionsMovingAverage"]["schedule_freq"]
self.act_moving_average_lower = self.act_moving_average_range[0]
self.act_moving_average_upper = self.act_moving_average_range[1]
self.act_moving_average = self.act_moving_average_upper
# Random cube observation
has_random_cube_obs = 'random_cube_observation' in self.cfg["env"]
if has_random_cube_obs:
self.enable_random_obs = self.cfg["env"]["random_cube_observation"]["enable"]
self.random_cube_pose_prob = self.cfg["env"]["random_cube_observation"]["prob"]
else:
self.enable_random_obs = False
# We have two ways to sample quaternions where one of the samplings is biased
# If this flag is enabled, the sampling will be UNBIASED
self.apply_random_quat = self.cfg['env'].get("apply_random_quat", True)
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.reset_time = self.cfg["env"].get("resetTime", -1.0)
self.print_success_stat = self.cfg["env"]["printNumSuccesses"]
self.eval_stats_name = self.cfg["env"].get("evalStatsName", '')
self.num_eval_frames = self.cfg["env"].get("numEvalFrames", None)
self.max_consecutive_successes = self.cfg["env"]["maxConsecutiveSuccesses"]
self.av_factor = self.cfg["env"].get("averFactor", 0.1)
self.cube_obs_delay_prob = self.cfg["env"].get("cubeObsDelayProb", 0.0)
# Action delay
self.action_delay_prob_max = self.cfg["env"]["actionDelayProbMax"]
self.action_latency_max = self.cfg["env"]["actionLatencyMax"]
self.action_latency_scheduled_steps = self.cfg["env"]["actionLatencyScheduledSteps"]
self.frame = 0
self.max_skip_obs = self.cfg["env"].get("maxObjectSkipObs", 1)
self.object_type = self.cfg["env"]["objectType"]
assert self.object_type in ["block", "egg"]
self.asset_files_dict = {
"block": "urdf/objects/cube_multicolor.urdf",
# "block": "urdf/objects/cube_multicolor_sdf.urdf",
"egg": "mjcf/open_ai_assets/hand/egg.xml",
}
if "asset" in self.cfg["env"]:
self.asset_files_dict["block"] = self.cfg["env"]["asset"].get("assetFileNameBlock", self.asset_files_dict["block"])
self.asset_files_dict["egg"] = self.cfg["env"]["asset"].get("assetFileNameEgg", self.asset_files_dict["egg"])
# Random Network Adversary
self.enable_rna = "random_network_adversary" in self.cfg["env"] and self.cfg["env"]["random_network_adversary"]["enable"]
if self.enable_rna:
if "prob" in self.cfg["env"]["random_network_adversary"]:
self.action_perturb_prob = self.cfg["env"]["random_network_adversary"]["prob"]
# how often we want to resample the weights of the random neural network
self.random_adversary_weight_sample_freq = self.cfg["env"]["random_network_adversary"]["weight_sample_freq"]
def _init_pre_sim_buffers(self):
"""Initialise buffers that must be initialised before sim startup."""
# 0 - scale, 1 - mass, 2 - friction
self.cube_random_params = torch.zeros((self.cfg["env"]["numEnvs"], 3), dtype=torch.float, device=self.sim_device)
# 0 - scale
self.hand_random_params = torch.zeros((self.cfg["env"]["numEnvs"], 1), dtype=torch.float, device=self.sim_device)
self.gravity_vec = torch.zeros((self.cfg["env"]["numEnvs"], 3), dtype=torch.float, device=self.sim_device)
def _init_post_sim_buffers(self):
"""Initialise buffers that must be initialised after sim startup."""
self.dt = self.sim_params.dt
control_freq_inv = self.cfg["env"].get("controlFrequencyInv", 1)
if self.reset_time > 0.0:
self.max_episode_length = int(round(self.reset_time/(control_freq_inv * self.dt)))
print("Reset time: ", self.reset_time)
print("New episode length: ", self.max_episode_length)
if self.viewer != None:
cam_pos = gymapi.Vec3(10.0, 5.0, 1.0)
cam_target = gymapi.Vec3(6.0, 5.0, 0.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# get gym GPU state tensors
actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
rigid_body_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, self.num_fingertips * 6)
dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs, self.num_hand_dofs)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
# create some wrapper tensors for different slices
self.hand_default_dof_pos = torch.zeros(self.num_hand_dofs, dtype=torch.float, device=self.device)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_state = self.dof_state.view(self.num_envs, -1, 2)[:, :self.num_hand_dofs]
self.dof_pos = self.dof_state[..., 0]
self.dof_vel = self.dof_state[..., 1]
self.rigid_body_states = gymtorch.wrap_tensor(rigid_body_tensor).view(self.num_envs, -1, 13)
self.num_bodies = self.rigid_body_states.shape[1]
self.root_state_tensor = gymtorch.wrap_tensor(actor_root_state_tensor).view(-1, 13)
self.num_dofs = self.gym.get_sim_dof_count(self.sim) // self.num_envs
print("Num dofs: ", self.num_dofs)
self.prev_targets = torch.zeros((self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device)
self.cur_targets = torch.zeros((self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device)
self.prev_prev_targets = torch.zeros((self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device)
self.global_indices = torch.arange(self.num_envs * 3, dtype=torch.int32, device=self.device).view(self.num_envs, -1)
self.x_unit_tensor = to_torch([1, 0, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.y_unit_tensor = to_torch([0, 1, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.z_unit_tensor = to_torch([0, 0, 1], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.reset_goal_buf = self.reset_buf.clone()
self.hold_count_buf = self.progress_buf.clone()
self.successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.consecutive_successes = torch.zeros(1, dtype=torch.float, device=self.device)
self.av_factor = to_torch(self.av_factor, dtype=torch.float, device=self.device)
self.total_successes = 0
self.total_resets = 0
# object apply random forces parameters
self.force_decay = to_torch(self.force_decay, dtype=torch.float, device=self.device)
self.force_prob_range = to_torch(self.force_prob_range, dtype=torch.float, device=self.device)
self.random_force_prob = torch.exp((torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(self.num_envs, device=self.device) + torch.log(self.force_prob_range[1]))
self.rb_forces = torch.zeros((self.num_envs, self.num_bodies, 3), dtype=torch.float, device=self.device)
# object observations parameters
self.object_pose = self.root_state_tensor[self.object_indices, 0:7]
self.object_pos = self.root_state_tensor[self.object_indices, 0:3]
self.object_rot = self.root_state_tensor[self.object_indices, 3:7]
self.object_linvel = self.root_state_tensor[self.object_indices, 7:10]
self.object_angvel = self.root_state_tensor[self.object_indices, 10:13]
# buffer storing object poses which are only refreshed every n steps
self.obs_object_pose_freq = self.object_pose.clone()
# buffer storing object poses with added delay which are only refreshed every n steps
self.obs_object_pose = self.object_pose.clone()
self.current_object_pose = self.object_pose.clone()
self.object_pose_wrt_wrist = torch.zeros_like(self.object_pose)
self.object_pose_wrt_wrist[:, 6] = 1.0
self.prev_object_pose = self.object_pose.clone()
# inverse refresh rate for each environment
self.cube_pose_refresh_rates = torch.randint(1, self.max_skip_obs+1, size=(self.num_envs,), device=self.device)
# offset so not all the environments have it each time
self.cube_pose_refresh_offset = torch.randint(0, self.max_skip_obs, size=(self.num_envs,), device=self.device)
self.prev_actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device)
# Related to action delay
self.prev_actions_queue = torch.zeros(self.cfg["env"]["numEnvs"], \
self.action_latency_max+1, self.cfg["env"]["numActions"], dtype=torch.float, device=self.sim_device)
# We have action latency MIN and MAX (declared in _read_cfg() function reading from a config file)
self.action_latency_min = 1
self.action_latency = torch.randint(0, self.action_latency_min + 1, \
size=(self.cfg["env"]["numEnvs"],), dtype=torch.long, device=self.device)
# tensors for rotation approach reward (-1 stands for not initialized)
self.curr_rotation_dist = None
self.best_rotation_dist = -torch.ones(self.num_envs, dtype=torch.float, device=self.device)
self.unique_cube_rotations = torch.tensor(unique_cube_rotations_3d(), dtype=torch.float, device=self.device)
self.unique_cube_rotations = matrix_to_quaternion(self.unique_cube_rotations)
self.num_unique_cube_rotations = self.unique_cube_rotations.shape[0]
def randomisation_callback(self, param_name, param_val, env_id=None, actor=None):
if param_name == "gravity":
self.gravity_vec[:, 0] = param_val.x
self.gravity_vec[:, 1] = param_val.y
self.gravity_vec[:, 2] = param_val.z
elif param_name == "scale" and actor == "object":
self.cube_random_params[env_id, 0] = param_val.mean()
elif param_name == "mass" and actor == "object":
self.cube_random_params[env_id, 1] = np.mean(param_val)
elif param_name == "friction" and actor == "object":
self.cube_random_params[env_id, 2] = np.mean(param_val)
elif param_name == "scale" and actor == "hand":
self.hand_random_params[env_id, 0] = param_val.mean()
class AllegroHandDextremeADR(AllegroHandDextreme):
def _init_pre_sim_buffers(self):
super()._init_pre_sim_buffers()
"""Initialise buffers that must be initialised before sim startup."""
self.cube_pose_refresh_rate = torch.zeros(self.cfg["env"]["numEnvs"], device=self.sim_device, dtype=torch.long)
# offset so not all the environments have it each time
self.cube_pose_refresh_offset = torch.zeros(self.cfg["env"]["numEnvs"], device=self.sim_device, dtype=torch.long)
# stores previous actions
self.prev_actions_queue = torch.zeros(self.cfg["env"]["numEnvs"], self.action_latency_max + 1, self.cfg["env"]["numActions"], dtype=torch.float, device=self.sim_device)
# tensors to store random affine transforms
self.affine_actions_scaling = torch.ones(self.cfg["env"]["numEnvs"], self.cfg["env"]["numActions"], dtype=torch.float, device=self.sim_device)
self.affine_actions_additive = torch.zeros(self.cfg["env"]["numEnvs"], self.cfg["env"]["numActions"], dtype=torch.float, device=self.sim_device)
self.affine_cube_pose_scaling = torch.ones(self.cfg["env"]["numEnvs"], 7, dtype=torch.float, device=self.sim_device)
self.affine_cube_pose_additive = torch.zeros(self.cfg["env"]["numEnvs"], 7, dtype=torch.float, device=self.sim_device)
self.affine_dof_pos_scaling = torch.ones(self.cfg["env"]["numEnvs"], 16, dtype=torch.float, device=self.sim_device)
self.affine_dof_pos_additive = torch.zeros(self.cfg["env"]["numEnvs"], 16, dtype=torch.float, device=self.sim_device)
self.action_latency = torch.zeros(self.cfg["env"]["numEnvs"], dtype=torch.long, device=self.sim_device)
def sample_discrete_adr(self, param_name, env_ids):
"""Samples a discrete value from ADR continuous distribution.
Eg, given a parameter with uniform sampling range
[0, 0.4]
Will sample 0 with 40% probability and 1 with 60% probability.
"""
adr_value = self.get_adr_tensor(param_name, env_ids=env_ids)
continuous_fuzzed = adr_value + (- (torch.rand_like(adr_value) - 0.5))
return continuous_fuzzed.round().long()
def sample_gaussian_adr(self, param_name, env_ids, trailing_dim=1):
adr_value = self.get_adr_tensor(param_name, env_ids=env_ids).view(-1, 1)
nonlinearity = torch.exp(torch.pow(adr_value, 2.)) - 1.
stdev = torch.where(adr_value > 0, nonlinearity, torch.zeros_like(adr_value))
return torch.randn(len(env_ids), trailing_dim, device=self.device, dtype=torch.float) * stdev
def get_rna_alpha(self):
return self.get_adr_tensor('rna_alpha').view(-1, 1)
def apply_randomizations(self, dr_params, randomize_buf, adr_objective=None, randomisation_callback=None):
super().apply_randomizations(dr_params, randomize_buf, adr_objective, randomisation_callback=self.randomisation_callback)
randomize_env_ids = randomize_buf.nonzero(as_tuple=False).squeeze(-1)
self.action_latency[randomize_env_ids] = self.sample_discrete_adr("action_latency", randomize_env_ids)
self.cube_pose_refresh_rate[randomize_env_ids] = self.sample_discrete_adr("cube_pose_refresh_rate", randomize_env_ids)
# Nb - code is to generate uniform from 1 to max_skip_obs (inclusive), but cant use
# torch.uniform as it doesn't support a different max/min value on each
self.cube_pose_refresh_offset[randomize_buf] = \
(torch.rand(randomize_env_ids.shape, device=self.device, dtype=torch.float) \
* (self.cube_pose_refresh_rate[randomize_env_ids].view(-1).float()) - 0.5).round().long() # offset range shifted back by one
self.affine_actions_scaling[randomize_env_ids] = 1. + self.sample_gaussian_adr("affine_action_scaling", randomize_env_ids, trailing_dim=self.num_actions)
self.affine_actions_additive[randomize_env_ids] = self.sample_gaussian_adr("affine_action_additive", randomize_env_ids, trailing_dim=self.num_actions)
self.affine_cube_pose_scaling[randomize_env_ids] = 1. + self.sample_gaussian_adr("affine_cube_pose_scaling", randomize_env_ids, trailing_dim=7)
self.affine_cube_pose_additive[randomize_env_ids] = self.sample_gaussian_adr("affine_cube_pose_additive", randomize_env_ids, trailing_dim=7)
self.affine_dof_pos_scaling[randomize_env_ids] = 1. + self.sample_gaussian_adr("affine_dof_pos_scaling", randomize_env_ids, trailing_dim=16)
self.affine_dof_pos_additive[randomize_env_ids] = self.sample_gaussian_adr("affine_dof_pos_additive", randomize_env_ids, trailing_dim=16)
def create_sim(self):
super().create_sim()
# If randomizing, apply once immediately on startup before the fist sim step
if self.randomize and self.use_adr:
adr_objective = torch.zeros(self.num_envs, dtype=float, device=self.device) if self.use_adr else None
apply_rand_ones = torch.ones(self.num_envs, dtype=bool, device=self.device)
self.apply_randomizations(self.randomization_params, apply_rand_ones, adr_objective=adr_objective,
randomisation_callback=self.randomisation_callback)
def apply_action_noise_latency(self):
action_delay_mask = (torch.rand(self.num_envs, device=self.device) < self.get_adr_tensor("action_delay_prob")).view(-1, 1)
actions = \
self.prev_actions_queue[torch.arange(self.prev_actions_queue.shape[0]), self.action_latency] * ~action_delay_mask \
+ self.prev_actions * action_delay_mask
white_noise = self.sample_gaussian_adr("affine_action_white", self.all_env_ids, trailing_dim=self.num_actions)
actions = self.affine_actions_scaling * actions + self.affine_actions_additive + white_noise
return actions
def compute_observations(self):
super().compute_observations()
update_freq = torch.remainder(self.frame + self.cube_pose_refresh_offset, self.cube_pose_refresh_rate) == 0
# get white noise
white_noise_pose = self.sample_gaussian_adr("affine_cube_pose_white", self.all_env_ids, trailing_dim=7)
# compute noisy object pose as a stochatsic affine transform of actual
noisy_object_pose = self.get_random_cube_observation(
self.affine_cube_pose_scaling * self.object_pose_wrt_wrist + self.affine_cube_pose_additive + white_noise_pose
)
self.obs_object_pose_freq[update_freq] = noisy_object_pose[update_freq]
# simulate adding delay
cube_obs_delay_prob = self.get_adr_tensor("cube_obs_delay_prob", self.all_env_ids).view(self.num_envs,)
update_delay = torch.rand(self.num_envs, device=self.device) < cube_obs_delay_prob
# update environments that are NOT delayed
self.obs_object_pose[~update_delay] = self.obs_object_pose_freq[~update_delay]
white_noise_dof_pos = self.sample_gaussian_adr("affine_dof_pos_white", self.all_env_ids, trailing_dim=16)
self.dof_pos_randomized = self.affine_dof_pos_scaling * self.dof_pos + self.affine_dof_pos_additive + white_noise_dof_pos
cube_scale = self.cube_random_params[:, 0]
cube_scale = cube_scale.reshape(-1, 1)
self.obs_dict["dof_pos_randomized"][:] = unscale(self.dof_pos_randomized, self.hand_dof_lower_limits, self.hand_dof_upper_limits)
self.obs_dict["object_pose_cam_randomized"][:] = self.obs_object_pose
self.obs_dict["goal_relative_rot_cam_randomized"][:] = quat_mul(self.obs_object_pose[:, 3:7], quat_conjugate(self.goal_wrt_wrist_rot))
self.obs_dict["stochastic_delay_params"][:] = torch.stack([
self.get_adr_tensor("cube_obs_delay_prob"),
self.cube_pose_refresh_rate.float() / 6.0,
self.get_adr_tensor("action_delay_prob"),
self.action_latency.float() / 60.0,
], dim=1)
self.obs_dict["affine_params"][:] = torch.cat([
self.affine_actions_scaling,
self.affine_actions_additive,
self.affine_cube_pose_scaling,
self.affine_cube_pose_additive,
self.affine_dof_pos_scaling,
self.affine_dof_pos_additive
],
dim=-1)
def _read_cfg(self):
super()._read_cfg()
self.vel_obs_scale = 1.0 # scale factor of velocity based observations
self.force_torque_obs_scale = 1.0 # scale factor of velocity based observations
return
class AllegroHandDextremeManualDR(AllegroHandDextreme):
def _init_post_sim_buffers(self):
super()._init_post_sim_buffers()
# We could potentially update this regularly
self.action_delay_prob = self.action_delay_prob_max * \
torch.rand(self.cfg["env"]["numEnvs"], dtype=torch.float, device=self.device)
# inverse refresh rate for each environment
self.cube_pose_refresh_rate = torch.randint(1, self.max_skip_obs+1, size=(self.num_envs,), device=self.device)
# offset so not all the environments have it each time
self.cube_pose_refresh_offset = torch.randint(0, self.max_skip_obs, size=(self.num_envs,), device=self.device)
def get_num_obs_dict(self, num_dofs=16):
return {"dof_pos": num_dofs,
"dof_vel": num_dofs,
"dof_force": num_dofs, # generalised forces
"object_pose": 7,
"object_vels": 6,
"goal_pose": 7,
"goal_relative_rot": 4,
"object_pose_cam": 7,
"goal_relative_rot_cam": 4,
"last_actions": num_dofs,
"cube_random_params": 3,
"hand_random_params": 1,
"gravity_vec": 3,
"rot_dist": 2,
"ft_states": 13 * self.num_fingertips, # (pos, quat, linvel, angvel) per fingertip
"ft_force_torques": 6 * self.num_fingertips, # wrenches
}
def get_rna_alpha(self):
if self.randomize:
return torch.rand(self.num_envs, 1, device=self.device)
else:
return torch.zeros(self.num_envs, 1, device=self.device)
def create_sim(self):
super().create_sim()
# If randomizing, apply once immediately on startup before the fist sim step
# ADR has its own create_sim and randomisation is called there with appropriate
# inputs
if self.randomize and not self.use_adr:
self.apply_randomizations(self.randomization_params, randomisation_callback=self.randomisation_callback)
def apply_randomizations(self, dr_params, randomize_buf=None, adr_objective=None, randomisation_callback=None):
super().apply_randomizations(dr_params, randomize_buf=None, adr_objective=None, randomisation_callback=self.randomisation_callback)
def apply_action_noise_latency(self):
# anneal action latency
if self.randomize:
self.cur_action_latency = 1.0 / self.action_latency_scheduled_steps \
* min(self.last_step, self.action_latency_scheduled_steps)
self.cur_action_latency = min(max(int(self.cur_action_latency), self.action_latency_min), self.action_latency_max)
self.extras['annealing/cur_action_latency_max'] = self.cur_action_latency
self.action_latency = torch.randint(0, self.cur_action_latency + 1, \
size=(self.cfg["env"]["numEnvs"],), dtype=torch.long, device=self.device)
# probability of not updating the action this step (on top of the delay)
action_delay_mask = (torch.rand(self.num_envs, device=self.device) > self.action_delay_prob).view(-1, 1)
actions_delayed = \
self.prev_actions_queue[torch.arange(self.prev_actions_queue.shape[0]), self.action_latency] * action_delay_mask \
+ self.prev_actions * ~action_delay_mask
return actions_delayed
def compute_observations(self):
super().compute_observations()
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def compute_hand_reward(
rew_buf, reset_buf, reset_goal_buf, progress_buf, hold_count_buf, cur_targets, prev_targets, hand_dof_vel, successes, consecutive_successes,
max_episode_length: float, object_pos, object_rot, target_pos, target_rot,
dist_reward_scale: float, rot_reward_scale: float, rot_eps: float,
actions, action_penalty_scale: float, action_delta_penalty_scale: float, #max_velocity: float,
success_tolerance: float, reach_goal_bonus: float, fall_dist: float,
fall_penalty: float, max_consecutive_successes: int, av_factor: float, num_success_hold_steps: int
) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
# Distance from the hand to the object
goal_dist = torch.norm(object_pos - target_pos, p=2, dim=-1)
# Orientation alignment for the cube in hand and goal cube
quat_diff = quat_mul(object_rot, quat_conjugate(target_rot))
rot_dist = 2.0 * torch.asin(torch.clamp(torch.norm(quat_diff[:, 0:3], p=2, dim=-1), max=1.0))
dist_rew = goal_dist * dist_reward_scale
rot_rew = 1.0/(torch.abs(rot_dist) + rot_eps) * rot_reward_scale
action_penalty = action_penalty_scale * torch.sum(actions ** 2, dim=-1)
action_delta_penalty = action_delta_penalty_scale * torch.sum((cur_targets - prev_targets) ** 2, dim=-1)
max_velocity = 5.0 #rad/s
vel_tolerance = 1.0
velocity_penalty_coef = -0.05
# todo add actions regularization
velocity_penalty = velocity_penalty_coef * torch.sum((hand_dof_vel/(max_velocity - vel_tolerance)) ** 2, dim=-1)
# Find out which envs hit the goal and update successes count
goal_reached = torch.where(torch.abs(rot_dist) <= success_tolerance, torch.ones_like(reset_goal_buf), reset_goal_buf)
hold_count_buf = torch.where(goal_reached, hold_count_buf + 1, torch.zeros_like(goal_reached))
goal_resets = torch.where(hold_count_buf > num_success_hold_steps, torch.ones_like(reset_goal_buf), reset_goal_buf)
successes = successes + goal_resets
# Success bonus: orientation is within `success_tolerance` of goal orientation
reach_goal_rew = (goal_resets == 1) * reach_goal_bonus
# Fall penalty: distance to the goal is larger than a threashold
fall_rew = (goal_dist >= fall_dist) * fall_penalty
# Check env termination conditions, including maximum success number
resets = torch.where(goal_dist >= fall_dist, torch.ones_like(reset_buf), reset_buf)
if max_consecutive_successes > 0:
# Reset progress buffer on goal envs if max_consecutive_successes > 0
progress_buf = torch.where(torch.abs(rot_dist) <= success_tolerance, torch.zeros_like(progress_buf), progress_buf)
resets = torch.where(successes >= max_consecutive_successes, torch.ones_like(resets), resets)
timed_out = progress_buf >= max_episode_length - 1
resets = torch.where(timed_out, torch.ones_like(resets), resets)
# Apply penalty for not reaching the goal
timeout_rew = timed_out * 0.5 * fall_penalty
# Total reward is: position distance + orientation alignment + action regularization + success bonus + fall penalty
reward = dist_rew + rot_rew + action_penalty + action_delta_penalty + velocity_penalty + reach_goal_rew + fall_rew + timeout_rew
num_resets = torch.sum(resets)
finished_cons_successes = torch.sum(successes * resets.float())
cons_successes = torch.where(num_resets > 0, av_factor*finished_cons_successes/num_resets + (1.0 - av_factor)*consecutive_successes, consecutive_successes)
return reward, resets, goal_resets, progress_buf, hold_count_buf, successes, cons_successes, \
dist_rew, rot_rew, action_penalty, action_delta_penalty, velocity_penalty, reach_goal_rew, fall_rew, timeout_rew # return individual rewards for visualization
@torch.jit.script
def randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor):
return quat_mul(quat_from_angle_axis(rand0 * np.pi, x_unit_tensor),
quat_from_angle_axis(rand1 * np.pi, y_unit_tensor))
def unique_cube_rotations_3d() -> List[np.ndarray]:
"""
Returns the list of all possible 90-degree cube rotations in 3D.
Based on https://stackoverflow.com/a/70413438/1645784
"""
all_rotations = []
for x, y, z in permutations([0, 1, 2]):
for sx, sy, sz in itertools.product([-1, 1], repeat=3):
rotation_matrix = np.zeros((3, 3))
rotation_matrix[0, x] = sx
rotation_matrix[1, y] = sy
rotation_matrix[2, z] = sz
if np.linalg.det(rotation_matrix) == 1:
all_rotations.append(rotation_matrix)
return all_rotations | 83,095 | Python | 48.198342 | 183 | 0.619592 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/dextreme/adr_vec_task.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import copy
from typing import Dict, Any, Tuple, List, Set
import gym
from gym import spaces
from isaacgym import gymtorch, gymapi
from isaacgymenvs.utils.dr_utils import get_property_setter_map, get_property_getter_map, \
get_default_setter_args, apply_random_samples, check_buckets, generate_random_samples
import torch
import numpy as np
import operator, random
from copy import deepcopy
from isaacgymenvs.utils.utils import nested_dict_get_attr, nested_dict_set_attr
from collections import deque
from enum import Enum
import sys
import abc
from abc import ABC
from omegaconf import ListConfig
class RolloutWorkerModes:
ADR_ROLLOUT = 0 # rollout with current ADR params
ADR_BOUNDARY = 1 # rollout with params on boundaries of ADR, used to decide whether to expand ranges
TEST_ENV = 2 # rollout wit default DR params, used to measure overall success rate. (currently unused)
from isaacgymenvs.tasks.base.vec_task import Env, VecTask
class EnvDextreme(Env):
def __init__(self, config: Dict[str, Any], rl_device: str, sim_device: str, graphics_device_id: int, headless: bool, use_dict_obs: bool):
Env.__init__(self, config, rl_device, sim_device, graphics_device_id, headless)
self.use_dict_obs = use_dict_obs
if self.use_dict_obs:
self.obs_dims = config["env"]["obsDims"]
self.obs_space = spaces.Dict(
{
k: spaces.Box(
np.ones(shape=dims) * -np.Inf, np.ones(shape=dims) * np.Inf
)
for k, dims in self.obs_dims.items()
}
)
else:
self.num_observations = config["env"]["numObservations"]
self.num_states = config["env"].get("numStates", 0)
self.obs_space = spaces.Box(np.ones(self.num_obs) * -np.Inf, np.ones(self.num_obs) * np.Inf)
self.state_space = spaces.Box(np.ones(self.num_states) * -np.Inf, np.ones(self.num_states) * np.Inf)
def get_env_state(self):
"""
Return serializable environment state to be saved to checkpoint.
Can be used for stateful training sessions, i.e. with adaptive curriculums.
"""
return None
def set_env_state(self, env_state):
pass
class VecTaskDextreme(EnvDextreme, VecTask):
def __init__(self, config, rl_device, sim_device, graphics_device_id, headless, use_dict_obs=False):
"""Initialise the `VecTask`.
Args:
config: config dictionary for the environment.
sim_device: the device to simulate physics on. eg. 'cuda:0' or 'cpu'
graphics_device_id: the device ID to render with.
headless: Set to False to disable viewer rendering.
"""
EnvDextreme.__init__(self, config, rl_device, sim_device, graphics_device_id, headless, use_dict_obs=use_dict_obs)
self.sim_params = self._VecTask__parse_sim_params(self.cfg["physics_engine"], self.cfg["sim"])
if self.cfg["physics_engine"] == "physx":
self.physics_engine = gymapi.SIM_PHYSX
elif self.cfg["physics_engine"] == "flex":
self.physics_engine = gymapi.SIM_FLEX
else:
msg = f"Invalid physics engine backend: {self.cfg['physics_engine']}"
raise ValueError(msg)
self.virtual_display = None
# optimization flags for pytorch JIT
torch._C._jit_set_profiling_mode(False)
torch._C._jit_set_profiling_executor(False)
self.gym = gymapi.acquire_gym()
self.first_randomization = True
self.randomize = self.cfg["task"]["randomize"]
self.randomize_obs_builtin = "observations" in self.cfg["task"].get("randomization_params", {})
self.randomize_act_builtin = "actions" in self.cfg["task"].get("randomization_params", {})
self.randomized_suffix = "randomized"
if self.use_dict_obs and self.randomize and self.randomize_obs_builtin:
self.randomisation_obs = set(self.obs_space.keys()).intersection(set(self.randomization_params['observations'].keys()))
for obs_name in self.randomisation_obs:
self.obs_space[f"{obs_name}_{self.randomized_suffix}"] = self.obs_space[obs_name]
self.obs_dims[f"{obs_name}_{self.randomized_suffix}"] = self.obs_dims[obs_name]
self.obs_randomizations = {}
elif self.randomize_obs_builtin:
self.obs_randomizations = None
self.action_randomizations = None
self.original_props = {}
self.actor_params_generator = None
self.extern_actor_params = {}
self.last_step = -1
self.last_rand_step = -1
for env_id in range(self.num_envs):
self.extern_actor_params[env_id] = None
# create envs, sim and viewer
self.sim_initialized = False
self.create_sim()
self.gym.prepare_sim(self.sim)
self.sim_initialized = True
self.set_viewer()
self.allocate_buffers()
def allocate_buffers(self):
"""Allocate the observation, states, etc. buffers.
These are what is used to set observations and states in the environment classes which
inherit from this one, and are read in `step` and other related functions.
"""
# allocate buffers
if self.use_dict_obs:
self.obs_dict = {
k: torch.zeros(
(self.num_envs, *dims), device=self.device, dtype=torch.float
)
for k, dims in self.obs_dims.items()
}
print("Obs dictinary: ")
print(self.obs_dims)
# print(self.obs_dict)
for k, dims in self.obs_dims.items():
print("1")
print(dims)
self.obs_dict_repeat = {
k: torch.zeros(
(self.num_envs, *dims), device=self.device, dtype=torch.float
)
for k, dims in self.obs_dims.items()
}
else:
self.obs_dict = {}
self.obs_buf = torch.zeros(
(self.num_envs, self.num_obs), device=self.device, dtype=torch.float)
self.states_buf = torch.zeros(
(self.num_envs, self.num_states), device=self.device, dtype=torch.float)
self.rew_buf = torch.zeros(
self.num_envs, device=self.device, dtype=torch.float)
self.reset_buf = torch.ones(
self.num_envs, device=self.device, dtype=torch.long)
self.timeout_buf = torch.zeros(
self.num_envs, device=self.device, dtype=torch.long)
self.progress_buf = torch.zeros(
self.num_envs, device=self.device, dtype=torch.long)
self.randomize_buf = torch.zeros(
self.num_envs, device=self.device, dtype=torch.long)
self.extras = {}
def create_sim(self, compute_device: int, graphics_device: int, physics_engine, sim_params: gymapi.SimParams):
"""Create an Isaac Gym sim object.
Args:
compute_device: ID of compute device to use.
graphics_device: ID of graphics device to use.
physics_engine: physics engine to use (`gymapi.SIM_PHYSX` or `gymapi.SIM_FLEX`)
sim_params: sim params to use.
Returns:
the Isaac Gym sim object.
"""
sim = self.gym.create_sim(compute_device, graphics_device, physics_engine, sim_params)
if sim is None:
print("*** Failed to create sim")
quit()
return sim
def get_state(self):
"""Returns the state buffer of the environment (the priviledged observations for asymmetric training)."""
if self.use_dict_obs:
raise NotImplementedError("No states in vec task when `use_dict_obs=True`")
return torch.clamp(self.states_buf, -self.clip_obs, self.clip_obs).to(self.rl_device)
@abc.abstractmethod
def pre_physics_step(self, actions: torch.Tensor):
"""Apply the actions to the environment (eg by setting torques, position targets).
Args:
actions: the actions to apply
"""
@abc.abstractmethod
def post_physics_step(self):
"""Compute reward and observations, reset any environments that require it."""
def step(self, actions: torch.Tensor) -> Tuple[Dict[str, torch.Tensor], torch.Tensor, torch.Tensor, Dict[str, Any]]:
"""Step the physics of the environment.
Args:
actions: actions to apply
Returns:
Observations, rewards, resets, info
Observations are dict of observations (currently only one member called 'obs')
"""
# randomize actions
if self.action_randomizations is not None and self.randomize_act_builtin:
actions = self.action_randomizations['noise_lambda'](actions)
action_tensor = torch.clamp(actions, -self.clip_actions, self.clip_actions)
# apply actions
self.pre_physics_step(action_tensor)
# step physics and render each frame
for i in range(self.control_freq_inv):
self.render()
self.gym.simulate(self.sim)
if self.device == 'cpu':
self.gym.fetch_results(self.sim, True)
# compute observations, rewards, resets, ...
self.post_physics_step()
# fill time out buffer: set to 1 if we reached the max episode length AND the reset buffer is 1. Timeout == 1 makes sense only if the reset buffer is 1.
self.timeout_buf = (self.progress_buf >= self.max_episode_length - 1) & (self.reset_buf != 0)
# randomize observations
# cannot randomise in the env because of missing suffix in the observation dict
if self.randomize and self.randomize_obs_builtin and self.use_dict_obs and len(self.obs_randomizations) > 0:
for obs_name, v in self.obs_randomizations.items():
self.obs_dict[f"{obs_name}_{self.randomized_suffix}"] = v['noise_lambda'](self.obs_dict[obs_name])
# Random cube pose
if hasattr(self, 'enable_random_obs') and self.enable_random_obs and obs_name == 'object_pose_cam':
self.obs_dict[f"{obs_name}_{self.randomized_suffix}"] \
= self.get_random_cube_observation(self.obs_dict[f"{obs_name}_{self.randomized_suffix}"])
if hasattr(self, 'enable_random_obs') and self.enable_random_obs:
relative_rot = self.get_relative_rot(self.obs_dict['object_pose_cam_'+ self.randomized_suffix][:, 3:7],
self.obs_dict['goal_pose'][:, 3:7])
v = self.obs_randomizations['goal_relative_rot_cam']
self.obs_dict["goal_relative_rot_cam_" + self.randomized_suffix] = v['noise_lambda'](relative_rot)
elif self.randomize and self.randomize_obs_builtin and not self.use_dict_obs and self.obs_randomizations is not None:
self.obs_buf = self.obs_randomizations['noise_lambda'](self.obs_buf)
self.extras["time_outs"] = self.timeout_buf.to(self.rl_device)
if self.use_dict_obs:
obs_dict_ret = {
k: torch.clone(torch.clamp(t, -self.clip_obs, self.clip_obs)).to(
self.rl_device
)
for k, t in self.obs_dict.items()
}
return obs_dict_ret, self.rew_buf.to(self.rl_device), self.reset_buf.to(self.rl_device), self.extras
else:
self.obs_dict["obs"] = torch.clamp(self.obs_buf, -self.clip_obs, self.clip_obs).to(self.rl_device)
# asymmetric actor-critic
if self.num_states > 0:
self.obs_dict["states"] = self.get_state()
return self.obs_dict, self.rew_buf.to(self.rl_device), self.reset_buf.to(self.rl_device), self.extras
def reset(self) -> torch.Tensor:
"""Reset the environment.
Returns:
Observation dictionary
"""
zero_actions = self.zero_actions()
# step the simulator
self.step(zero_actions)
if self.use_dict_obs:
obs_dict_ret = {
k: torch.clone(
torch.clamp(t, -self.clip_obs, self.clip_obs).to(self.rl_device)
)
for k, t in self.obs_dict.items()
}
return obs_dict_ret
else:
self.obs_dict["obs"] = torch.clamp(self.obs_buf, -self.clip_obs, self.clip_obs).to(self.rl_device)
# asymmetric actor-critic
if self.num_states > 0:
self.obs_dict["states"] = self.get_state()
return self.obs_dict
"""
Domain Randomization methods
"""
def get_env_state(self):
"""
Return serializable environment state to be saved to checkpoint.
Can be used for stateful training sessions, i.e. with adaptive curriculums.
"""
if self.use_adr:
return dict(adr_params=self.adr_params)
else:
return {}
def set_env_state(self, env_state):
if env_state is None:
return
for key in self.get_env_state().keys():
if key == "adr_params" and self.use_adr and not self.adr_load_from_checkpoint:
print("Skipping loading ADR params from checkpoint...")
continue
value = env_state.get(key, None)
if value is None:
continue
self.__dict__[key] = value
print(f'Loaded env state value {key}:{value}')
if self.use_adr:
print(f'ADR Params after loading from checkpoint: {self.adr_params}')
def get_randomization_dict(self, dr_params, obs_shape):
dist = dr_params["distribution"]
op_type = dr_params["operation"]
sched_type = dr_params["schedule"] if "schedule" in dr_params else None
sched_step = dr_params["schedule_steps"] if "schedule" in dr_params else None
op = operator.add if op_type == 'additive' else operator.mul
if not self.use_adr:
apply_white_noise_prob = dr_params.get("apply_white_noise", 0.5)
if sched_type == 'linear':
sched_scaling = 1.0 / sched_step * \
min(self.last_step, sched_step)
elif sched_type == 'constant':
sched_scaling = 0 if self.last_step < sched_step else 1
else:
sched_scaling = 1
if dist == 'gaussian':
mu, var = dr_params["range"]
mu_corr, var_corr = dr_params.get("range_correlated", [0., 0.])
if op_type == 'additive':
mu *= sched_scaling
var *= sched_scaling
mu_corr *= sched_scaling
var_corr *= sched_scaling
elif op_type == 'scaling':
var = var * sched_scaling # scale up var over time
mu = mu * sched_scaling + 1.0 * \
(1.0 - sched_scaling) # linearly interpolate
var_corr = var_corr * sched_scaling # scale up var over time
mu_corr = mu_corr * sched_scaling + 1.0 * \
(1.0 - sched_scaling) # linearly interpolate
local_params = {
'mu': mu, 'var': var, 'mu_corr': mu_corr, 'var_corr': var_corr,
'corr': torch.randn(self.num_envs, *obs_shape, device=self.device)
}
if not self.use_adr:
local_params['apply_white_noise_mask'] = (torch.rand(self.num_envs, device=self.device) < apply_white_noise_prob).float()
def noise_lambda(tensor, params=local_params):
corr = local_params['corr']
corr = corr * params['var_corr'] + params['mu_corr']
if self.use_adr:
return op(
tensor, corr + torch.randn_like(tensor) * params['var'] + params['mu'])
else:
return op(
tensor, corr + torch.randn_like(tensor) * params['apply_white_noise_mask'].view(-1, 1) * params['var'] + params['mu'])
elif dist == 'uniform':
lo, hi = dr_params["range"]
lo_corr, hi_corr = dr_params.get("range_correlated", [0., 0.])
if op_type == 'additive':
lo *= sched_scaling
hi *= sched_scaling
lo_corr *= sched_scaling
hi_corr *= sched_scaling
elif op_type == 'scaling':
lo = lo * sched_scaling + 1.0 * (1.0 - sched_scaling)
hi = hi * sched_scaling + 1.0 * (1.0 - sched_scaling)
lo_corr = lo_corr * sched_scaling + 1.0 * (1.0 - sched_scaling)
hi_corr = hi_corr * sched_scaling + 1.0 * (1.0 - sched_scaling)
local_params = {'lo': lo, 'hi': hi, 'lo_corr': lo_corr, 'hi_corr': hi_corr,
'corr': torch.rand(self.num_envs, *obs_shape, device=self.device)
}
if not self.use_adr:
local_params['apply_white_noise_mask'] = (torch.rand(self.num_envs, device=self.device) < apply_white_noise_prob).float()
def noise_lambda(tensor, params=local_params):
corr = params['corr']
corr = corr * (params['hi_corr'] - params['lo_corr']) + params['lo_corr']
if self.use_adr:
return op(tensor, corr + torch.rand_like(tensor) * (params['hi'] - params['lo']) + params['lo'])
else:
return op(tensor, corr + torch.rand_like(tensor) * params['apply_white_noise_mask'].view(-1, 1) * (params['hi'] - params['lo']) + params['lo'])
else:
raise NotImplementedError
# return {'lo': lo, 'hi': hi, 'lo_corr': lo_corr, 'hi_corr': hi_corr, 'noise_lambda': noise_lambda}
return {'noise_lambda': noise_lambda, 'corr_val': local_params['corr']}
class ADRVecTask(VecTaskDextreme):
def __init__(self, config, rl_device, sim_device, graphics_device_id, headless, use_dict_obs=False):
self.adr_cfg = self.cfg["task"].get("adr", {})
self.use_adr = self.adr_cfg.get("use_adr", False)
self.all_env_ids = torch.tensor(list(range(self.cfg["env"]["numEnvs"])), dtype=torch.long, device=sim_device)
if self.use_adr:
self.worker_adr_boundary_fraction = self.adr_cfg["worker_adr_boundary_fraction"]
self.adr_queue_threshold_length = self.adr_cfg["adr_queue_threshold_length"]
self.adr_objective_threshold_low = self.adr_cfg["adr_objective_threshold_low"]
self.adr_objective_threshold_high = self.adr_cfg["adr_objective_threshold_high"]
self.adr_extended_boundary_sample = self.adr_cfg["adr_extended_boundary_sample"]
self.adr_rollout_perf_alpha = self.adr_cfg["adr_rollout_perf_alpha"]
self.update_adr_ranges = self.adr_cfg["update_adr_ranges"]
self.adr_clear_other_queues = self.adr_cfg["clear_other_queues"]
self.adr_rollout_perf_last = None
self.adr_load_from_checkpoint = self.adr_cfg["adr_load_from_checkpoint"]
assert self.randomize, "Worker mode currently only supported when Domain Randomization is turned on"
# 0 = rollout worker
# 1 = ADR worker (see https://arxiv.org/pdf/1910.07113.pdf Section 5)
# 2 = eval worker
# rollout type is selected when an environment gets randomized
self.worker_types = torch.zeros(self.cfg["env"]["numEnvs"], dtype=torch.long, device=sim_device)
self.adr_tensor_values = {}
self.adr_params = self.adr_cfg["params"]
self.adr_params_keys = list(self.adr_params.keys())
# list of params which rely on patching the built in domain randomisation
self.adr_params_builtin_keys = []
for k in self.adr_params:
self.adr_params[k]["range"] = self.adr_params[k]["init_range"]
if "limits" not in self.adr_params[k]:
self.adr_params[k]["limits"] = [None, None]
if "delta_style" in self.adr_params[k]:
assert self.adr_params[k]["delta_style"] in ["additive", "multiplicative"]
else:
self.adr_params[k]["delta_style"] = "additive"
if "range_path" in self.adr_params[k]:
self.adr_params_builtin_keys.append(k)
else: # normal tensorised ADR param
param_type = self.adr_params[k].get("type", "uniform")
dtype = torch.long if param_type == "categorical" else torch.float
self.adr_tensor_values[k] = torch.zeros(self.cfg["env"]["numEnvs"], device=sim_device, dtype=dtype)
self.num_adr_params = len(self.adr_params)
# modes for ADR workers.
# there are 2n modes, where mode 2n is lower range and mode 2n+1 is upper range for DR parameter n
self.adr_modes = torch.zeros(self.cfg["env"]["numEnvs"], dtype=torch.long, device=sim_device)
self.adr_objective_queues = [deque(maxlen=self.adr_queue_threshold_length) for _ in range(2*self.num_adr_params)]
super().__init__(config, rl_device, sim_device, graphics_device_id, headless, use_dict_obs=use_dict_obs)
def get_current_adr_params(self, dr_params):
"""Splices the current ADR parameters into the requried ranges"""
current_adr_params = copy.deepcopy(dr_params)
for k in self.adr_params_builtin_keys:
nested_dict_set_attr(current_adr_params, self.adr_params[k]["range_path"], self.adr_params[k]["range"])
return current_adr_params
def get_dr_params_by_env_id(self, env_id, default_dr_params, current_adr_params):
"""Returns the (dictionary) DR params for a particular env ID.
(only applies to env randomisations, for tensor randomisations see `sample_adr_tensor`.)
Params:
env_id: which env ID to get the dict for.
default_dr_params: environment default DR params.
current_adr_params: current dictionary of DR params with current ADR ranges patched in.
Returns:
a patched dictionary with the env randomisations corresponding to the env ID.
"""
env_type = self.worker_types[env_id]
if env_type == RolloutWorkerModes.ADR_ROLLOUT: # rollout worker, uses current ADR params
return current_adr_params
elif env_type == RolloutWorkerModes.ADR_BOUNDARY: # ADR worker, substitute upper or lower bound as entire range for this env
adr_mode = int(self.adr_modes[env_id])
env_adr_params = copy.deepcopy(current_adr_params)
adr_id = adr_mode // 2 # which adr parameter
adr_bound = adr_mode % 2 # 0 = lower, 1 = upper
param_name = self.adr_params_keys[adr_id]
# this DR parameter is randomised as a tensor not through normal DR api
# if not "range_path" in self.adr_params[self.adr_params_keys[adr_id]]:
if not param_name in self.adr_params_builtin_keys:
return env_adr_params
if self.adr_extended_boundary_sample:
boundary_value = self.adr_params[param_name]["next_limits"][adr_bound]
else:
boundary_value = self.adr_params[param_name]["range"][adr_bound]
new_range = [boundary_value, boundary_value]
nested_dict_set_attr(env_adr_params, self.adr_params[param_name]["range_path"], new_range)
return env_adr_params
elif env_type == RolloutWorkerModes.TEST_ENV: # eval worker, uses default fixed params
return default_dr_params
else:
raise NotImplementedError
def modify_adr_param(self, param, direction, adr_param_dict, param_limit=None):
"""Modify an ADR param.
Args:
param: current value of the param.
direction: what direction to move the ADR parameter ('up' or 'down')
adr_param_dict: dictionary of ADR parameter, used to read delta and method of applying delta
param_limit: limit of the parameter (upper bound for 'up' and lower bound for 'down' mode)
Returns:
whether the param was updated
"""
op = adr_param_dict["delta_style"]
delta = adr_param_dict["delta"]
if direction == 'up':
if op == "additive":
new_val = param + delta
elif op == "multiplicative":
assert delta > 1.0, "Must have delta>1 for multiplicative ADR update."
new_val = param * delta
else:
raise NotImplementedError
if param_limit is not None:
new_val = min(new_val, param_limit)
changed = abs(new_val - param) > 1e-9
return new_val, changed
elif direction == 'down':
if op == "additive":
new_val = param - delta
elif op == "multiplicative":
assert delta > 1.0, "Must have delta>1 for multiplicative ADR update."
new_val = param / delta
else:
raise NotImplementedError
if param_limit is not None:
new_val = max(new_val, param_limit)
changed = abs(new_val - param) > 1e-9
return new_val, changed
else:
raise NotImplementedError
@staticmethod
def env_ids_from_mask(mask):
return torch.nonzero(mask, as_tuple=False).squeeze(-1)
def sample_adr_tensor(self, param_name, env_ids=None):
"""Samples the values for a particular ADR parameter as a tensor.
Sets the value as a side-effect in the dictionary of current adr tensors.
Args:
param_name: name of the parameter to sample
env_ids: env ids to sample
Returns:
(len(env_ids), tensor_dim) tensor of sampled parameter values,
where tensor_dim is the trailing dimension of the generated tensor as
specifide in the ADR conifg
"""
if env_ids is None:
env_ids = self.all_env_ids
sample_mask = torch.zeros(self.num_envs, dtype=torch.bool, device=self.device)
sample_mask[env_ids] = True
params = self.adr_params[param_name]
param_range = params["range"]
next_limits = params.get("next_limits", None)
param_type = params.get("type", "uniform")
n = self.adr_params_keys.index(param_name)
low_idx = 2*n
high_idx = 2*n + 1
adr_workers_low_mask = (self.worker_types == RolloutWorkerModes.ADR_BOUNDARY) & (self.adr_modes == low_idx) & sample_mask
adr_workers_high_mask = (self.worker_types == RolloutWorkerModes.ADR_BOUNDARY) & (self.adr_modes == high_idx) & sample_mask
rollout_workers_mask = (~adr_workers_low_mask) & (~adr_workers_high_mask) & sample_mask
rollout_workers_env_ids = self.env_ids_from_mask(rollout_workers_mask)
if param_type == "uniform":
result = torch.zeros((len(env_ids),), device=self.device, dtype=torch.float)
uniform_noise_rollout_workers = \
torch.rand((rollout_workers_env_ids.shape[0],), device=self.device, dtype=torch.float) \
* (param_range[1] - param_range[0]) + param_range[0]
result[rollout_workers_mask[env_ids]] = uniform_noise_rollout_workers
if self.adr_extended_boundary_sample:
result[adr_workers_low_mask[env_ids]] = next_limits[0]
result[adr_workers_high_mask[env_ids]] = next_limits[1]
else:
result[adr_workers_low_mask[env_ids]] = param_range[0]
result[adr_workers_high_mask[env_ids]] = param_range[1]
elif param_type == "categorical":
result = torch.zeros((len(env_ids), ), device=self.device, dtype=torch.long)
uniform_noise_rollout_workers = torch.randint(int(param_range[0]), int(param_range[1])+1, size=(rollout_workers_env_ids.shape[0], ), device=self.device)
result[rollout_workers_mask[env_ids]] = uniform_noise_rollout_workers
result[adr_workers_low_mask[env_ids]] = int(next_limits[0] if self.adr_extended_boundary_sample else param_range[0])
result[adr_workers_high_mask[env_ids]] = int(next_limits[1] if self.adr_extended_boundary_sample else param_range[1])
else:
raise NotImplementedError(f"Unknown distribution type {param_type}")
self.adr_tensor_values[param_name][env_ids] = result
return result
def get_adr_tensor(self, param_name, env_ids=None):
"""Returns the current value of an ADR tensor.
"""
if env_ids is None:
return self.adr_tensor_values[param_name]
else:
return self.adr_tensor_values[param_name][env_ids]
def recycle_envs(self, recycle_envs):
"""Recycle the workers that have finished their episodes or to be reassigned etc.
Args:
recycle_envs: env_ids of environments to be recycled
"""
worker_types_rand = torch.rand(len(recycle_envs), device=self.device, dtype=torch.float)
new_worker_types = torch.zeros(len(recycle_envs), device=self.device, dtype=torch.long)
# Choose new types for wokrers
new_worker_types[(worker_types_rand < self.worker_adr_boundary_fraction)] = RolloutWorkerModes.ADR_ROLLOUT
new_worker_types[(worker_types_rand >= self.worker_adr_boundary_fraction)] = RolloutWorkerModes.ADR_BOUNDARY
self.worker_types[recycle_envs] = new_worker_types
# resample the ADR modes (which boundary values to sample) for the given environments (only applies to ADR_BOUNDARY mode)
self.adr_modes[recycle_envs] = torch.randint(0, self.num_adr_params * 2, (len(recycle_envs),), dtype=torch.long, device=self.device)
def adr_update(self, rand_envs, adr_objective):
"""Performs ADR update step (implements algorithm 1 from https://arxiv.org/pdf/1910.07113.pdf).
"""
rand_env_mask = torch.zeros(self.num_envs, dtype=torch.bool, device=self.device)
rand_env_mask[rand_envs] = True
total_nats = 0.0 # measuring entropy
if self.update_adr_ranges:
adr_params_iter = list(enumerate(self.adr_params))
random.shuffle(adr_params_iter)
# only recycle once
already_recycled = False
for n, adr_param_name in adr_params_iter:
# mode index for environments evaluating lower ADR bound
low_idx = 2*n
# mode index for environments evaluating upper ADR bound
high_idx = 2*n+1
adr_workers_low = (self.worker_types == RolloutWorkerModes.ADR_BOUNDARY) & (self.adr_modes == low_idx)
adr_workers_high = (self.worker_types == RolloutWorkerModes.ADR_BOUNDARY) & (self.adr_modes == high_idx)
# environments which will be evaluated for ADR (finished the episode) and which are evaluating performance at the
# lower and upper boundaries
adr_done_low = rand_env_mask & adr_workers_low
adr_done_high = rand_env_mask & adr_workers_high
# objective value at environments which have been evaluating the lower bound of ADR param n
objective_low_bounds = adr_objective[adr_done_low]
# objective value at environments which have been evaluating the upper bound of ADR param n
objective_high_bounds = adr_objective[adr_done_high]
# add the success of objectives to queues
self.adr_objective_queues[low_idx].extend(objective_low_bounds.cpu().numpy().tolist())
self.adr_objective_queues[high_idx].extend(objective_high_bounds.cpu().numpy().tolist())
low_queue = self.adr_objective_queues[low_idx]
high_queue = self.adr_objective_queues[high_idx]
mean_low = np.mean(low_queue) if len(low_queue) > 0 else 0.
mean_high = np.mean(high_queue) if len(high_queue) > 0 else 0.
current_range = self.adr_params[adr_param_name]["range"]
range_lower = current_range[0]
range_upper = current_range[1]
range_limits = self.adr_params[adr_param_name]["limits"]
init_range = self.adr_params[adr_param_name]["init_range"]
# one step beyond the current ADR values
[next_limit_lower, next_limit_upper] = self.adr_params[adr_param_name].get("next_limits", [None, None])
changed_low, changed_high = False, False
if len(low_queue) >= self.adr_queue_threshold_length:
changed_low = False
if mean_low < self.adr_objective_threshold_low:
# increase lower bound
range_lower, changed_low = self.modify_adr_param(
range_lower, 'up', self.adr_params[adr_param_name], param_limit=init_range[0]
)
elif mean_low > self.adr_objective_threshold_high:
# reduce lower bound
range_lower, changed_low = self.modify_adr_param(
range_lower, 'down', self.adr_params[adr_param_name], param_limit=range_limits[0]
)
# if the ADR boundary is changed, workers working from the old paremeters become invalid.
# Therefore, while we use the data from them to train, we can no longer use them to evaluate DR at the boundary
if changed_low:
print(f'Changing {adr_param_name} lower bound. Queue length {len(self.adr_objective_queues[low_idx])}. Mean perf: {mean_low}. Old val: {current_range[0]}. New val: {range_lower}')
self.adr_objective_queues[low_idx].clear()
self.worker_types[adr_workers_low] = RolloutWorkerModes.ADR_ROLLOUT
if len(high_queue) >= self.adr_queue_threshold_length:
if mean_high < self.adr_objective_threshold_low:
# reduce upper bound
range_upper, changed_high = self.modify_adr_param(
range_upper, 'down', self.adr_params[adr_param_name], param_limit=init_range[1]
)
elif mean_high > self.adr_objective_threshold_high:
# increase upper bound
range_upper, changed_high = self.modify_adr_param(
range_upper, 'up', self.adr_params[adr_param_name], param_limit=range_limits[1]
)
# if the ADR boundary is changed, workers working from the old paremeters become invalid.
# Therefore, while we use the data from them to train, we can no longer use them to evaluate DR at the boundary
if changed_high:
print(f'Changing upper bound {adr_param_name}. Queue length {len(self.adr_objective_queues[high_idx])}. Mean perf {mean_high}. Old val: {current_range[1]}. New val: {range_upper}')
self.adr_objective_queues[high_idx].clear()
self.worker_types[adr_workers_high] = RolloutWorkerModes.ADR_ROLLOUT
if changed_low or next_limit_lower is None:
next_limit_lower, _ = self.modify_adr_param(range_lower, 'down', self.adr_params[adr_param_name], param_limit=range_limits[0])
if changed_high or next_limit_upper is None:
next_limit_upper, _ = self.modify_adr_param(range_upper, 'up', self.adr_params[adr_param_name], param_limit=range_limits[1])
self.adr_params[adr_param_name]["range"] = [range_lower, range_upper]
if not self.adr_params[adr_param_name]["delta"] < 1e-9: # disabled
upper_lower_delta = range_upper - range_lower
if upper_lower_delta < 1e-3:
upper_lower_delta = 1e-3
nats = np.log(upper_lower_delta)
total_nats += nats
# print(f'nats {nats} delta {upper_lower_delta} range lower {range_lower} range upper {range_upper}')
self.adr_params[adr_param_name]["next_limits"] = [next_limit_lower, next_limit_upper]
if hasattr(self, 'extras') and ((changed_high or changed_low) or self.last_step % 100 == 0): # only log so often to prevent huge log files with ADR vars
self.extras[f'adr/params/{adr_param_name}/lower'] = range_lower
self.extras[f'adr/params/{adr_param_name}/upper'] = range_upper
self.extras[f'adr/objective_perf/boundary/{adr_param_name}/lower/value'] = mean_low
self.extras[f'adr/objective_perf/boundary/{adr_param_name}/lower/queue_len'] = len(low_queue)
self.extras[f'adr/objective_perf/boundary/{adr_param_name}/upper/value'] = mean_high
self.extras[f'adr/objective_perf/boundary/{adr_param_name}/upper/queue_len'] = len(high_queue)
if self.adr_clear_other_queues and (changed_low or changed_high):
for q in self.adr_objective_queues:
q.clear()
recycle_envs = torch.nonzero((self.worker_types == RolloutWorkerModes.ADR_BOUNDARY), as_tuple=False).squeeze(-1)
self.recycle_envs(recycle_envs)
already_recycled = True
break
if hasattr(self, 'extras') and self.last_step % 100 == 0: # only log so often to prevent huge log files with ADR vars
mean_perf = adr_objective[rand_env_mask & (self.worker_types == RolloutWorkerModes.ADR_ROLLOUT)].mean()
if self.adr_rollout_perf_last is None:
self.adr_rollout_perf_last = mean_perf
else:
self.adr_rollout_perf_last = self.adr_rollout_perf_last * self.adr_rollout_perf_alpha + mean_perf * (1-self.adr_rollout_perf_alpha)
self.extras[f'adr/objective_perf/rollouts'] = self.adr_rollout_perf_last
self.extras[f'adr/npd'] = total_nats / len(self.adr_params)
if not already_recycled:
self.recycle_envs(rand_envs)
else:
self.worker_types[rand_envs] = RolloutWorkerModes.ADR_ROLLOUT
# ensure tensors get re-sampled before new episode
for k in self.adr_tensor_values:
self.sample_adr_tensor(k, rand_envs)
def apply_randomizations(self, dr_params, randomize_buf, adr_objective=None, randomisation_callback=None):
"""Apply domain randomizations to the environment.
Note that currently we can only apply randomizations only on resets, due to current PhysX limitations
Args:
dr_params: parameters for domain randomization to use.
randomize_buf: selective randomisation of environments
adr_objective: consecutive successes scalar
randomisation_callback: callbacks we may want to use from the environment class
"""
# If we don't have a randomization frequency, randomize every step
rand_freq = dr_params.get("frequency", 1)
# First, determine what to randomize:
# - non-environment parameters when > frequency steps have passed since the last non-environment
# - physical environments in the reset buffer, which have exceeded the randomization frequency threshold
# - on the first call, randomize everything
self.last_step = self.gym.get_frame_count(self.sim)
# for ADR
if self.use_adr:
if self.first_randomization:
adr_env_ids = list(range(self.num_envs))
else:
adr_env_ids = torch.nonzero(randomize_buf, as_tuple=False).squeeze(-1).tolist()
self.adr_update(adr_env_ids, adr_objective)
current_adr_params = self.get_current_adr_params(dr_params)
if self.first_randomization:
do_nonenv_randomize = True
env_ids = list(range(self.num_envs))
else:
do_nonenv_randomize = (self.last_step - self.last_rand_step) >= rand_freq
env_ids = torch.nonzero(randomize_buf, as_tuple=False).squeeze(-1).tolist()
if do_nonenv_randomize:
self.last_rand_step = self.last_step
# For Manual DR
if not self.use_adr:
if self.first_randomization:
do_nonenv_randomize = True
env_ids = list(range(self.num_envs))
else:
# randomise if the number of steps since the last randomization is greater than the randomization frequency
do_nonenv_randomize = (self.last_step - self.last_rand_step) >= rand_freq
rand_envs = torch.where(self.randomize_buf >= rand_freq, torch.ones_like(self.randomize_buf), torch.zeros_like(self.randomize_buf))
rand_envs = torch.logical_and(rand_envs, self.reset_buf)
env_ids = torch.nonzero(rand_envs, as_tuple=False).squeeze(-1).tolist()
self.randomize_buf[rand_envs] = 0
if do_nonenv_randomize:
self.last_rand_step = self.last_step
# We don't use it for ADR(!)
if self.randomize_act_builtin:
self.action_randomizations = self.get_randomization_dict(dr_params['actions'], (self.num_actions,))
if self.use_dict_obs and self.randomize_obs_builtin:
for nonphysical_param in self.randomisation_obs:
self.obs_randomizations[nonphysical_param] = self.get_randomization_dict(dr_params['observations'][nonphysical_param],
self.obs_space[nonphysical_param].shape)
elif self.randomize_obs_builtin:
self.observation_randomizations = self.get_randomization_dict(dr_params['observations'], self.obs_space.shape)
param_setters_map = get_property_setter_map(self.gym)
param_setter_defaults_map = get_default_setter_args(self.gym)
param_getters_map = get_property_getter_map(self.gym)
# On first iteration, check the number of buckets
if self.first_randomization:
check_buckets(self.gym, self.envs, dr_params)
# Randomize non-environment parameters e.g. gravity, timestep, rest_offset etc.
if "sim_params" in dr_params and do_nonenv_randomize:
prop_attrs = dr_params["sim_params"]
prop = self.gym.get_sim_params(self.sim)
# Get the list of original paramters set in the yaml and we do add/scale
# on these values
if self.first_randomization:
self.original_props["sim_params"] = {
attr: getattr(prop, attr) for attr in dir(prop)}
# Get prop attrs randomised by add/scale of the original_props values
# attr is [gravity, reset_offset, ... ]
# attr_randomization_params can be {'range': [0, 0.5], 'operation': 'additive', 'distribution': 'gaussian'}
# therefore, prop.val = original_val <operator> random sample
# where operator is add/mul
for attr, attr_randomization_params in prop_attrs.items():
apply_random_samples(
prop, self.original_props["sim_params"], attr, attr_randomization_params, self.last_step)
if attr == "gravity":
randomisation_callback('gravity', prop.gravity)
# Randomize physical environments
# if self.last_step % 10 == 0 and self.last_step > 0:
# print('random rest offset = ', prop.physx.rest_offset)
self.gym.set_sim_params(self.sim, prop)
# If self.actor_params_generator is initialized: use it to
# sample actor simulation params. This gives users the
# freedom to generate samples from arbitrary distributions,
# e.g. use full-covariance distributions instead of the DR's
# default of treating each simulation parameter independently.
extern_offsets = {}
if self.actor_params_generator is not None:
for env_id in env_ids:
self.extern_actor_params[env_id] = \
self.actor_params_generator.sample()
extern_offsets[env_id] = 0
# randomise all attributes of each actor (hand, cube etc..)
# actor_properties are (stiffness, damping etc..)
# Loop over envs, then loop over actors, then loop over their props
# and lastly loop over the ranges of the params
for i_, env_id in enumerate(env_ids):
if self.use_adr:
# need to generate a custom dictionary for ADR parameters
env_dr_params = self.get_dr_params_by_env_id(env_id, dr_params, current_adr_params)
else:
env_dr_params = dr_params
for actor, actor_properties in env_dr_params["actor_params"].items():
if self.first_randomization and i_ % 1000 == 0:
print(f'Initializing domain randomization for {actor} env={i_}')
env = self.envs[env_id]
handle = self.gym.find_actor_handle(env, actor)
extern_sample = self.extern_actor_params[env_id]
# randomise dof_props, rigid_body, rigid_shape properties
# all obtained from the YAML file
# EXAMPLE: prop name: dof_properties, rigid_body_properties, rigid_shape properties
# prop_attrs:
# {'damping': {'range': [0.3, 3.0], 'operation': 'scaling', 'distribution': 'loguniform'}
# {'stiffness': {'range': [0.75, 1.5], 'operation': 'scaling', 'distribution': 'loguniform'}
for prop_name, prop_attrs in actor_properties.items():
# These properties are to do with whole obj mesh related
if prop_name == 'color':
num_bodies = self.gym.get_actor_rigid_body_count(
env, handle)
for n in range(num_bodies):
self.gym.set_rigid_body_color(env, handle, n, gymapi.MESH_VISUAL,
gymapi.Vec3(random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1)))
continue
if prop_name == 'scale':
setup_only = prop_attrs.get('setup_only', False)
if (setup_only and not self.sim_initialized) or not setup_only:
attr_randomization_params = prop_attrs
sample = generate_random_samples(attr_randomization_params, 1,
self.last_step, None)
og_scale = 1
if attr_randomization_params['operation'] == 'scaling':
new_scale = og_scale * sample
elif attr_randomization_params['operation'] == 'additive':
new_scale = og_scale + sample
self.gym.set_actor_scale(env, handle, new_scale)
if hasattr(self, 'cube_random_params') and actor == 'object':
randomisation_callback('scale', new_scale, actor=actor, env_id=env_id)
if hasattr(self, 'hand_random_params') and actor == 'object':
self.hand_random_params[env_id, 0] = new_scale.mean()
continue
# Get the properties from the sim API
# prop_names is dof_properties, rigid_body_properties, rigid_shape_properties
prop = param_getters_map[prop_name](env, handle)
set_random_properties = True
# if list it is likely to be
# - rigid_body_properties
# - rigid_shape_properties
if isinstance(prop, list):
# Read the original values; remember that
# randomised_prop_val = original_prop_val <operator> random sample
if self.first_randomization:
self.original_props[prop_name] = [
{attr: getattr(p, attr) for attr in dir(p)} for p in prop]
# # list to record value of attr for each body.
# recorded_attrs = {"mass": [], "friction": []}
# Loop over all the rigid bodies of the actor and then the corresponding
# attribute ranges
for attr, attr_randomization_params_cfg in prop_attrs.items():
# for curr_prop, og_p in zip(prop, self.original_props[prop_name]):
for body_idx, (p, og_p) in enumerate(zip(prop, self.original_props[prop_name])):
curr_prop = p
if self.use_adr and isinstance(attr_randomization_params_cfg['range'], dict):
# we have custom ranges for different bodies in this actor
# first: let's find out which group of bodies this body belongs to
body_group_name = None
for group_name, list_of_bodies in self.custom_body_handles[actor].items():
if body_idx in list_of_bodies:
body_group_name = group_name
break
if body_group_name is None:
raise ValueError(
f'Could not find body group for body {body_idx} in actor {actor}.\n'
f'Body groups: {self.custom_body_handles}',
)
# now: get the range for this body group
rand_range = attr_randomization_params_cfg['range'][body_group_name]
attr_randomization_params = copy.deepcopy(attr_randomization_params_cfg)
attr_randomization_params['range'] = rand_range
# we need to sore original params as ADR generated samples need to be bucketed
original_randomization_params = copy.deepcopy(dr_params['actor_params'][actor][prop_name][attr])
original_randomization_params['range'] = original_randomization_params['range'][body_group_name]
else:
attr_randomization_params = attr_randomization_params_cfg
# we need to sore original params as ADR generated samples need to be bucketed
original_randomization_params = dr_params['actor_params'][actor][prop_name][attr]
assert isinstance(attr_randomization_params['range'], (list, tuple, ListConfig)), \
f'range for {prop_name} must be a list or tuple, got {attr_randomization_params["range"]}'
# attrs:
# if rigid_body_properties, it is mass
# if rigid_shape_properties it is friction etc.
setup_only = attr_randomization_params.get('setup_only', False)
if (setup_only and not self.sim_initialized) or not setup_only:
smpl = None
if self.actor_params_generator is not None:
smpl, extern_offsets[env_id] = get_attr_val_from_sample(
extern_sample, extern_offsets[env_id], curr_prop, attr)
# generate the samples and add them to props
# e.g. curr_prop is rigid_body_properties
# attr is 'mass' (string)
# mass_val = getattr(curr_prop, 'mass')
# new_mass_val = mass_val <operator> sample
# setattr(curr_prop, 'mass', new_mass_val)
apply_random_samples(
curr_prop, og_p, attr, attr_randomization_params,
self.last_step, smpl,
bucketing_randomization_params=original_randomization_params)
# if attr in recorded_attrs:
# recorded_attrs[attr] = getattr(curr_prop, attr)
if hasattr(self, 'cube_random_params') and actor == 'object':
assert len(self.original_props[prop_name]) == 1
if attr == 'mass':
self.cube_random_params[env_id, 1] = p.mass
elif attr == 'friction':
self.cube_random_params[env_id, 2] = p.friction
else:
set_random_properties = False
# # call the callback with the list of attr values that have just been set (for each rigid body / shape in the actor)
# for attr, val_list in recorded_attrs.items():
# randomisation_callback(attr, val_list, actor=actor, env_id=env_id)
# if it is not a list, it is likely an array
# which means it is for dof_properties
else:
# prop_name is e.g. dof_properties with corresponding meta-data
if self.first_randomization:
self.original_props[prop_name] = deepcopy(prop)
# attrs is damping, stiffness etc.
# attrs_randomisation_params is range, distr, schedule
for attr, attr_randomization_params in prop_attrs.items():
setup_only = attr_randomization_params.get('setup_only', False)
if (setup_only and not self.sim_initialized) or not setup_only:
smpl = None
if self.actor_params_generator is not None:
smpl, extern_offsets[env_id] = get_attr_val_from_sample(
extern_sample, extern_offsets[env_id], prop, attr)
# we need to sore original params as ADR generated samples need to be bucketed
original_randomization_params = dr_params['actor_params'][actor][prop_name][attr]
# generate random samples and add them to props
# and we set the props back in sim later on
apply_random_samples(
prop, self.original_props[prop_name], attr,
attr_randomization_params, self.last_step, smpl,
bucketing_randomization_params=original_randomization_params)
else:
set_random_properties = False
if set_random_properties:
setter = param_setters_map[prop_name]
default_args = param_setter_defaults_map[prop_name]
setter(env, handle, prop, *default_args)
if self.actor_params_generator is not None:
for env_id in env_ids: # check that we used all dims in sample
if extern_offsets[env_id] > 0:
extern_sample = self.extern_actor_params[env_id]
if extern_offsets[env_id] != extern_sample.shape[0]:
print('env_id', env_id,
'extern_offset', extern_offsets[env_id],
'vs extern_sample.shape', extern_sample.shape)
raise Exception("Invalid extern_sample size")
self.first_randomization = False
| 60,236 | Python | 47.151079 | 204 | 0.55671 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/base/vec_task.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
import time
from datetime import datetime
from os.path import join
from typing import Dict, Any, Tuple, List, Set
import gym
from gym import spaces
from isaacgym import gymtorch, gymapi
from isaacgymenvs.utils.torch_jit_utils import to_torch
from isaacgymenvs.utils.dr_utils import get_property_setter_map, get_property_getter_map, \
get_default_setter_args, apply_random_samples, check_buckets, generate_random_samples
import torch
import numpy as np
import operator, random
from copy import deepcopy
from isaacgymenvs.utils.utils import nested_dict_get_attr, nested_dict_set_attr
from collections import deque
import sys
import abc
from abc import ABC
EXISTING_SIM = None
SCREEN_CAPTURE_RESOLUTION = (1027, 768)
def _create_sim_once(gym, *args, **kwargs):
global EXISTING_SIM
if EXISTING_SIM is not None:
return EXISTING_SIM
else:
EXISTING_SIM = gym.create_sim(*args, **kwargs)
return EXISTING_SIM
class Env(ABC):
def __init__(self, config: Dict[str, Any], rl_device: str, sim_device: str, graphics_device_id: int, headless: bool):
"""Initialise the env.
Args:
config: the configuration dictionary.
sim_device: the device to simulate physics on. eg. 'cuda:0' or 'cpu'
graphics_device_id: the device ID to render with.
headless: Set to False to disable viewer rendering.
"""
split_device = sim_device.split(":")
self.device_type = split_device[0]
self.device_id = int(split_device[1]) if len(split_device) > 1 else 0
self.device = "cpu"
if config["sim"]["use_gpu_pipeline"]:
if self.device_type.lower() == "cuda" or self.device_type.lower() == "gpu":
self.device = "cuda" + ":" + str(self.device_id)
else:
print("GPU Pipeline can only be used with GPU simulation. Forcing CPU Pipeline.")
config["sim"]["use_gpu_pipeline"] = False
self.rl_device = rl_device
# Rendering
# if training in a headless mode
self.headless = headless
enable_camera_sensors = config["env"].get("enableCameraSensors", False)
self.graphics_device_id = graphics_device_id
if enable_camera_sensors == False and self.headless == True:
self.graphics_device_id = -1
self.num_environments = config["env"]["numEnvs"]
self.num_agents = config["env"].get("numAgents", 1) # used for multi-agent environments
self.num_observations = config["env"].get("numObservations", 0)
self.num_states = config["env"].get("numStates", 0)
self.obs_space = spaces.Box(np.ones(self.num_obs) * -np.Inf, np.ones(self.num_obs) * np.Inf)
self.state_space = spaces.Box(np.ones(self.num_states) * -np.Inf, np.ones(self.num_states) * np.Inf)
self.num_actions = config["env"]["numActions"]
self.control_freq_inv = config["env"].get("controlFrequencyInv", 1)
self.act_space = spaces.Box(np.ones(self.num_actions) * -1., np.ones(self.num_actions) * 1.)
self.clip_obs = config["env"].get("clipObservations", np.Inf)
self.clip_actions = config["env"].get("clipActions", np.Inf)
# Total number of training frames since the beginning of the experiment.
# We get this information from the learning algorithm rather than tracking ourselves.
# The learning algorithm tracks the total number of frames since the beginning of training and accounts for
# experiments restart/resumes. This means this number can be > 0 right after initialization if we resume the
# experiment.
self.total_train_env_frames: int = 0
# number of control steps
self.control_steps: int = 0
self.render_fps: int = config["env"].get("renderFPS", -1)
self.last_frame_time: float = 0.0
self.record_frames: bool = False
self.record_frames_dir = join("recorded_frames", datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
@abc.abstractmethod
def allocate_buffers(self):
"""Create torch buffers for observations, rewards, actions dones and any additional data."""
@abc.abstractmethod
def step(self, actions: torch.Tensor) -> Tuple[Dict[str, torch.Tensor], torch.Tensor, torch.Tensor, Dict[str, Any]]:
"""Step the physics of the environment.
Args:
actions: actions to apply
Returns:
Observations, rewards, resets, info
Observations are dict of observations (currently only one member called 'obs')
"""
@abc.abstractmethod
def reset(self)-> Dict[str, torch.Tensor]:
"""Reset the environment.
Returns:
Observation dictionary
"""
@abc.abstractmethod
def reset_idx(self, env_ids: torch.Tensor):
"""Reset environments having the provided indices.
Args:
env_ids: environments to reset
"""
@property
def observation_space(self) -> gym.Space:
"""Get the environment's observation space."""
return self.obs_space
@property
def action_space(self) -> gym.Space:
"""Get the environment's action space."""
return self.act_space
@property
def num_envs(self) -> int:
"""Get the number of environments."""
return self.num_environments
@property
def num_acts(self) -> int:
"""Get the number of actions in the environment."""
return self.num_actions
@property
def num_obs(self) -> int:
"""Get the number of observations in the environment."""
return self.num_observations
def set_train_info(self, env_frames, *args, **kwargs):
"""
Send the information in the direction algo->environment.
Most common use case: tell the environment how far along we are in the training process. This is useful
for implementing curriculums and things such as that.
"""
self.total_train_env_frames = env_frames
# print(f'env_frames updated to {self.total_train_env_frames}')
def get_env_state(self):
"""
Return serializable environment state to be saved to checkpoint.
Can be used for stateful training sessions, i.e. with adaptive curriculums.
"""
return None
def set_env_state(self, env_state):
pass
class VecTask(Env):
metadata = {"render.modes": ["human", "rgb_array"], "video.frames_per_second": 24}
def __init__(self, config, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture: bool = False, force_render: bool = False):
"""Initialise the `VecTask`.
Args:
config: config dictionary for the environment.
sim_device: the device to simulate physics on. eg. 'cuda:0' or 'cpu'
graphics_device_id: the device ID to render with.
headless: Set to False to disable viewer rendering.
virtual_screen_capture: Set to True to allow the users get captured screen in RGB array via `env.render(mode='rgb_array')`.
force_render: Set to True to always force rendering in the steps (if the `control_freq_inv` is greater than 1 we suggest stting this arg to True)
"""
# super().__init__(config, rl_device, sim_device, graphics_device_id, headless, use_dict_obs)
super().__init__(config, rl_device, sim_device, graphics_device_id, headless)
self.virtual_screen_capture = virtual_screen_capture
self.virtual_display = None
if self.virtual_screen_capture:
from pyvirtualdisplay.smartdisplay import SmartDisplay
self.virtual_display = SmartDisplay(size=SCREEN_CAPTURE_RESOLUTION)
self.virtual_display.start()
self.force_render = force_render
self.sim_params = self.__parse_sim_params(self.cfg["physics_engine"], self.cfg["sim"])
if self.cfg["physics_engine"] == "physx":
self.physics_engine = gymapi.SIM_PHYSX
elif self.cfg["physics_engine"] == "flex":
self.physics_engine = gymapi.SIM_FLEX
else:
msg = f"Invalid physics engine backend: {self.cfg['physics_engine']}"
raise ValueError(msg)
self.dt: float = self.sim_params.dt
# optimization flags for pytorch JIT
torch._C._jit_set_profiling_mode(False)
torch._C._jit_set_profiling_executor(False)
self.gym = gymapi.acquire_gym()
self.first_randomization = True
self.original_props = {}
self.dr_randomizations = {}
self.actor_params_generator = None
self.extern_actor_params = {}
self.last_step = -1
self.last_rand_step = -1
for env_id in range(self.num_envs):
self.extern_actor_params[env_id] = None
# create envs, sim and viewer
self.sim_initialized = False
self.create_sim()
self.gym.prepare_sim(self.sim)
self.sim_initialized = True
self.set_viewer()
self.allocate_buffers()
self.obs_dict = {}
def set_viewer(self):
"""Create the viewer."""
# todo: read from config
self.enable_viewer_sync = True
self.viewer = None
# if running with a viewer, set up keyboard shortcuts and camera
if self.headless == False:
# subscribe to keyboard shortcuts
self.viewer = self.gym.create_viewer(
self.sim, gymapi.CameraProperties())
self.gym.subscribe_viewer_keyboard_event(
self.viewer, gymapi.KEY_ESCAPE, "QUIT")
self.gym.subscribe_viewer_keyboard_event(
self.viewer, gymapi.KEY_V, "toggle_viewer_sync")
self.gym.subscribe_viewer_keyboard_event(
self.viewer, gymapi.KEY_R, "record_frames")
# set the camera position based on up axis
sim_params = self.gym.get_sim_params(self.sim)
if sim_params.up_axis == gymapi.UP_AXIS_Z:
cam_pos = gymapi.Vec3(20.0, 25.0, 3.0)
cam_target = gymapi.Vec3(10.0, 15.0, 0.0)
else:
cam_pos = gymapi.Vec3(20.0, 3.0, 25.0)
cam_target = gymapi.Vec3(10.0, 0.0, 15.0)
self.gym.viewer_camera_look_at(
self.viewer, None, cam_pos, cam_target)
def allocate_buffers(self):
"""Allocate the observation, states, etc. buffers.
These are what is used to set observations and states in the environment classes which
inherit from this one, and are read in `step` and other related functions.
"""
# allocate buffers
self.obs_buf = torch.zeros(
(self.num_envs, self.num_obs), device=self.device, dtype=torch.float)
self.states_buf = torch.zeros(
(self.num_envs, self.num_states), device=self.device, dtype=torch.float)
self.rew_buf = torch.zeros(
self.num_envs, device=self.device, dtype=torch.float)
self.reset_buf = torch.ones(
self.num_envs, device=self.device, dtype=torch.long)
self.timeout_buf = torch.zeros(
self.num_envs, device=self.device, dtype=torch.long)
self.progress_buf = torch.zeros(
self.num_envs, device=self.device, dtype=torch.long)
self.randomize_buf = torch.zeros(
self.num_envs, device=self.device, dtype=torch.long)
self.extras = {}
def create_sim(self, compute_device: int, graphics_device: int, physics_engine, sim_params: gymapi.SimParams):
"""Create an Isaac Gym sim object.
Args:
compute_device: ID of compute device to use.
graphics_device: ID of graphics device to use.
physics_engine: physics engine to use (`gymapi.SIM_PHYSX` or `gymapi.SIM_FLEX`)
sim_params: sim params to use.
Returns:
the Isaac Gym sim object.
"""
sim = _create_sim_once(self.gym, compute_device, graphics_device, physics_engine, sim_params)
if sim is None:
print("*** Failed to create sim")
quit()
return sim
def get_state(self):
"""Returns the state buffer of the environment (the privileged observations for asymmetric training)."""
return torch.clamp(self.states_buf, -self.clip_obs, self.clip_obs).to(self.rl_device)
@abc.abstractmethod
def pre_physics_step(self, actions: torch.Tensor):
"""Apply the actions to the environment (eg by setting torques, position targets).
Args:
actions: the actions to apply
"""
@abc.abstractmethod
def post_physics_step(self):
"""Compute reward and observations, reset any environments that require it."""
def step(self, actions: torch.Tensor) -> Tuple[Dict[str, torch.Tensor], torch.Tensor, torch.Tensor, Dict[str, Any]]:
"""Step the physics of the environment.
Args:
actions: actions to apply
Returns:
Observations, rewards, resets, info
Observations are dict of observations (currently only one member called 'obs')
"""
# randomize actions
if self.dr_randomizations.get('actions', None):
actions = self.dr_randomizations['actions']['noise_lambda'](actions)
action_tensor = torch.clamp(actions, -self.clip_actions, self.clip_actions)
# apply actions
self.pre_physics_step(action_tensor)
# step physics and render each frame
for i in range(self.control_freq_inv):
if self.force_render:
self.render()
self.gym.simulate(self.sim)
# to fix!
if self.device == 'cpu':
self.gym.fetch_results(self.sim, True)
# compute observations, rewards, resets, ...
self.post_physics_step()
self.control_steps += 1
# fill time out buffer: set to 1 if we reached the max episode length AND the reset buffer is 1. Timeout == 1 makes sense only if the reset buffer is 1.
self.timeout_buf = (self.progress_buf >= self.max_episode_length - 1) & (self.reset_buf != 0)
# randomize observations
if self.dr_randomizations.get('observations', None):
self.obs_buf = self.dr_randomizations['observations']['noise_lambda'](self.obs_buf)
self.extras["time_outs"] = self.timeout_buf.to(self.rl_device)
self.obs_dict["obs"] = torch.clamp(self.obs_buf, -self.clip_obs, self.clip_obs).to(self.rl_device)
# asymmetric actor-critic
if self.num_states > 0:
self.obs_dict["states"] = self.get_state()
return self.obs_dict, self.rew_buf.to(self.rl_device), self.reset_buf.to(self.rl_device), self.extras
def zero_actions(self) -> torch.Tensor:
"""Returns a buffer with zero actions.
Returns:
A buffer of zero torch actions
"""
actions = torch.zeros([self.num_envs, self.num_actions], dtype=torch.float32, device=self.rl_device)
return actions
def reset_idx(self, env_idx):
"""Reset environment with indces in env_idx.
Should be implemented in an environment class inherited from VecTask.
"""
pass
def reset(self):
"""Is called only once when environment starts to provide the first observations.
Doesn't calculate observations. Actual reset and observation calculation need to be implemented by user.
Returns:
Observation dictionary
"""
self.obs_dict["obs"] = torch.clamp(self.obs_buf, -self.clip_obs, self.clip_obs).to(self.rl_device)
# asymmetric actor-critic
if self.num_states > 0:
self.obs_dict["states"] = self.get_state()
return self.obs_dict
def reset_done(self):
"""Reset the environment.
Returns:
Observation dictionary, indices of environments being reset
"""
done_env_ids = self.reset_buf.nonzero(as_tuple=False).flatten()
if len(done_env_ids) > 0:
self.reset_idx(done_env_ids)
self.obs_dict["obs"] = torch.clamp(self.obs_buf, -self.clip_obs, self.clip_obs).to(self.rl_device)
# asymmetric actor-critic
if self.num_states > 0:
self.obs_dict["states"] = self.get_state()
return self.obs_dict, done_env_ids
def render(self, mode="rgb_array"):
"""Draw the frame to the viewer, and check for keyboard events."""
if self.viewer:
# check for window closed
if self.gym.query_viewer_has_closed(self.viewer):
sys.exit()
# check for keyboard events
for evt in self.gym.query_viewer_action_events(self.viewer):
if evt.action == "QUIT" and evt.value > 0:
sys.exit()
elif evt.action == "toggle_viewer_sync" and evt.value > 0:
self.enable_viewer_sync = not self.enable_viewer_sync
elif evt.action == "record_frames" and evt.value > 0:
self.record_frames = not self.record_frames
# fetch results
if self.device != 'cpu':
self.gym.fetch_results(self.sim, True)
# step graphics
if self.enable_viewer_sync:
self.gym.step_graphics(self.sim)
self.gym.draw_viewer(self.viewer, self.sim, True)
# Wait for dt to elapse in real time.
# This synchronizes the physics simulation with the rendering rate.
self.gym.sync_frame_time(self.sim)
# it seems like in some cases sync_frame_time still results in higher-than-realtime framerate
# this code will slow down the rendering to real time
now = time.time()
delta = now - self.last_frame_time
if self.render_fps < 0:
# render at control frequency
render_dt = self.dt * self.control_freq_inv # render every control step
else:
render_dt = 1.0 / self.render_fps
if delta < render_dt:
time.sleep(render_dt - delta)
self.last_frame_time = time.time()
else:
self.gym.poll_viewer_events(self.viewer)
if self.record_frames:
if not os.path.isdir(self.record_frames_dir):
os.makedirs(self.record_frames_dir, exist_ok=True)
self.gym.write_viewer_image_to_file(self.viewer, join(self.record_frames_dir, f"frame_{self.control_steps}.png"))
if self.virtual_display and mode == "rgb_array":
img = self.virtual_display.grab()
return np.array(img)
def __parse_sim_params(self, physics_engine: str, config_sim: Dict[str, Any]) -> gymapi.SimParams:
"""Parse the config dictionary for physics stepping settings.
Args:
physics_engine: which physics engine to use. "physx" or "flex"
config_sim: dict of sim configuration parameters
Returns
IsaacGym SimParams object with updated settings.
"""
sim_params = gymapi.SimParams()
# check correct up-axis
if config_sim["up_axis"] not in ["z", "y"]:
msg = f"Invalid physics up-axis: {config_sim['up_axis']}"
print(msg)
raise ValueError(msg)
# assign general sim parameters
sim_params.dt = config_sim["dt"]
sim_params.num_client_threads = config_sim.get("num_client_threads", 0)
sim_params.use_gpu_pipeline = config_sim["use_gpu_pipeline"]
sim_params.substeps = config_sim.get("substeps", 2)
# assign up-axis
if config_sim["up_axis"] == "z":
sim_params.up_axis = gymapi.UP_AXIS_Z
else:
sim_params.up_axis = gymapi.UP_AXIS_Y
# assign gravity
sim_params.gravity = gymapi.Vec3(*config_sim["gravity"])
# configure physics parameters
if physics_engine == "physx":
# set the parameters
if "physx" in config_sim:
for opt in config_sim["physx"].keys():
if opt == "contact_collection":
setattr(sim_params.physx, opt, gymapi.ContactCollection(config_sim["physx"][opt]))
else:
setattr(sim_params.physx, opt, config_sim["physx"][opt])
else:
# set the parameters
if "flex" in config_sim:
for opt in config_sim["flex"].keys():
setattr(sim_params.flex, opt, config_sim["flex"][opt])
# return the configured params
return sim_params
"""
Domain Randomization methods
"""
def get_actor_params_info(self, dr_params: Dict[str, Any], env):
"""Generate a flat array of actor params, their names and ranges.
Returns:
The array
"""
if "actor_params" not in dr_params:
return None
params = []
names = []
lows = []
highs = []
param_getters_map = get_property_getter_map(self.gym)
for actor, actor_properties in dr_params["actor_params"].items():
handle = self.gym.find_actor_handle(env, actor)
for prop_name, prop_attrs in actor_properties.items():
if prop_name == 'color':
continue # this is set randomly
props = param_getters_map[prop_name](env, handle)
if not isinstance(props, list):
props = [props]
for prop_idx, prop in enumerate(props):
for attr, attr_randomization_params in prop_attrs.items():
name = prop_name+'_' + str(prop_idx) + '_'+attr
lo_hi = attr_randomization_params['range']
distr = attr_randomization_params['distribution']
if 'uniform' not in distr:
lo_hi = (-1.0*float('Inf'), float('Inf'))
if isinstance(prop, np.ndarray):
for attr_idx in range(prop[attr].shape[0]):
params.append(prop[attr][attr_idx])
names.append(name+'_'+str(attr_idx))
lows.append(lo_hi[0])
highs.append(lo_hi[1])
else:
params.append(getattr(prop, attr))
names.append(name)
lows.append(lo_hi[0])
highs.append(lo_hi[1])
return params, names, lows, highs
def apply_randomizations(self, dr_params):
"""Apply domain randomizations to the environment.
Note that currently we can only apply randomizations only on resets, due to current PhysX limitations
Args:
dr_params: parameters for domain randomization to use.
"""
# If we don't have a randomization frequency, randomize every step
rand_freq = dr_params.get("frequency", 1)
# First, determine what to randomize:
# - non-environment parameters when > frequency steps have passed since the last non-environment
# - physical environments in the reset buffer, which have exceeded the randomization frequency threshold
# - on the first call, randomize everything
self.last_step = self.gym.get_frame_count(self.sim)
if self.first_randomization:
do_nonenv_randomize = True
env_ids = list(range(self.num_envs))
else:
do_nonenv_randomize = (self.last_step - self.last_rand_step) >= rand_freq
rand_envs = torch.where(self.randomize_buf >= rand_freq, torch.ones_like(self.randomize_buf), torch.zeros_like(self.randomize_buf))
rand_envs = torch.logical_and(rand_envs, self.reset_buf)
env_ids = torch.nonzero(rand_envs, as_tuple=False).squeeze(-1).tolist()
self.randomize_buf[rand_envs] = 0
if do_nonenv_randomize:
self.last_rand_step = self.last_step
param_setters_map = get_property_setter_map(self.gym)
param_setter_defaults_map = get_default_setter_args(self.gym)
param_getters_map = get_property_getter_map(self.gym)
# On first iteration, check the number of buckets
if self.first_randomization:
check_buckets(self.gym, self.envs, dr_params)
for nonphysical_param in ["observations", "actions"]:
if nonphysical_param in dr_params and do_nonenv_randomize:
dist = dr_params[nonphysical_param]["distribution"]
op_type = dr_params[nonphysical_param]["operation"]
sched_type = dr_params[nonphysical_param]["schedule"] if "schedule" in dr_params[nonphysical_param] else None
sched_step = dr_params[nonphysical_param]["schedule_steps"] if "schedule" in dr_params[nonphysical_param] else None
op = operator.add if op_type == 'additive' else operator.mul
if sched_type == 'linear':
sched_scaling = 1.0 / sched_step * \
min(self.last_step, sched_step)
elif sched_type == 'constant':
sched_scaling = 0 if self.last_step < sched_step else 1
else:
sched_scaling = 1
if dist == 'gaussian':
mu, var = dr_params[nonphysical_param]["range"]
mu_corr, var_corr = dr_params[nonphysical_param].get("range_correlated", [0., 0.])
if op_type == 'additive':
mu *= sched_scaling
var *= sched_scaling
mu_corr *= sched_scaling
var_corr *= sched_scaling
elif op_type == 'scaling':
var = var * sched_scaling # scale up var over time
mu = mu * sched_scaling + 1.0 * \
(1.0 - sched_scaling) # linearly interpolate
var_corr = var_corr * sched_scaling # scale up var over time
mu_corr = mu_corr * sched_scaling + 1.0 * \
(1.0 - sched_scaling) # linearly interpolate
def noise_lambda(tensor, param_name=nonphysical_param):
params = self.dr_randomizations[param_name]
corr = params.get('corr', None)
if corr is None:
corr = torch.randn_like(tensor)
params['corr'] = corr
corr = corr * params['var_corr'] + params['mu_corr']
return op(
tensor, corr + torch.randn_like(tensor) * params['var'] + params['mu'])
self.dr_randomizations[nonphysical_param] = {'mu': mu, 'var': var, 'mu_corr': mu_corr, 'var_corr': var_corr, 'noise_lambda': noise_lambda}
elif dist == 'uniform':
lo, hi = dr_params[nonphysical_param]["range"]
lo_corr, hi_corr = dr_params[nonphysical_param].get("range_correlated", [0., 0.])
if op_type == 'additive':
lo *= sched_scaling
hi *= sched_scaling
lo_corr *= sched_scaling
hi_corr *= sched_scaling
elif op_type == 'scaling':
lo = lo * sched_scaling + 1.0 * (1.0 - sched_scaling)
hi = hi * sched_scaling + 1.0 * (1.0 - sched_scaling)
lo_corr = lo_corr * sched_scaling + 1.0 * (1.0 - sched_scaling)
hi_corr = hi_corr * sched_scaling + 1.0 * (1.0 - sched_scaling)
def noise_lambda(tensor, param_name=nonphysical_param):
params = self.dr_randomizations[param_name]
corr = params.get('corr', None)
if corr is None:
corr = torch.randn_like(tensor)
params['corr'] = corr
corr = corr * (params['hi_corr'] - params['lo_corr']) + params['lo_corr']
return op(tensor, corr + torch.rand_like(tensor) * (params['hi'] - params['lo']) + params['lo'])
self.dr_randomizations[nonphysical_param] = {'lo': lo, 'hi': hi, 'lo_corr': lo_corr, 'hi_corr': hi_corr, 'noise_lambda': noise_lambda}
if "sim_params" in dr_params and do_nonenv_randomize:
prop_attrs = dr_params["sim_params"]
prop = self.gym.get_sim_params(self.sim)
if self.first_randomization:
self.original_props["sim_params"] = {
attr: getattr(prop, attr) for attr in dir(prop)}
for attr, attr_randomization_params in prop_attrs.items():
apply_random_samples(
prop, self.original_props["sim_params"], attr, attr_randomization_params, self.last_step)
self.gym.set_sim_params(self.sim, prop)
# If self.actor_params_generator is initialized: use it to
# sample actor simulation params. This gives users the
# freedom to generate samples from arbitrary distributions,
# e.g. use full-covariance distributions instead of the DR's
# default of treating each simulation parameter independently.
extern_offsets = {}
if self.actor_params_generator is not None:
for env_id in env_ids:
self.extern_actor_params[env_id] = \
self.actor_params_generator.sample()
extern_offsets[env_id] = 0
# randomise all attributes of each actor (hand, cube etc..)
# actor_properties are (stiffness, damping etc..)
# Loop over actors, then loop over envs, then loop over their props
# and lastly loop over the ranges of the params
for actor, actor_properties in dr_params["actor_params"].items():
# Loop over all envs as this part is not tensorised yet
for env_id in env_ids:
env = self.envs[env_id]
handle = self.gym.find_actor_handle(env, actor)
extern_sample = self.extern_actor_params[env_id]
# randomise dof_props, rigid_body, rigid_shape properties
# all obtained from the YAML file
# EXAMPLE: prop name: dof_properties, rigid_body_properties, rigid_shape properties
# prop_attrs:
# {'damping': {'range': [0.3, 3.0], 'operation': 'scaling', 'distribution': 'loguniform'}
# {'stiffness': {'range': [0.75, 1.5], 'operation': 'scaling', 'distribution': 'loguniform'}
for prop_name, prop_attrs in actor_properties.items():
if prop_name == 'color':
num_bodies = self.gym.get_actor_rigid_body_count(
env, handle)
for n in range(num_bodies):
self.gym.set_rigid_body_color(env, handle, n, gymapi.MESH_VISUAL,
gymapi.Vec3(random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1)))
continue
if prop_name == 'scale':
setup_only = prop_attrs.get('setup_only', False)
if (setup_only and not self.sim_initialized) or not setup_only:
attr_randomization_params = prop_attrs
sample = generate_random_samples(attr_randomization_params, 1,
self.last_step, None)
og_scale = 1
if attr_randomization_params['operation'] == 'scaling':
new_scale = og_scale * sample
elif attr_randomization_params['operation'] == 'additive':
new_scale = og_scale + sample
self.gym.set_actor_scale(env, handle, new_scale)
continue
prop = param_getters_map[prop_name](env, handle)
set_random_properties = True
if isinstance(prop, list):
if self.first_randomization:
self.original_props[prop_name] = [
{attr: getattr(p, attr) for attr in dir(p)} for p in prop]
for p, og_p in zip(prop, self.original_props[prop_name]):
for attr, attr_randomization_params in prop_attrs.items():
setup_only = attr_randomization_params.get('setup_only', False)
if (setup_only and not self.sim_initialized) or not setup_only:
smpl = None
if self.actor_params_generator is not None:
smpl, extern_offsets[env_id] = get_attr_val_from_sample(
extern_sample, extern_offsets[env_id], p, attr)
apply_random_samples(
p, og_p, attr, attr_randomization_params,
self.last_step, smpl)
else:
set_random_properties = False
else:
if self.first_randomization:
self.original_props[prop_name] = deepcopy(prop)
for attr, attr_randomization_params in prop_attrs.items():
setup_only = attr_randomization_params.get('setup_only', False)
if (setup_only and not self.sim_initialized) or not setup_only:
smpl = None
if self.actor_params_generator is not None:
smpl, extern_offsets[env_id] = get_attr_val_from_sample(
extern_sample, extern_offsets[env_id], prop, attr)
apply_random_samples(
prop, self.original_props[prop_name], attr,
attr_randomization_params, self.last_step, smpl)
else:
set_random_properties = False
if set_random_properties:
setter = param_setters_map[prop_name]
default_args = param_setter_defaults_map[prop_name]
setter(env, handle, prop, *default_args)
if self.actor_params_generator is not None:
for env_id in env_ids: # check that we used all dims in sample
if extern_offsets[env_id] > 0:
extern_sample = self.extern_actor_params[env_id]
if extern_offsets[env_id] != extern_sample.shape[0]:
print('env_id', env_id,
'extern_offset', extern_offsets[env_id],
'vs extern_sample.shape', extern_sample.shape)
raise Exception("Invalid extern_sample size")
self.first_randomization = False | 37,452 | Python | 43.586905 | 160 | 0.569476 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/base/__init__.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
| 1,558 | Python | 54.678569 | 80 | 0.784339 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_base.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: base class.
Inherits Gym's VecTask class and abstract base class. Inherited by environment classes. Not directly executed.
Configuration defined in FactoryBase.yaml. Asset info defined in factory_asset_info_franka_table.yaml.
"""
import hydra
import math
import numpy as np
import os
import sys
import torch
from gym import logger
from isaacgym import gymapi, gymtorch
from isaacgymenvs.utils import torch_jit_utils as torch_utils
from isaacgymenvs.tasks.base.vec_task import VecTask
import isaacgymenvs.tasks.factory.factory_control as fc
from isaacgymenvs.tasks.factory.factory_schema_class_base import FactoryABCBase
from isaacgymenvs.tasks.factory.factory_schema_config_base import FactorySchemaConfigBase
class FactoryBase(VecTask, FactoryABCBase):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
"""Initialize instance variables. Initialize VecTask superclass."""
self.cfg = cfg
self.cfg['headless'] = headless
self._get_base_yaml_params()
if self.cfg_base.mode.export_scene:
sim_device = 'cpu'
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render) # create_sim() is called here
def _get_base_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name='factory_schema_config_base', node=FactorySchemaConfigBase)
config_path = 'task/FactoryBase.yaml' # relative to Gym's Hydra search path (cfg dir)
self.cfg_base = hydra.compose(config_name=config_path)
self.cfg_base = self.cfg_base['task'] # strip superfluous nesting
asset_info_path = '../../assets/factory/yaml/factory_asset_info_franka_table.yaml' # relative to Gym's Hydra search path (cfg dir)
self.asset_info_franka_table = hydra.compose(config_name=asset_info_path)
self.asset_info_franka_table = self.asset_info_franka_table['']['']['']['']['']['']['assets']['factory']['yaml'] # strip superfluous nesting
def create_sim(self):
"""Set sim and PhysX params. Create sim object, ground plane, and envs."""
if self.cfg_base.mode.export_scene:
self.sim_params.use_gpu_pipeline = False
self.sim = super().create_sim(compute_device=self.device_id,
graphics_device=self.graphics_device_id,
physics_engine=self.physics_engine,
sim_params=self.sim_params)
self._create_ground_plane()
self.create_envs() # defined in subclass
def _create_ground_plane(self):
"""Set ground plane params. Add plane."""
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
plane_params.distance = 0.0 # default = 0.0
plane_params.static_friction = 1.0 # default = 1.0
plane_params.dynamic_friction = 1.0 # default = 1.0
plane_params.restitution = 0.0 # default = 0.0
self.gym.add_ground(self.sim, plane_params)
def import_franka_assets(self):
"""Set Franka and table asset options. Import assets."""
urdf_root = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'assets', 'factory', 'urdf')
franka_file = 'factory_franka.urdf'
franka_options = gymapi.AssetOptions()
franka_options.flip_visual_attachments = True
franka_options.fix_base_link = True
franka_options.collapse_fixed_joints = False
franka_options.thickness = 0.0 # default = 0.02
franka_options.density = 1000.0 # default = 1000.0
franka_options.armature = 0.01 # default = 0.0
franka_options.use_physx_armature = True
if self.cfg_base.sim.add_damping:
franka_options.linear_damping = 1.0 # default = 0.0; increased to improve stability
franka_options.max_linear_velocity = 1.0 # default = 1000.0; reduced to prevent CUDA errors
franka_options.angular_damping = 5.0 # default = 0.5; increased to improve stability
franka_options.max_angular_velocity = 2 * math.pi # default = 64.0; reduced to prevent CUDA errors
else:
franka_options.linear_damping = 0.0 # default = 0.0
franka_options.max_linear_velocity = 1000.0 # default = 1000.0
franka_options.angular_damping = 0.5 # default = 0.5
franka_options.max_angular_velocity = 64.0 # default = 64.0
franka_options.disable_gravity = True
franka_options.enable_gyroscopic_forces = True
franka_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
franka_options.use_mesh_materials = True
if self.cfg_base.mode.export_scene:
franka_options.mesh_normal_mode = gymapi.COMPUTE_PER_FACE
table_options = gymapi.AssetOptions()
table_options.flip_visual_attachments = False # default = False
table_options.fix_base_link = True
table_options.thickness = 0.0 # default = 0.02
table_options.density = 1000.0 # default = 1000.0
table_options.armature = 0.0 # default = 0.0
table_options.use_physx_armature = True
table_options.linear_damping = 0.0 # default = 0.0
table_options.max_linear_velocity = 1000.0 # default = 1000.0
table_options.angular_damping = 0.0 # default = 0.5
table_options.max_angular_velocity = 64.0 # default = 64.0
table_options.disable_gravity = False
table_options.enable_gyroscopic_forces = True
table_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
table_options.use_mesh_materials = False
if self.cfg_base.mode.export_scene:
table_options.mesh_normal_mode = gymapi.COMPUTE_PER_FACE
franka_asset = self.gym.load_asset(self.sim, urdf_root, franka_file, franka_options)
table_asset = self.gym.create_box(self.sim, self.asset_info_franka_table.table_depth,
self.asset_info_franka_table.table_width, self.cfg_base.env.table_height,
table_options)
return franka_asset, table_asset
def acquire_base_tensors(self):
"""Acquire and wrap tensors. Create views."""
_root_state = self.gym.acquire_actor_root_state_tensor(self.sim) # shape = (num_envs * num_actors, 13)
_body_state = self.gym.acquire_rigid_body_state_tensor(self.sim) # shape = (num_envs * num_bodies, 13)
_dof_state = self.gym.acquire_dof_state_tensor(self.sim) # shape = (num_envs * num_dofs, 2)
_dof_force = self.gym.acquire_dof_force_tensor(self.sim) # shape = (num_envs * num_dofs, 1)
_contact_force = self.gym.acquire_net_contact_force_tensor(self.sim) # shape = (num_envs * num_bodies, 3)
_jacobian = self.gym.acquire_jacobian_tensor(self.sim, 'franka') # shape = (num envs, num_bodies, 6, num_dofs)
_mass_matrix = self.gym.acquire_mass_matrix_tensor(self.sim, 'franka') # shape = (num_envs, num_dofs, num_dofs)
self.root_state = gymtorch.wrap_tensor(_root_state)
self.body_state = gymtorch.wrap_tensor(_body_state)
self.dof_state = gymtorch.wrap_tensor(_dof_state)
self.dof_force = gymtorch.wrap_tensor(_dof_force)
self.contact_force = gymtorch.wrap_tensor(_contact_force)
self.jacobian = gymtorch.wrap_tensor(_jacobian)
self.mass_matrix = gymtorch.wrap_tensor(_mass_matrix)
self.root_pos = self.root_state.view(self.num_envs, self.num_actors, 13)[..., 0:3]
self.root_quat = self.root_state.view(self.num_envs, self.num_actors, 13)[..., 3:7]
self.root_linvel = self.root_state.view(self.num_envs, self.num_actors, 13)[..., 7:10]
self.root_angvel = self.root_state.view(self.num_envs, self.num_actors, 13)[..., 10:13]
self.body_pos = self.body_state.view(self.num_envs, self.num_bodies, 13)[..., 0:3]
self.body_quat = self.body_state.view(self.num_envs, self.num_bodies, 13)[..., 3:7]
self.body_linvel = self.body_state.view(self.num_envs, self.num_bodies, 13)[..., 7:10]
self.body_angvel = self.body_state.view(self.num_envs, self.num_bodies, 13)[..., 10:13]
self.dof_pos = self.dof_state.view(self.num_envs, self.num_dofs, 2)[..., 0]
self.dof_vel = self.dof_state.view(self.num_envs, self.num_dofs, 2)[..., 1]
self.dof_force_view = self.dof_force.view(self.num_envs, self.num_dofs, 1)[..., 0]
self.contact_force = self.contact_force.view(self.num_envs, self.num_bodies, 3)[..., 0:3]
self.arm_dof_pos = self.dof_pos[:, 0:7]
self.arm_mass_matrix = self.mass_matrix[:, 0:7, 0:7] # for Franka arm (not gripper)
self.hand_pos = self.body_pos[:, self.hand_body_id_env, 0:3]
self.hand_quat = self.body_quat[:, self.hand_body_id_env, 0:4]
self.hand_linvel = self.body_linvel[:, self.hand_body_id_env, 0:3]
self.hand_angvel = self.body_angvel[:, self.hand_body_id_env, 0:3]
self.hand_jacobian = self.jacobian[:, self.hand_body_id_env - 1, 0:6, 0:7] # minus 1 because base is fixed
self.left_finger_pos = self.body_pos[:, self.left_finger_body_id_env, 0:3]
self.left_finger_quat = self.body_quat[:, self.left_finger_body_id_env, 0:4]
self.left_finger_linvel = self.body_linvel[:, self.left_finger_body_id_env, 0:3]
self.left_finger_angvel = self.body_angvel[:, self.left_finger_body_id_env, 0:3]
self.left_finger_jacobian = self.jacobian[:, self.left_finger_body_id_env - 1, 0:6, 0:7] # minus 1 because base is fixed
self.right_finger_pos = self.body_pos[:, self.right_finger_body_id_env, 0:3]
self.right_finger_quat = self.body_quat[:, self.right_finger_body_id_env, 0:4]
self.right_finger_linvel = self.body_linvel[:, self.right_finger_body_id_env, 0:3]
self.right_finger_angvel = self.body_angvel[:, self.right_finger_body_id_env, 0:3]
self.right_finger_jacobian = self.jacobian[:, self.right_finger_body_id_env - 1, 0:6, 0:7] # minus 1 because base is fixed
self.left_finger_force = self.contact_force[:, self.left_finger_body_id_env, 0:3]
self.right_finger_force = self.contact_force[:, self.right_finger_body_id_env, 0:3]
self.gripper_dof_pos = self.dof_pos[:, 7:9]
self.fingertip_centered_pos = self.body_pos[:, self.fingertip_centered_body_id_env, 0:3]
self.fingertip_centered_quat = self.body_quat[:, self.fingertip_centered_body_id_env, 0:4]
self.fingertip_centered_linvel = self.body_linvel[:, self.fingertip_centered_body_id_env, 0:3]
self.fingertip_centered_angvel = self.body_angvel[:, self.fingertip_centered_body_id_env, 0:3]
self.fingertip_centered_jacobian = self.jacobian[:, self.fingertip_centered_body_id_env - 1, 0:6, 0:7] # minus 1 because base is fixed
self.fingertip_midpoint_pos = self.fingertip_centered_pos.detach().clone() # initial value
self.fingertip_midpoint_quat = self.fingertip_centered_quat # always equal
self.fingertip_midpoint_linvel = self.fingertip_centered_linvel.detach().clone() # initial value
# From sum of angular velocities (https://physics.stackexchange.com/questions/547698/understanding-addition-of-angular-velocity),
# angular velocity of midpoint w.r.t. world is equal to sum of
# angular velocity of midpoint w.r.t. hand and angular velocity of hand w.r.t. world.
# Midpoint is in sliding contact (i.e., linear relative motion) with hand; angular velocity of midpoint w.r.t. hand is zero.
# Thus, angular velocity of midpoint w.r.t. world is equal to angular velocity of hand w.r.t. world.
self.fingertip_midpoint_angvel = self.fingertip_centered_angvel # always equal
self.fingertip_midpoint_jacobian = (self.left_finger_jacobian + self.right_finger_jacobian) * 0.5 # approximation
self.dof_torque = torch.zeros((self.num_envs, self.num_dofs), device=self.device)
self.fingertip_contact_wrench = torch.zeros((self.num_envs, 6), device=self.device)
self.ctrl_target_fingertip_midpoint_pos = torch.zeros((self.num_envs, 3), device=self.device)
self.ctrl_target_fingertip_midpoint_quat = torch.zeros((self.num_envs, 4), device=self.device)
self.ctrl_target_dof_pos = torch.zeros((self.num_envs, self.num_dofs), device=self.device)
self.ctrl_target_gripper_dof_pos = torch.zeros((self.num_envs, 2), device=self.device)
self.ctrl_target_fingertip_contact_wrench = torch.zeros((self.num_envs, 6), device=self.device)
self.prev_actions = torch.zeros((self.num_envs, self.num_actions), device=self.device)
def refresh_base_tensors(self):
"""Refresh tensors."""
# NOTE: Tensor refresh functions should be called once per step, before setters.
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
self.gym.refresh_dof_force_tensor(self.sim)
self.gym.refresh_net_contact_force_tensor(self.sim)
self.gym.refresh_jacobian_tensors(self.sim)
self.gym.refresh_mass_matrix_tensors(self.sim)
self.finger_midpoint_pos = (self.left_finger_pos + self.right_finger_pos) * 0.5
self.fingertip_midpoint_pos = fc.translate_along_local_z(pos=self.finger_midpoint_pos,
quat=self.hand_quat,
offset=self.asset_info_franka_table.franka_finger_length,
device=self.device)
# TODO: Add relative velocity term (see https://dynamicsmotioncontrol487379916.files.wordpress.com/2020/11/21-me258pointmovingrigidbody.pdf)
self.fingertip_midpoint_linvel = self.fingertip_centered_linvel + torch.cross(self.fingertip_centered_angvel,
(self.fingertip_midpoint_pos - self.fingertip_centered_pos),
dim=1)
self.fingertip_midpoint_jacobian = (self.left_finger_jacobian + self.right_finger_jacobian) * 0.5 # approximation
def parse_controller_spec(self):
"""Parse controller specification into lower-level controller configuration."""
cfg_ctrl_keys = {'num_envs',
'jacobian_type',
'gripper_prop_gains',
'gripper_deriv_gains',
'motor_ctrl_mode',
'gain_space',
'ik_method',
'joint_prop_gains',
'joint_deriv_gains',
'do_motion_ctrl',
'task_prop_gains',
'task_deriv_gains',
'do_inertial_comp',
'motion_ctrl_axes',
'do_force_ctrl',
'force_ctrl_method',
'wrench_prop_gains',
'force_ctrl_axes'}
self.cfg_ctrl = {cfg_ctrl_key: None for cfg_ctrl_key in cfg_ctrl_keys}
self.cfg_ctrl['num_envs'] = self.num_envs
self.cfg_ctrl['jacobian_type'] = self.cfg_task.ctrl.all.jacobian_type
self.cfg_ctrl['gripper_prop_gains'] = torch.tensor(self.cfg_task.ctrl.all.gripper_prop_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['gripper_deriv_gains'] = torch.tensor(self.cfg_task.ctrl.all.gripper_deriv_gains,
device=self.device).repeat((self.num_envs, 1))
ctrl_type = self.cfg_task.ctrl.ctrl_type
if ctrl_type == 'gym_default':
self.cfg_ctrl['motor_ctrl_mode'] = 'gym'
self.cfg_ctrl['gain_space'] = 'joint'
self.cfg_ctrl['ik_method'] = self.cfg_task.ctrl.gym_default.ik_method
self.cfg_ctrl['joint_prop_gains'] = torch.tensor(self.cfg_task.ctrl.gym_default.joint_prop_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['joint_deriv_gains'] = torch.tensor(self.cfg_task.ctrl.gym_default.joint_deriv_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['gripper_prop_gains'] = torch.tensor(self.cfg_task.ctrl.gym_default.gripper_prop_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['gripper_deriv_gains'] = torch.tensor(self.cfg_task.ctrl.gym_default.gripper_deriv_gains,
device=self.device).repeat((self.num_envs, 1))
elif ctrl_type == 'joint_space_ik':
self.cfg_ctrl['motor_ctrl_mode'] = 'manual'
self.cfg_ctrl['gain_space'] = 'joint'
self.cfg_ctrl['ik_method'] = self.cfg_task.ctrl.joint_space_ik.ik_method
self.cfg_ctrl['joint_prop_gains'] = torch.tensor(self.cfg_task.ctrl.joint_space_ik.joint_prop_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['joint_deriv_gains'] = torch.tensor(self.cfg_task.ctrl.joint_space_ik.joint_deriv_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['do_inertial_comp'] = False
elif ctrl_type == 'joint_space_id':
self.cfg_ctrl['motor_ctrl_mode'] = 'manual'
self.cfg_ctrl['gain_space'] = 'joint'
self.cfg_ctrl['ik_method'] = self.cfg_task.ctrl.joint_space_id.ik_method
self.cfg_ctrl['joint_prop_gains'] = torch.tensor(self.cfg_task.ctrl.joint_space_id.joint_prop_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['joint_deriv_gains'] = torch.tensor(self.cfg_task.ctrl.joint_space_id.joint_deriv_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['do_inertial_comp'] = True
elif ctrl_type == 'task_space_impedance':
self.cfg_ctrl['motor_ctrl_mode'] = 'manual'
self.cfg_ctrl['gain_space'] = 'task'
self.cfg_ctrl['do_motion_ctrl'] = True
self.cfg_ctrl['task_prop_gains'] = torch.tensor(self.cfg_task.ctrl.task_space_impedance.task_prop_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['task_deriv_gains'] = torch.tensor(self.cfg_task.ctrl.task_space_impedance.task_deriv_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['do_inertial_comp'] = False
self.cfg_ctrl['motion_ctrl_axes'] = torch.tensor(self.cfg_task.ctrl.task_space_impedance.motion_ctrl_axes,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['do_force_ctrl'] = False
elif ctrl_type == 'operational_space_motion':
self.cfg_ctrl['motor_ctrl_mode'] = 'manual'
self.cfg_ctrl['gain_space'] = 'task'
self.cfg_ctrl['do_motion_ctrl'] = True
self.cfg_ctrl['task_prop_gains'] = torch.tensor(self.cfg_task.ctrl.operational_space_motion.task_prop_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['task_deriv_gains'] = torch.tensor(
self.cfg_task.ctrl.operational_space_motion.task_deriv_gains, device=self.device).repeat(
(self.num_envs, 1))
self.cfg_ctrl['do_inertial_comp'] = True
self.cfg_ctrl['motion_ctrl_axes'] = torch.tensor(
self.cfg_task.ctrl.operational_space_motion.motion_ctrl_axes, device=self.device).repeat(
(self.num_envs, 1))
self.cfg_ctrl['do_force_ctrl'] = False
elif ctrl_type == 'open_loop_force':
self.cfg_ctrl['motor_ctrl_mode'] = 'manual'
self.cfg_ctrl['gain_space'] = 'task'
self.cfg_ctrl['do_motion_ctrl'] = False
self.cfg_ctrl['do_force_ctrl'] = True
self.cfg_ctrl['force_ctrl_method'] = 'open'
self.cfg_ctrl['force_ctrl_axes'] = torch.tensor(self.cfg_task.ctrl.open_loop_force.force_ctrl_axes,
device=self.device).repeat((self.num_envs, 1))
elif ctrl_type == 'closed_loop_force':
self.cfg_ctrl['motor_ctrl_mode'] = 'manual'
self.cfg_ctrl['gain_space'] = 'task'
self.cfg_ctrl['do_motion_ctrl'] = False
self.cfg_ctrl['do_force_ctrl'] = True
self.cfg_ctrl['force_ctrl_method'] = 'closed'
self.cfg_ctrl['wrench_prop_gains'] = torch.tensor(self.cfg_task.ctrl.closed_loop_force.wrench_prop_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['force_ctrl_axes'] = torch.tensor(self.cfg_task.ctrl.closed_loop_force.force_ctrl_axes,
device=self.device).repeat((self.num_envs, 1))
elif ctrl_type == 'hybrid_force_motion':
self.cfg_ctrl['motor_ctrl_mode'] = 'manual'
self.cfg_ctrl['gain_space'] = 'task'
self.cfg_ctrl['do_motion_ctrl'] = True
self.cfg_ctrl['task_prop_gains'] = torch.tensor(self.cfg_task.ctrl.hybrid_force_motion.task_prop_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['task_deriv_gains'] = torch.tensor(self.cfg_task.ctrl.hybrid_force_motion.task_deriv_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['do_inertial_comp'] = True
self.cfg_ctrl['motion_ctrl_axes'] = torch.tensor(self.cfg_task.ctrl.hybrid_force_motion.motion_ctrl_axes,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['do_force_ctrl'] = True
self.cfg_ctrl['force_ctrl_method'] = 'closed'
self.cfg_ctrl['wrench_prop_gains'] = torch.tensor(self.cfg_task.ctrl.hybrid_force_motion.wrench_prop_gains,
device=self.device).repeat((self.num_envs, 1))
self.cfg_ctrl['force_ctrl_axes'] = torch.tensor(self.cfg_task.ctrl.hybrid_force_motion.force_ctrl_axes,
device=self.device).repeat((self.num_envs, 1))
if self.cfg_ctrl['motor_ctrl_mode'] == 'gym':
prop_gains = torch.cat((self.cfg_ctrl['joint_prop_gains'],
self.cfg_ctrl['gripper_prop_gains']), dim=-1).to('cpu')
deriv_gains = torch.cat((self.cfg_ctrl['joint_deriv_gains'],
self.cfg_ctrl['gripper_deriv_gains']), dim=-1).to('cpu')
# No tensor API for getting/setting actor DOF props; thus, loop required
for env_ptr, franka_handle, prop_gain, deriv_gain in zip(self.env_ptrs, self.franka_handles, prop_gains,
deriv_gains):
franka_dof_props = self.gym.get_actor_dof_properties(env_ptr, franka_handle)
franka_dof_props['driveMode'][:] = gymapi.DOF_MODE_POS
franka_dof_props['stiffness'] = prop_gain
franka_dof_props['damping'] = deriv_gain
self.gym.set_actor_dof_properties(env_ptr, franka_handle, franka_dof_props)
elif self.cfg_ctrl['motor_ctrl_mode'] == 'manual':
# No tensor API for getting/setting actor DOF props; thus, loop required
for env_ptr, franka_handle in zip(self.env_ptrs, self.franka_handles):
franka_dof_props = self.gym.get_actor_dof_properties(env_ptr, franka_handle)
franka_dof_props['driveMode'][:] = gymapi.DOF_MODE_EFFORT
franka_dof_props['stiffness'][:] = 0.0 # zero passive stiffness
franka_dof_props['damping'][:] = 0.0 # zero passive damping
self.gym.set_actor_dof_properties(env_ptr, franka_handle, franka_dof_props)
def generate_ctrl_signals(self):
"""Get Jacobian. Set Franka DOF position targets or DOF torques."""
# Get desired Jacobian
if self.cfg_ctrl['jacobian_type'] == 'geometric':
self.fingertip_midpoint_jacobian_tf = self.fingertip_midpoint_jacobian
elif self.cfg_ctrl['jacobian_type'] == 'analytic':
self.fingertip_midpoint_jacobian_tf = fc.get_analytic_jacobian(
fingertip_quat=self.fingertip_quat,
fingertip_jacobian=self.fingertip_midpoint_jacobian,
num_envs=self.num_envs,
device=self.device)
# Set PD joint pos target or joint torque
if self.cfg_ctrl['motor_ctrl_mode'] == 'gym':
self._set_dof_pos_target()
elif self.cfg_ctrl['motor_ctrl_mode'] == 'manual':
self._set_dof_torque()
def _set_dof_pos_target(self):
"""Set Franka DOF position target to move fingertips towards target pose."""
self.ctrl_target_dof_pos = fc.compute_dof_pos_target(
cfg_ctrl=self.cfg_ctrl,
arm_dof_pos=self.arm_dof_pos,
fingertip_midpoint_pos=self.fingertip_midpoint_pos,
fingertip_midpoint_quat=self.fingertip_midpoint_quat,
jacobian=self.fingertip_midpoint_jacobian_tf,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
ctrl_target_gripper_dof_pos=self.ctrl_target_gripper_dof_pos,
device=self.device)
self.gym.set_dof_position_target_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.ctrl_target_dof_pos),
gymtorch.unwrap_tensor(self.franka_actor_ids_sim),
len(self.franka_actor_ids_sim))
def _set_dof_torque(self):
"""Set Franka DOF torque to move fingertips towards target pose."""
self.dof_torque = fc.compute_dof_torque(
cfg_ctrl=self.cfg_ctrl,
dof_pos=self.dof_pos,
dof_vel=self.dof_vel,
fingertip_midpoint_pos=self.fingertip_midpoint_pos,
fingertip_midpoint_quat=self.fingertip_midpoint_quat,
fingertip_midpoint_linvel=self.fingertip_midpoint_linvel,
fingertip_midpoint_angvel=self.fingertip_midpoint_angvel,
left_finger_force=self.left_finger_force,
right_finger_force=self.right_finger_force,
jacobian=self.fingertip_midpoint_jacobian_tf,
arm_mass_matrix=self.arm_mass_matrix,
ctrl_target_gripper_dof_pos=self.ctrl_target_gripper_dof_pos,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
ctrl_target_fingertip_contact_wrench=self.ctrl_target_fingertip_contact_wrench,
device=self.device)
self.gym.set_dof_actuation_force_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_torque),
gymtorch.unwrap_tensor(self.franka_actor_ids_sim),
len(self.franka_actor_ids_sim))
def print_sdf_warning(self):
"""Generate SDF warning message."""
logger.warn('Please be patient: SDFs may be generating, which may take a few minutes. Terminating prematurely may result in a corrupted SDF cache.')
def enable_gravity(self, gravity_mag):
"""Enable gravity."""
sim_params = self.gym.get_sim_params(self.sim)
sim_params.gravity.z = -gravity_mag
self.gym.set_sim_params(self.sim, sim_params)
def disable_gravity(self):
"""Disable gravity."""
sim_params = self.gym.get_sim_params(self.sim)
sim_params.gravity.z = 0.0
self.gym.set_sim_params(self.sim, sim_params)
def export_scene(self, label):
"""Export scene to USD."""
usd_export_options = gymapi.UsdExportOptions()
usd_export_options.export_physics = False
usd_exporter = self.gym.create_usd_exporter(usd_export_options)
self.gym.export_usd_sim(usd_exporter, self.sim, label)
sys.exit()
def extract_poses(self):
"""Extract poses of all bodies."""
if not hasattr(self, 'export_pos'):
self.export_pos = []
self.export_rot = []
self.frame_count = 0
pos = self.body_pos
rot = self.body_quat
self.export_pos.append(pos.cpu().numpy().copy())
self.export_rot.append(rot.cpu().numpy().copy())
self.frame_count += 1
if len(self.export_pos) == self.max_episode_length:
output_dir = self.__class__.__name__
save_dir = os.path.join('usd', output_dir)
os.makedirs(output_dir, exist_ok=True)
print(f'Exporting poses to {output_dir}...')
np.save(os.path.join(save_dir, 'body_position.npy'), np.array(self.export_pos))
np.save(os.path.join(save_dir, 'body_rotation.npy'), np.array(self.export_rot))
print('Export completed.')
sys.exit()
| 32,041 | Python | 58.668529 | 156 | 0.601635 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_env_gears.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: class for gears env.
Inherits base class and abstract environment class. Inherited by gear task class. Not directly executed.
Configuration defined in FactoryEnvGears.yaml. Asset info defined in factory_asset_info_gears.yaml.
"""
import hydra
import numpy as np
import os
import torch
from isaacgym import gymapi
from isaacgymenvs.tasks.factory.factory_base import FactoryBase
import isaacgymenvs.tasks.factory.factory_control as fc
from isaacgymenvs.tasks.factory.factory_schema_class_env import FactoryABCEnv
from isaacgymenvs.tasks.factory.factory_schema_config_env import FactorySchemaConfigEnv
class FactoryEnvGears(FactoryBase, FactoryABCEnv):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
"""Initialize instance variables. Initialize environment superclass. Acquire tensors."""
self._get_env_yaml_params()
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
self.acquire_base_tensors() # defined in superclass
self._acquire_env_tensors()
self.refresh_base_tensors() # defined in superclass
self.refresh_env_tensors()
def _get_env_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name='factory_schema_config_env', node=FactorySchemaConfigEnv)
config_path = 'task/FactoryEnvGears.yaml' # relative to Gym's Hydra search path (cfg dir)
self.cfg_env = hydra.compose(config_name=config_path)
self.cfg_env = self.cfg_env['task'] # strip superfluous nesting
asset_info_path = '../../assets/factory/yaml/factory_asset_info_gears.yaml' # relative to Hydra search path (cfg dir)
self.asset_info_gears = hydra.compose(config_name=asset_info_path)
self.asset_info_gears = self.asset_info_gears['']['']['']['']['']['']['assets']['factory']['yaml'] # strip superfluous nesting
def create_envs(self):
"""Set env options. Import assets. Create actors."""
lower = gymapi.Vec3(-self.cfg_base.env.env_spacing, -self.cfg_base.env.env_spacing, 0.0)
upper = gymapi.Vec3(self.cfg_base.env.env_spacing, self.cfg_base.env.env_spacing, self.cfg_base.env.env_spacing)
num_per_row = int(np.sqrt(self.num_envs))
self.print_sdf_warning()
franka_asset, table_asset = self.import_franka_assets()
gear_small_asset, gear_medium_asset, gear_large_asset, base_asset = self._import_env_assets()
self._create_actors(lower, upper, num_per_row, franka_asset, gear_small_asset, gear_medium_asset,
gear_large_asset, base_asset, table_asset)
def _import_env_assets(self):
"""Set gear and base asset options. Import assets."""
urdf_root = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'assets', 'factory', 'urdf')
gear_small_file = 'factory_gear_small.urdf'
gear_medium_file = 'factory_gear_medium.urdf'
gear_large_file = 'factory_gear_large.urdf'
if self.cfg_env.env.tight_or_loose == 'tight':
base_file = 'factory_gear_base_tight.urdf'
elif self.cfg_env.env.tight_or_loose == 'loose':
base_file = 'factory_gear_base_loose.urdf'
gear_options = gymapi.AssetOptions()
gear_options.flip_visual_attachments = False
gear_options.fix_base_link = False
gear_options.thickness = 0.0 # default = 0.02
gear_options.density = self.cfg_env.env.gears_density # default = 1000.0
gear_options.armature = 0.0 # default = 0.0
gear_options.use_physx_armature = True
gear_options.linear_damping = 0.0 # default = 0.0
gear_options.max_linear_velocity = 1000.0 # default = 1000.0
gear_options.angular_damping = 0.0 # default = 0.5
gear_options.max_angular_velocity = 64.0 # default = 64.0
gear_options.disable_gravity = False
gear_options.enable_gyroscopic_forces = True
gear_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
gear_options.use_mesh_materials = False
if self.cfg_base.mode.export_scene:
gear_options.mesh_normal_mode = gymapi.COMPUTE_PER_FACE
base_options = gymapi.AssetOptions()
base_options.flip_visual_attachments = False
base_options.fix_base_link = True
base_options.thickness = 0.0 # default = 0.02
base_options.density = self.cfg_env.env.base_density # default = 1000.0
base_options.armature = 0.0 # default = 0.0
base_options.use_physx_armature = True
base_options.linear_damping = 0.0 # default = 0.0
base_options.max_linear_velocity = 1000.0 # default = 1000.0
base_options.angular_damping = 0.0 # default = 0.5
base_options.max_angular_velocity = 64.0 # default = 64.0
base_options.disable_gravity = False
base_options.enable_gyroscopic_forces = True
base_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
base_options.use_mesh_materials = False
if self.cfg_base.mode.export_scene:
base_options.mesh_normal_mode = gymapi.COMPUTE_PER_FACE
gear_small_asset = self.gym.load_asset(self.sim, urdf_root, gear_small_file, gear_options)
gear_medium_asset = self.gym.load_asset(self.sim, urdf_root, gear_medium_file, gear_options)
gear_large_asset = self.gym.load_asset(self.sim, urdf_root, gear_large_file, gear_options)
base_asset = self.gym.load_asset(self.sim, urdf_root, base_file, base_options)
return gear_small_asset, gear_medium_asset, gear_large_asset, base_asset
def _create_actors(self, lower, upper, num_per_row, franka_asset, gear_small_asset, gear_medium_asset,
gear_large_asset, base_asset, table_asset):
"""Set initial actor poses. Create actors. Set shape and DOF properties."""
franka_pose = gymapi.Transform()
franka_pose.p.x = self.cfg_base.env.franka_depth
franka_pose.p.y = 0.0
franka_pose.p.z = 0.0
franka_pose.r = gymapi.Quat(0.0, 0.0, 1.0, 0.0)
gear_pose = gymapi.Transform()
gear_pose.p.x = 0.0
gear_pose.p.y = self.cfg_env.env.gears_lateral_offset
gear_pose.p.z = self.cfg_base.env.table_height
gear_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
base_pose = gymapi.Transform()
base_pose.p.x = 0.0
base_pose.p.y = 0.0
base_pose.p.z = self.cfg_base.env.table_height
base_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
table_pose = gymapi.Transform()
table_pose.p.x = 0.0
table_pose.p.y = 0.0
table_pose.p.z = self.cfg_base.env.table_height * 0.5
table_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
self.env_ptrs = []
self.franka_handles = []
self.gear_small_handles = []
self.gear_medium_handles = []
self.gear_large_handles = []
self.base_handles = []
self.table_handles = []
self.shape_ids = []
self.franka_actor_ids_sim = [] # within-sim indices
self.gear_small_actor_ids_sim = [] # within-sim indices
self.gear_medium_actor_ids_sim = [] # within-sim indices
self.gear_large_actor_ids_sim = [] # within-sim indices
self.base_actor_ids_sim = [] # within-sim indices
self.table_actor_ids_sim = [] # within-sim indices
actor_count = 0
for i in range(self.num_envs):
env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row)
if self.cfg_env.sim.disable_franka_collisions:
franka_handle = self.gym.create_actor(env_ptr, franka_asset, franka_pose, 'franka', i + self.num_envs, 0, 0)
else:
franka_handle = self.gym.create_actor(env_ptr, franka_asset, franka_pose, 'franka', i, 0, 0)
self.franka_actor_ids_sim.append(actor_count)
actor_count += 1
gear_small_handle = self.gym.create_actor(env_ptr, gear_small_asset, gear_pose, 'gear_small', i, 0, 0)
self.gear_small_actor_ids_sim.append(actor_count)
actor_count += 1
gear_medium_handle = self.gym.create_actor(env_ptr, gear_medium_asset, gear_pose, 'gear_medium', i, 0, 0)
self.gear_medium_actor_ids_sim.append(actor_count)
actor_count += 1
gear_large_handle = self.gym.create_actor(env_ptr, gear_large_asset, gear_pose, 'gear_large', i, 0, 0)
self.gear_large_actor_ids_sim.append(actor_count)
actor_count += 1
base_handle = self.gym.create_actor(env_ptr, base_asset, base_pose, 'base', i, 0, 0)
self.base_actor_ids_sim.append(actor_count)
actor_count += 1
table_handle = self.gym.create_actor(env_ptr, table_asset, table_pose, 'table', i, 0, 0)
self.table_actor_ids_sim.append(actor_count)
actor_count += 1
link7_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_link7', gymapi.DOMAIN_ACTOR)
hand_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_hand', gymapi.DOMAIN_ACTOR)
left_finger_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_leftfinger',
gymapi.DOMAIN_ACTOR)
right_finger_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_rightfinger',
gymapi.DOMAIN_ACTOR)
self.shape_ids = [link7_id, hand_id, left_finger_id, right_finger_id]
franka_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, franka_handle)
for shape_id in self.shape_ids:
franka_shape_props[shape_id].friction = self.cfg_base.env.franka_friction
franka_shape_props[shape_id].rolling_friction = 0.0 # default = 0.0
franka_shape_props[shape_id].torsion_friction = 0.0 # default = 0.0
franka_shape_props[shape_id].restitution = 0.0 # default = 0.0
franka_shape_props[shape_id].compliance = 0.0 # default = 0.0
franka_shape_props[shape_id].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, franka_handle, franka_shape_props)
gear_small_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, gear_small_handle)
gear_small_shape_props[0].friction = self.cfg_env.env.gears_friction
gear_small_shape_props[0].rolling_friction = 0.0 # default = 0.0
gear_small_shape_props[0].torsion_friction = 0.0 # default = 0.0
gear_small_shape_props[0].restitution = 0.0 # default = 0.0
gear_small_shape_props[0].compliance = 0.0 # default = 0.0
gear_small_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, gear_small_handle, gear_small_shape_props)
gear_medium_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, gear_medium_handle)
gear_medium_shape_props[0].friction = self.cfg_env.env.gears_friction
gear_medium_shape_props[0].rolling_friction = 0.0 # default = 0.0
gear_medium_shape_props[0].torsion_friction = 0.0 # default = 0.0
gear_medium_shape_props[0].restitution = 0.0 # default = 0.0
gear_medium_shape_props[0].compliance = 0.0 # default = 0.0
gear_medium_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, gear_medium_handle, gear_medium_shape_props)
gear_large_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, gear_large_handle)
gear_large_shape_props[0].friction = self.cfg_env.env.gears_friction
gear_large_shape_props[0].rolling_friction = 0.0 # default = 0.0
gear_large_shape_props[0].torsion_friction = 0.0 # default = 0.0
gear_large_shape_props[0].restitution = 0.0 # default = 0.0
gear_large_shape_props[0].compliance = 0.0 # default = 0.0
gear_large_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, gear_large_handle, gear_large_shape_props)
base_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, base_handle)
base_shape_props[0].friction = self.cfg_env.env.base_friction
base_shape_props[0].rolling_friction = 0.0 # default = 0.0
base_shape_props[0].torsion_friction = 0.0 # default = 0.0
base_shape_props[0].restitution = 0.0 # default = 0.0
base_shape_props[0].compliance = 0.0 # default = 0.0
base_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, base_handle, base_shape_props)
table_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, table_handle)
table_shape_props[0].friction = self.cfg_base.env.table_friction
table_shape_props[0].rolling_friction = 0.0 # default = 0.0
table_shape_props[0].torsion_friction = 0.0 # default = 0.0
table_shape_props[0].restitution = 0.0 # default = 0.0
table_shape_props[0].compliance = 0.0 # default = 0.0
table_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, table_handle, table_shape_props)
self.franka_num_dofs = self.gym.get_actor_dof_count(env_ptr, franka_handle)
self.gym.enable_actor_dof_force_sensors(env_ptr, franka_handle)
self.env_ptrs.append(env_ptr)
self.franka_handles.append(franka_handle)
self.gear_small_handles.append(gear_small_handle)
self.gear_medium_handles.append(gear_medium_handle)
self.gear_large_handles.append(gear_large_handle)
self.base_handles.append(base_handle)
self.table_handles.append(table_handle)
self.num_actors = int(actor_count / self.num_envs) # per env
self.num_bodies = self.gym.get_env_rigid_body_count(env_ptr) # per env
self.num_dofs = self.gym.get_env_dof_count(env_ptr) # per env
# For setting targets
self.franka_actor_ids_sim = torch.tensor(self.franka_actor_ids_sim, dtype=torch.int32, device=self.device)
self.gear_small_actor_ids_sim = torch.tensor(self.gear_small_actor_ids_sim, dtype=torch.int32,
device=self.device)
self.gear_medium_actor_ids_sim = torch.tensor(self.gear_medium_actor_ids_sim, dtype=torch.int32,
device=self.device)
self.gear_large_actor_ids_sim = torch.tensor(self.gear_large_actor_ids_sim, dtype=torch.int32,
device=self.device)
self.base_actor_ids_sim = torch.tensor(self.base_actor_ids_sim, dtype=torch.int32, device=self.device)
# For extracting root pos/quat
self.gear_small_actor_id_env = self.gym.find_actor_index(env_ptr, 'gear_small', gymapi.DOMAIN_ENV)
self.gear_medium_actor_id_env = self.gym.find_actor_index(env_ptr, 'gear_medium', gymapi.DOMAIN_ENV)
self.gear_large_actor_id_env = self.gym.find_actor_index(env_ptr, 'gear_large', gymapi.DOMAIN_ENV)
self.base_actor_id_env = self.gym.find_actor_index(env_ptr, 'base', gymapi.DOMAIN_ENV)
# For extracting body pos/quat, force, and Jacobian
self.gear_small_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, gear_small_handle, 'gear_small',
gymapi.DOMAIN_ENV)
self.gear_mediums_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, gear_medium_handle, 'gear_small',
gymapi.DOMAIN_ENV)
self.gear_large_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, gear_large_handle, 'gear_small',
gymapi.DOMAIN_ENV)
self.base_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, base_handle, 'base', gymapi.DOMAIN_ENV)
self.hand_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_hand',
gymapi.DOMAIN_ENV)
self.left_finger_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_leftfinger',
gymapi.DOMAIN_ENV)
self.right_finger_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle,
'panda_rightfinger', gymapi.DOMAIN_ENV)
self.fingertip_centered_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle,
'panda_fingertip_centered',
gymapi.DOMAIN_ENV)
def _acquire_env_tensors(self):
"""Acquire and wrap tensors. Create views."""
self.gear_small_pos = self.root_pos[:, self.gear_small_actor_id_env, 0:3]
self.gear_small_quat = self.root_quat[:, self.gear_small_actor_id_env, 0:4]
self.gear_small_linvel = self.root_linvel[:, self.gear_small_actor_id_env, 0:3]
self.gear_small_angvel = self.root_angvel[:, self.gear_small_actor_id_env, 0:3]
self.gear_medium_pos = self.root_pos[:, self.gear_medium_actor_id_env, 0:3]
self.gear_medium_quat = self.root_quat[:, self.gear_medium_actor_id_env, 0:4]
self.gear_medium_linvel = self.root_linvel[:, self.gear_medium_actor_id_env, 0:3]
self.gear_medium_angvel = self.root_angvel[:, self.gear_medium_actor_id_env, 0:3]
self.gear_large_pos = self.root_pos[:, self.gear_large_actor_id_env, 0:3]
self.gear_large_quat = self.root_quat[:, self.gear_large_actor_id_env, 0:4]
self.gear_large_linvel = self.root_linvel[:, self.gear_large_actor_id_env, 0:3]
self.gear_large_angvel = self.root_angvel[:, self.gear_large_actor_id_env, 0:3]
self.base_pos = self.root_pos[:, self.base_actor_id_env, 0:3]
self.base_quat = self.root_quat[:, self.base_actor_id_env, 0:4]
self.gear_small_com_pos = fc.translate_along_local_z(pos=self.gear_small_pos,
quat=self.gear_small_quat,
offset=self.asset_info_gears.gear_base_height + self.asset_info_gears.gear_height * 0.5,
device=self.device)
self.gear_small_com_quat = self.gear_small_quat # always equal
self.gear_small_com_linvel = self.gear_small_linvel + torch.cross(self.gear_small_angvel,
(self.gear_small_com_pos - self.gear_small_pos),
dim=1)
self.gear_small_com_angvel = self.gear_small_angvel # always equal
self.gear_medium_com_pos = fc.translate_along_local_z(pos=self.gear_medium_pos,
quat=self.gear_medium_quat,
offset=self.asset_info_gears.gear_base_height + self.asset_info_gears.gear_height * 0.5,
device=self.device)
self.gear_medium_com_quat = self.gear_medium_quat # always equal
self.gear_medium_com_linvel = self.gear_medium_linvel + torch.cross(self.gear_medium_angvel,
(self.gear_medium_com_pos - self.gear_medium_pos),
dim=1)
self.gear_medium_com_angvel = self.gear_medium_angvel # always equal
self.gear_large_com_pos = fc.translate_along_local_z(pos=self.gear_large_pos,
quat=self.gear_large_quat,
offset=self.asset_info_gears.gear_base_height + self.asset_info_gears.gear_height * 0.5,
device=self.device)
self.gear_large_com_quat = self.gear_large_quat # always equal
self.gear_large_com_linvel = self.gear_large_linvel + torch.cross(self.gear_large_angvel,
(self.gear_large_com_pos - self.gear_large_pos),
dim=1)
self.gear_large_com_angvel = self.gear_large_angvel # always equal
def refresh_env_tensors(self):
"""Refresh tensors."""
# NOTE: Tensor refresh functions should be called once per step, before setters.
self.gear_small_com_pos = fc.translate_along_local_z(pos=self.gear_small_pos,
quat=self.gear_small_quat,
offset=self.asset_info_gears.gear_base_height + self.asset_info_gears.gear_height * 0.5,
device=self.device)
self.gear_small_com_linvel = self.gear_small_linvel + torch.cross(self.gear_small_angvel,
(self.gear_small_com_pos - self.gear_small_pos),
dim=1)
self.gear_medium_com_pos = fc.translate_along_local_z(pos=self.gear_medium_pos,
quat=self.gear_medium_quat,
offset=self.asset_info_gears.gear_base_height + self.asset_info_gears.gear_height * 0.5,
device=self.device)
self.gear_medium_com_linvel = self.gear_medium_linvel + torch.cross(self.gear_medium_angvel,
(self.gear_medium_com_pos - self.gear_medium_pos),
dim=1)
self.gear_large_com_pos = fc.translate_along_local_z(pos=self.gear_large_pos,
quat=self.gear_large_quat,
offset=self.asset_info_gears.gear_base_height + self.asset_info_gears.gear_height * 0.5,
device=self.device)
self.gear_large_com_linvel = self.gear_large_linvel + torch.cross(self.gear_large_angvel,
(self.gear_large_com_pos - self.gear_large_pos),
dim=1)
| 25,262 | Python | 60.617073 | 150 | 0.586731 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_schema_config_task.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: schema for task class configurations.
Used by Hydra. Defines template for task class YAML files. Not enforced.
"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class Sim:
use_gpu_pipeline: bool # use GPU pipeline
up_axis: str # up-down axis {x, y, z}
dt: float # timestep size
gravity: list[float] # gravity vector
disable_gravity: bool # disable gravity for all actors
@dataclass
class Env:
numObservations: int # number of observations per env; camel case required by VecTask
numActions: int # number of actions per env; camel case required by VecTask
numEnvs: int # number of envs; camel case required by VecTask
@dataclass
class Randomize:
franka_arm_initial_dof_pos: list[float] # initial Franka arm DOF position (7)
@dataclass
class RL:
pos_action_scale: list[float] # scale on pos displacement targets (3), to convert [-1, 1] to +- x m
rot_action_scale: list[float] # scale on rot displacement targets (3), to convert [-1, 1] to +- x rad
force_action_scale: list[float] # scale on force targets (3), to convert [-1, 1] to +- x N
torque_action_scale: list[float] # scale on torque targets (3), to convert [-1, 1] to +- x Nm
clamp_rot: bool # clamp small values of rotation actions to zero
clamp_rot_thresh: float # smallest acceptable value
max_episode_length: int # max number of timesteps in each episode
@dataclass
class All:
jacobian_type: str # map between joint space and task space via geometric or analytic Jacobian {geometric, analytic}
gripper_prop_gains: list[float] # proportional gains on left and right Franka gripper finger DOF position (2)
gripper_deriv_gains: list[float] # derivative gains on left and right Franka gripper finger DOF position (2)
@dataclass
class GymDefault:
joint_prop_gains: list[int] # proportional gains on Franka arm DOF position (7)
joint_deriv_gains: list[int] # derivative gains on Franka arm DOF position (7)
@dataclass
class JointSpaceIK:
ik_method: str # use Jacobian pseudoinverse, Jacobian transpose, damped least squares or adaptive SVD {pinv, trans, dls, svd}
joint_prop_gains: list[int]
joint_deriv_gains: list[int]
@dataclass
class JointSpaceID:
ik_method: str
joint_prop_gains: list[int]
joint_deriv_gains: list[int]
@dataclass
class TaskSpaceImpedance:
motion_ctrl_axes: list[bool] # axes for which to enable motion control {0, 1} (6)
task_prop_gains: list[float] # proportional gains on Franka fingertip pose (6)
task_deriv_gains: list[float] # derivative gains on Franka fingertip pose (6)
@dataclass
class OperationalSpaceMotion:
motion_ctrl_axes: list[bool]
task_prop_gains: list[float]
task_deriv_gains: list[float]
@dataclass
class OpenLoopForce:
force_ctrl_axes: list[bool] # axes for which to enable force control {0, 1} (6)
@dataclass
class ClosedLoopForce:
force_ctrl_axes: list[bool]
wrench_prop_gains: list[float] # proportional gains on Franka finger force (6)
@dataclass
class HybridForceMotion:
motion_ctrl_axes: list[bool]
task_prop_gains: list[float]
task_deriv_gains: list[float]
force_ctrl_axes: list[bool]
wrench_prop_gains: list[float]
@dataclass
class Ctrl:
ctrl_type: str # {gym_default,
# joint_space_ik,
# joint_space_id,
# task_space_impedance,
# operational_space_motion,
# open_loop_force,
# closed_loop_force,
# hybrid_force_motion}
gym_default: GymDefault
joint_space_ik: JointSpaceIK
joint_space_id: JointSpaceID
task_space_impedance: TaskSpaceImpedance
operational_space_motion: OperationalSpaceMotion
open_loop_force: OpenLoopForce
closed_loop_force: ClosedLoopForce
hybrid_force_motion: HybridForceMotion
@dataclass
class FactorySchemaConfigTask:
name: str
physics_engine: str
sim: Sim
env: Env
rl: RL
ctrl: Ctrl
| 5,639 | Python | 33.814815 | 130 | 0.715552 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_task_nut_bolt_place.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: Class for nut-bolt place task.
Inherits nut-bolt environment class and abstract task class (not enforced). Can be executed with
python train.py task=FactoryTaskNutBoltPlace
"""
import hydra
import math
import omegaconf
import os
import torch
from isaacgym import gymapi, gymtorch
from isaacgymenvs.utils import torch_jit_utils as torch_utils
import isaacgymenvs.tasks.factory.factory_control as fc
from isaacgymenvs.tasks.factory.factory_env_nut_bolt import FactoryEnvNutBolt
from isaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from isaacgymenvs.tasks.factory.factory_schema_config_task import FactorySchemaConfigTask
from isaacgymenvs.utils import torch_jit_utils
class FactoryTaskNutBoltPlace(FactoryEnvNutBolt, FactoryABCTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
"""Initialize instance variables. Initialize environment superclass."""
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
self.cfg = cfg
self._get_task_yaml_params()
self._acquire_task_tensors()
self.parse_controller_spec()
if self.cfg_task.sim.disable_gravity:
self.disable_gravity()
if self.viewer is not None:
self._set_viewer_params()
def _get_task_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name='factory_schema_config_task', node=FactorySchemaConfigTask)
self.cfg_task = omegaconf.OmegaConf.create(self.cfg)
self.max_episode_length = self.cfg_task.rl.max_episode_length # required instance var for VecTask
asset_info_path = '../../assets/factory/yaml/factory_asset_info_nut_bolt.yaml' # relative to Gym's Hydra search path (cfg dir)
self.asset_info_nut_bolt = hydra.compose(config_name=asset_info_path)
self.asset_info_nut_bolt = self.asset_info_nut_bolt['']['']['']['']['']['']['assets']['factory']['yaml'] # strip superfluous nesting
ppo_path = 'train/FactoryTaskNutBoltPlacePPO.yaml' # relative to Gym's Hydra search path (cfg dir)
self.cfg_ppo = hydra.compose(config_name=ppo_path)
self.cfg_ppo = self.cfg_ppo['train'] # strip superfluous nesting
def _acquire_task_tensors(self):
"""Acquire tensors."""
# Nut-bolt tensors
self.nut_base_pos_local = \
self.bolt_head_heights * torch.tensor([0.0, 0.0, 1.0], device=self.device).repeat((self.num_envs, 1))
bolt_heights = self.bolt_head_heights + self.bolt_shank_lengths
self.bolt_tip_pos_local = \
bolt_heights * torch.tensor([0.0, 0.0, 1.0], device=self.device).repeat((self.num_envs, 1))
# Keypoint tensors
self.keypoint_offsets = \
self._get_keypoint_offsets(self.cfg_task.rl.num_keypoints) * self.cfg_task.rl.keypoint_scale
self.keypoints_nut = torch.zeros((self.num_envs, self.cfg_task.rl.num_keypoints, 3),
dtype=torch.float32,
device=self.device)
self.keypoints_bolt = torch.zeros_like(self.keypoints_nut, device=self.device)
self.identity_quat = \
torch.tensor([0.0, 0.0, 0.0, 1.0], device=self.device).unsqueeze(0).repeat(self.num_envs, 1)
self.actions = torch.zeros((self.num_envs, self.cfg_task.env.numActions), device=self.device)
def _refresh_task_tensors(self):
"""Refresh tensors."""
# Compute pos of keypoints on gripper, nut, and bolt in world frame
for idx, keypoint_offset in enumerate(self.keypoint_offsets):
self.keypoints_nut[:, idx] = torch_jit_utils.tf_combine(self.nut_quat,
self.nut_pos,
self.identity_quat,
(keypoint_offset + self.nut_base_pos_local))[1]
self.keypoints_bolt[:, idx] = torch_jit_utils.tf_combine(self.bolt_quat,
self.bolt_pos,
self.identity_quat,
(keypoint_offset + self.bolt_tip_pos_local))[1]
def pre_physics_step(self, actions):
"""Reset environments. Apply actions from policy. Simulation step called after this method."""
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.actions = actions.clone().to(self.device) # shape = (num_envs, num_actions); values = [-1, 1]
self._apply_actions_as_ctrl_targets(actions=self.actions,
ctrl_target_gripper_dof_pos=0.0,
do_scale=True)
def post_physics_step(self):
"""Step buffers. Refresh tensors. Compute observations and reward. Reset environments."""
self.progress_buf[:] += 1
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
self.compute_observations()
self.compute_reward()
def compute_observations(self):
"""Compute observations."""
# Shallow copies of tensors
obs_tensors = [self.fingertip_midpoint_pos,
self.fingertip_midpoint_quat,
self.fingertip_midpoint_linvel,
self.fingertip_midpoint_angvel,
self.nut_pos,
self.nut_quat,
self.bolt_pos,
self.bolt_quat]
if self.cfg_task.rl.add_obs_bolt_tip_pos:
obs_tensors += [self.bolt_tip_pos_local]
self.obs_buf = torch.cat(obs_tensors, dim=-1) # shape = (num_envs, num_observations)
return self.obs_buf
def compute_reward(self):
"""Update reward and reset buffers."""
self._update_reset_buf()
self._update_rew_buf()
def _update_reset_buf(self):
"""Assign environments for reset if successful or failed."""
# If max episode length has been reached
self.reset_buf[:] = torch.where(self.progress_buf[:] >= self.cfg_task.rl.max_episode_length - 1,
torch.ones_like(self.reset_buf),
self.reset_buf)
def _update_rew_buf(self):
"""Compute reward at current timestep."""
keypoint_reward = -self._get_keypoint_dist()
action_penalty = torch.norm(self.actions, p=2, dim=-1) * self.cfg_task.rl.action_penalty_scale
self.rew_buf[:] = keypoint_reward * self.cfg_task.rl.keypoint_reward_scale \
- action_penalty * self.cfg_task.rl.action_penalty_scale
# In this policy, episode length is constant across all envs
is_last_step = (self.progress_buf[0] == self.max_episode_length - 1)
if is_last_step:
# Check if nut is close enough to bolt
is_nut_close_to_bolt = self._check_nut_close_to_bolt()
self.rew_buf[:] += is_nut_close_to_bolt * self.cfg_task.rl.success_bonus
self.extras['successes'] = torch.mean(is_nut_close_to_bolt.float())
def reset_idx(self, env_ids):
"""Reset specified environments."""
self._reset_franka(env_ids)
self._reset_object(env_ids)
# Close gripper onto nut
self.disable_gravity() # to prevent nut from falling
for _ in range(self.cfg_task.env.num_gripper_close_sim_steps):
self.ctrl_target_dof_pos[env_ids, 7:9] = 0.0
delta_hand_pose = torch.zeros((self.num_envs, self.cfg_task.env.numActions),
device=self.device) # no arm motion
self._apply_actions_as_ctrl_targets(actions=delta_hand_pose,
ctrl_target_gripper_dof_pos=0.0,
do_scale=False)
self.gym.simulate(self.sim)
self.render()
self.enable_gravity(gravity_mag=abs(self.cfg_base.sim.gravity[2]))
self._randomize_gripper_pose(env_ids, sim_steps=self.cfg_task.env.num_gripper_move_sim_steps)
self._reset_buffers(env_ids)
def _reset_franka(self, env_ids):
"""Reset DOF states and DOF targets of Franka."""
self.dof_pos[env_ids] = \
torch.cat((torch.tensor(self.cfg_task.randomize.franka_arm_initial_dof_pos, device=self.device).repeat((len(env_ids), 1)),
(self.nut_widths_max * 0.5) * 1.1, # buffer on gripper DOF pos to prevent initial contact
(self.nut_widths_max * 0.5) * 1.1), # buffer on gripper DOF pos to prevent initial contact
dim=-1) # shape = (num_envs, num_dofs)
self.dof_vel[env_ids] = 0.0 # shape = (num_envs, num_dofs)
self.ctrl_target_dof_pos[env_ids] = self.dof_pos[env_ids]
multi_env_ids_int32 = self.franka_actor_ids_sim[env_ids].flatten()
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(multi_env_ids_int32),
len(multi_env_ids_int32))
def _reset_object(self, env_ids):
"""Reset root states of nut and bolt."""
# shape of root_pos = (num_envs, num_actors, 3)
# shape of root_quat = (num_envs, num_actors, 4)
# shape of root_linvel = (num_envs, num_actors, 3)
# shape of root_angvel = (num_envs, num_actors, 3)
# Randomize root state of nut within gripper
self.root_pos[env_ids, self.nut_actor_id_env, 0] = 0.0
self.root_pos[env_ids, self.nut_actor_id_env, 1] = 0.0
fingertip_midpoint_pos_reset = 0.58781 # self.fingertip_midpoint_pos at reset
nut_base_pos_local = self.bolt_head_heights.squeeze(-1)
self.root_pos[env_ids, self.nut_actor_id_env, 2] = fingertip_midpoint_pos_reset - nut_base_pos_local
nut_noise_pos_in_gripper = \
2 * (torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) - 0.5) # [-1, 1]
nut_noise_pos_in_gripper = nut_noise_pos_in_gripper @ torch.diag(
torch.tensor(self.cfg_task.randomize.nut_noise_pos_in_gripper, device=self.device))
self.root_pos[env_ids, self.nut_actor_id_env, :] += nut_noise_pos_in_gripper[env_ids]
nut_rot_euler = torch.tensor([0.0, 0.0, math.pi * 0.5], device=self.device).repeat(len(env_ids), 1)
nut_noise_rot_in_gripper = \
2 * (torch.rand(self.num_envs, dtype=torch.float32, device=self.device) - 0.5) # [-1, 1]
nut_noise_rot_in_gripper *= self.cfg_task.randomize.nut_noise_rot_in_gripper
nut_rot_euler[:, 2] += nut_noise_rot_in_gripper
nut_rot_quat = torch_utils.quat_from_euler_xyz(nut_rot_euler[:, 0], nut_rot_euler[:, 1], nut_rot_euler[:, 2])
self.root_quat[env_ids, self.nut_actor_id_env] = nut_rot_quat
# Randomize root state of bolt
bolt_noise_xy = 2 * (torch.rand((self.num_envs, 2), dtype=torch.float32, device=self.device) - 0.5) # [-1, 1]
bolt_noise_xy = bolt_noise_xy @ torch.diag(
torch.tensor(self.cfg_task.randomize.bolt_pos_xy_noise, dtype=torch.float32, device=self.device))
self.root_pos[env_ids, self.bolt_actor_id_env, 0] = self.cfg_task.randomize.bolt_pos_xy_initial[0] + \
bolt_noise_xy[env_ids, 0]
self.root_pos[env_ids, self.bolt_actor_id_env, 1] = self.cfg_task.randomize.bolt_pos_xy_initial[1] + \
bolt_noise_xy[env_ids, 1]
self.root_pos[env_ids, self.bolt_actor_id_env, 2] = self.cfg_base.env.table_height
self.root_quat[env_ids, self.bolt_actor_id_env] = torch.tensor([0.0, 0.0, 0.0, 1.0], dtype=torch.float32,
device=self.device).repeat(len(env_ids), 1)
self.root_linvel[env_ids, self.bolt_actor_id_env] = 0.0
self.root_angvel[env_ids, self.bolt_actor_id_env] = 0.0
nut_bolt_actor_ids_sim = torch.cat((self.nut_actor_ids_sim[env_ids],
self.bolt_actor_ids_sim[env_ids]),
dim=0)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state),
gymtorch.unwrap_tensor(nut_bolt_actor_ids_sim),
len(nut_bolt_actor_ids_sim))
def _reset_buffers(self, env_ids):
"""Reset buffers. """
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def _set_viewer_params(self):
"""Set viewer parameters."""
cam_pos = gymapi.Vec3(-1.0, -1.0, 1.0)
cam_target = gymapi.Vec3(0.0, 0.0, 0.5)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
def _apply_actions_as_ctrl_targets(self, actions, ctrl_target_gripper_dof_pos, do_scale):
"""Apply actions from policy as position/rotation targets."""
# Interpret actions as target pos displacements and set pos target
pos_actions = actions[:, 0:3]
if do_scale:
pos_actions = pos_actions @ torch.diag(torch.tensor(self.cfg_task.rl.pos_action_scale, device=self.device))
self.ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions
# Interpret actions as target rot (axis-angle) displacements
rot_actions = actions[:, 3:6]
if do_scale:
rot_actions = rot_actions @ torch.diag(torch.tensor(self.cfg_task.rl.rot_action_scale, device=self.device))
# Convert to quat and set rot target
angle = torch.norm(rot_actions, p=2, dim=-1)
axis = rot_actions / angle.unsqueeze(-1)
rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis)
if self.cfg_task.rl.clamp_rot:
rot_actions_quat = torch.where(angle.unsqueeze(-1).repeat(1, 4) > self.cfg_task.rl.clamp_rot_thresh,
rot_actions_quat,
torch.tensor([0.0, 0.0, 0.0, 1.0], device=self.device).repeat(self.num_envs,
1))
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat)
if self.cfg_ctrl['do_force_ctrl']:
# Interpret actions as target forces and target torques
force_actions = actions[:, 6:9]
if do_scale:
force_actions = force_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.force_action_scale, device=self.device))
torque_actions = actions[:, 9:12]
if do_scale:
torque_actions = torque_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.torque_action_scale, device=self.device))
self.ctrl_target_fingertip_contact_wrench = torch.cat((force_actions, torque_actions), dim=-1)
self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos
self.generate_ctrl_signals()
def _open_gripper(self, sim_steps=20):
"""Fully open gripper using controller. Called outside RL loop (i.e., after last step of episode)."""
self._move_gripper_to_dof_pos(gripper_dof_pos=0.1, sim_steps=sim_steps)
def _move_gripper_to_dof_pos(self, gripper_dof_pos, sim_steps=20):
"""Move gripper fingers to specified DOF position using controller."""
delta_hand_pose = torch.zeros((self.num_envs, self.cfg_task.env.numActions),
device=self.device) # no arm motion
self._apply_actions_as_ctrl_targets(delta_hand_pose, gripper_dof_pos, do_scale=False)
# Step sim
for _ in range(sim_steps):
self.render()
self.gym.simulate(self.sim)
def _lift_gripper(self, gripper_dof_pos=0.0, lift_distance=0.3, sim_steps=20):
"""Lift gripper by specified distance. Called outside RL loop (i.e., after last step of episode)."""
delta_hand_pose = torch.zeros([self.num_envs, 6], device=self.device)
delta_hand_pose[:, 2] = lift_distance # lift along z
# Step sim
for _ in range(sim_steps):
self._apply_actions_as_ctrl_targets(delta_hand_pose, gripper_dof_pos, do_scale=False)
self.render()
self.gym.simulate(self.sim)
def _get_keypoint_offsets(self, num_keypoints):
"""Get uniformly-spaced keypoints along a line of unit length, centered at 0."""
keypoint_offsets = torch.zeros((num_keypoints, 3), device=self.device)
keypoint_offsets[:, -1] = torch.linspace(0.0, 1.0, num_keypoints, device=self.device) - 0.5
return keypoint_offsets
def _get_keypoint_dist(self):
"""Get keypoint distances."""
keypoint_dist = torch.sum(torch.norm(self.keypoints_bolt - self.keypoints_nut, p=2, dim=-1), dim=-1)
return keypoint_dist
def _check_nut_close_to_bolt(self):
"""Check if nut is close to bolt."""
keypoint_dist = torch.norm(self.keypoints_bolt - self.keypoints_nut, p=2, dim=-1)
is_nut_close_to_bolt = torch.where(torch.sum(keypoint_dist, dim=-1) < self.cfg_task.rl.close_error_thresh,
torch.ones_like(self.progress_buf),
torch.zeros_like(self.progress_buf))
return is_nut_close_to_bolt
def _randomize_gripper_pose(self, env_ids, sim_steps):
"""Move gripper to random pose."""
# Set target pos above table
self.ctrl_target_fingertip_midpoint_pos = \
torch.tensor([0.0, 0.0, self.cfg_base.env.table_height], device=self.device) \
+ torch.tensor(self.cfg_task.randomize.fingertip_midpoint_pos_initial, device=self.device)
self.ctrl_target_fingertip_midpoint_pos = self.ctrl_target_fingertip_midpoint_pos.unsqueeze(0).repeat(
self.num_envs, 1)
fingertip_midpoint_pos_noise = \
2 * (torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) - 0.5) # [-1, 1]
fingertip_midpoint_pos_noise = fingertip_midpoint_pos_noise @ torch.diag(
torch.tensor(self.cfg_task.randomize.fingertip_midpoint_pos_noise, device=self.device))
self.ctrl_target_fingertip_midpoint_pos += fingertip_midpoint_pos_noise
# Set target rot
ctrl_target_fingertip_midpoint_euler = torch.tensor(self.cfg_task.randomize.fingertip_midpoint_rot_initial,
device=self.device).unsqueeze(0).repeat(self.num_envs, 1)
fingertip_midpoint_rot_noise = \
2 * (torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) - 0.5) # [-1, 1]
fingertip_midpoint_rot_noise = fingertip_midpoint_rot_noise @ torch.diag(
torch.tensor(self.cfg_task.randomize.fingertip_midpoint_rot_noise, device=self.device))
ctrl_target_fingertip_midpoint_euler += fingertip_midpoint_rot_noise
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz(
ctrl_target_fingertip_midpoint_euler[:, 0],
ctrl_target_fingertip_midpoint_euler[:, 1],
ctrl_target_fingertip_midpoint_euler[:, 2])
# Step sim and render
for _ in range(sim_steps):
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
pos_error, axis_angle_error = fc.get_pose_error(
fingertip_midpoint_pos=self.fingertip_midpoint_pos,
fingertip_midpoint_quat=self.fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
jacobian_type=self.cfg_ctrl['jacobian_type'],
rot_error_type='axis_angle')
delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1)
actions = torch.zeros((self.num_envs, self.cfg_task.env.numActions), device=self.device)
actions[:, :6] = delta_hand_pose
self._apply_actions_as_ctrl_targets(actions=actions,
ctrl_target_gripper_dof_pos=0.0,
do_scale=False)
self.gym.simulate(self.sim)
self.render()
self.dof_vel[env_ids, :] = torch.zeros_like(self.dof_vel[env_ids])
# Set DOF state
multi_env_ids_int32 = self.franka_actor_ids_sim[env_ids].flatten()
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(multi_env_ids_int32),
len(multi_env_ids_int32))
| 23,304 | Python | 49.226293 | 141 | 0.596421 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_schema_config_env.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: schema for environment class configurations.
Used by Hydra. Defines template for environment class YAML files.
"""
from dataclasses import dataclass
@dataclass
class Sim:
disable_franka_collisions: bool # disable collisions between Franka and objects
@dataclass
class Env:
env_name: str # name of scene
@dataclass
class FactorySchemaConfigEnv:
sim: Sim
env: Env
| 1,959 | Python | 37.431372 | 84 | 0.776927 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_schema_class_task.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: abstract base class for task classes.
Inherits ABC class. Inherited by task classes. Defines template for task classes.
"""
from abc import ABC, abstractmethod
class FactoryABCTask(ABC):
@abstractmethod
def __init__(self):
"""Initialize instance variables. Initialize environment superclass."""
pass
@abstractmethod
def _get_task_yaml_params(self):
"""Initialize instance variables from YAML files."""
pass
@abstractmethod
def _acquire_task_tensors(self):
"""Acquire tensors."""
pass
@abstractmethod
def _refresh_task_tensors(self):
"""Refresh tensors."""
pass
@abstractmethod
def pre_physics_step(self):
"""Reset environments. Apply actions from policy as controller targets. Simulation step called after this method."""
pass
@abstractmethod
def post_physics_step(self):
"""Step buffers. Refresh tensors. Compute observations and reward."""
pass
@abstractmethod
def compute_observations(self):
"""Compute observations."""
pass
@abstractmethod
def compute_reward(self):
"""Detect successes and failures. Update reward and reset buffers."""
pass
@abstractmethod
def _update_rew_buf(self):
"""Compute reward at current timestep."""
pass
@abstractmethod
def _update_reset_buf(self):
"""Assign environments for reset if successful or failed."""
pass
@abstractmethod
def reset_idx(self):
"""Reset specified environments."""
pass
@abstractmethod
def _reset_franka(self):
"""Reset DOF states and DOF targets of Franka."""
pass
@abstractmethod
def _reset_object(self):
"""Reset root state of object."""
pass
@abstractmethod
def _reset_buffers(self):
"""Reset buffers."""
pass
@abstractmethod
def _set_viewer_params(self):
"""Set viewer parameters."""
pass
| 3,598 | Python | 30.849557 | 124 | 0.691773 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_schema_class_env.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: abstract base class for environment classes.
Inherits ABC class. Inherited by environment classes. Defines template for environment classes.
"""
from abc import ABC, abstractmethod
class FactoryABCEnv(ABC):
@abstractmethod
def __init__(self):
"""Initialize instance variables. Initialize base superclass. Acquire tensors."""
pass
@abstractmethod
def _get_env_yaml_params(self):
"""Initialize instance variables from YAML files."""
pass
@abstractmethod
def create_envs(self):
"""Set env options. Import assets. Create actors."""
pass
@abstractmethod
def _import_env_assets(self):
"""Set asset options. Import assets."""
pass
@abstractmethod
def _create_actors(self):
"""Set initial actor poses. Create actors. Set shape and DOF properties."""
pass
@abstractmethod
def _acquire_env_tensors(self):
"""Acquire and wrap tensors. Create views."""
pass
@abstractmethod
def refresh_env_tensors(self):
"""Refresh tensors."""
# NOTE: Tensor refresh functions should be called once per step, before setters.
pass
| 2,760 | Python | 36.31081 | 95 | 0.724638 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_task_nut_bolt_screw.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: Class for nut-bolt screw task.
Inherits nut-bolt environment class and abstract task class (not enforced). Can be executed with
python train.py task=FactoryTaskNutBoltScrew
Initial Franka/nut states are ideal for M16 nut-and-bolt.
In this example, initial state randomization is not applied; thus, policy should succeed almost instantly.
"""
import hydra
import math
import omegaconf
import os
import torch
from isaacgym import gymapi, gymtorch
from isaacgymenvs.utils import torch_jit_utils as torch_utils
import isaacgymenvs.tasks.factory.factory_control as fc
from isaacgymenvs.tasks.factory.factory_env_nut_bolt import FactoryEnvNutBolt
from isaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from isaacgymenvs.tasks.factory.factory_schema_config_task import FactorySchemaConfigTask
class FactoryTaskNutBoltScrew(FactoryEnvNutBolt, FactoryABCTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
"""Initialize instance variables. Initialize environment superclass."""
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
self.cfg = cfg
self._get_task_yaml_params()
self._acquire_task_tensors()
self.parse_controller_spec()
if self.cfg_task.sim.disable_gravity:
self.disable_gravity()
if self.viewer != None:
self._set_viewer_params()
def _get_task_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name='factory_schema_config_task', node=FactorySchemaConfigTask)
self.cfg_task = omegaconf.OmegaConf.create(self.cfg)
self.max_episode_length = self.cfg_task.rl.max_episode_length # required instance var for VecTask
asset_info_path = '../../assets/factory/yaml/factory_asset_info_nut_bolt.yaml' # relative to Gym's Hydra search path (cfg dir)
self.asset_info_nut_bolt = hydra.compose(config_name=asset_info_path)
self.asset_info_nut_bolt = self.asset_info_nut_bolt['']['']['']['']['']['']['assets']['factory']['yaml'] # strip superfluous nesting
ppo_path = 'train/FactoryTaskNutBoltScrewPPO.yaml' # relative to Gym's Hydra search path (cfg dir)
self.cfg_ppo = hydra.compose(config_name=ppo_path)
self.cfg_ppo = self.cfg_ppo['train'] # strip superfluous nesting
def _acquire_task_tensors(self):
"""Acquire tensors."""
target_heights = self.cfg_base.env.table_height + self.bolt_head_heights + self.nut_heights * 0.5
self.target_pos = target_heights * torch.tensor([0.0, 0.0, 1.0], device=self.device).repeat((self.num_envs, 1))
def _refresh_task_tensors(self):
"""Refresh tensors."""
self.fingerpad_midpoint_pos = fc.translate_along_local_z(pos=self.finger_midpoint_pos,
quat=self.hand_quat,
offset=self.asset_info_franka_table.franka_finger_length - self.asset_info_franka_table.franka_fingerpad_length * 0.5,
device=self.device)
self.finger_nut_keypoint_dist = self._get_keypoint_dist(body='finger_nut')
self.nut_keypoint_dist = self._get_keypoint_dist(body='nut')
self.nut_dist_to_target = torch.norm(self.target_pos - self.nut_com_pos, p=2,
dim=-1) # distance between nut COM and target
self.nut_dist_to_fingerpads = torch.norm(self.fingerpad_midpoint_pos - self.nut_com_pos, p=2,
dim=-1) # distance between nut COM and midpoint between centers of fingerpads
def pre_physics_step(self, actions):
"""Reset environments. Apply actions from policy. Simulation step called after this method."""
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.actions = actions.clone().to(self.device) # shape = (num_envs, num_actions); values = [-1, 1]
self._apply_actions_as_ctrl_targets(actions=self.actions,
ctrl_target_gripper_dof_pos=0.0,
do_scale=True)
def post_physics_step(self):
"""Step buffers. Refresh tensors. Compute observations and reward. Reset environments."""
self.progress_buf[:] += 1
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
self.compute_observations()
self.compute_reward()
def compute_observations(self):
"""Compute observations."""
# Shallow copies of tensors
obs_tensors = [self.fingertip_midpoint_pos,
self.fingertip_midpoint_quat,
self.fingertip_midpoint_linvel,
self.fingertip_midpoint_angvel,
self.nut_com_pos,
self.nut_com_quat,
self.nut_com_linvel,
self.nut_com_angvel]
if self.cfg_task.rl.add_obs_finger_force:
obs_tensors += [self.left_finger_force, self.right_finger_force]
obs_tensors = torch.cat(obs_tensors, dim=-1)
self.obs_buf[:, :obs_tensors.shape[-1]] = obs_tensors # shape = (num_envs, num_observations)
return self.obs_buf
def compute_reward(self):
"""Detect successes and failures. Update reward and reset buffers."""
# Get successful and failed envs at current timestep
curr_successes = self._get_curr_successes()
curr_failures = self._get_curr_failures(curr_successes)
self._update_reset_buf(curr_successes, curr_failures)
self._update_rew_buf(curr_successes)
def _update_reset_buf(self, curr_successes, curr_failures):
"""Assign environments for reset if successful or failed."""
self.reset_buf[:] = torch.logical_or(curr_successes, curr_failures)
def _update_rew_buf(self, curr_successes):
"""Compute reward at current timestep."""
keypoint_reward = -(self.nut_keypoint_dist + self.finger_nut_keypoint_dist)
action_penalty = torch.norm(self.actions, p=2, dim=-1)
self.rew_buf[:] = keypoint_reward * self.cfg_task.rl.keypoint_reward_scale \
- action_penalty * self.cfg_task.rl.action_penalty_scale \
+ curr_successes * self.cfg_task.rl.success_bonus
def reset_idx(self, env_ids):
"""Reset specified environments. Zero buffers."""
self._reset_franka(env_ids)
self._reset_object(env_ids)
self._reset_buffers(env_ids)
def _reset_franka(self, env_ids):
"""Reset DOF states and DOF targets of Franka."""
self.dof_pos[env_ids] = torch.cat((torch.tensor(self.cfg_task.randomize.franka_arm_initial_dof_pos,
device=self.device).repeat((len(env_ids), 1)),
(self.nut_widths_max[env_ids] * 0.5) * 1.1, # buffer on gripper DOF pos to prevent initial contact
(self.nut_widths_max[env_ids] * 0.5) * 1.1), # buffer on gripper DOF pos to prevent initial contact
dim=-1) # shape = (num_envs, num_dofs)
self.dof_vel[env_ids] = 0.0 # shape = (num_envs, num_dofs)
self.ctrl_target_dof_pos[env_ids] = self.dof_pos[env_ids]
multi_env_ids_int32 = self.franka_actor_ids_sim[env_ids].flatten()
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(multi_env_ids_int32),
len(multi_env_ids_int32))
def _reset_object(self, env_ids):
"""Reset root state of nut."""
# shape of root_pos = (num_envs, num_actors, 3)
# shape of root_quat = (num_envs, num_actors, 4)
# shape of root_linvel = (num_envs, num_actors, 3)
# shape of root_angvel = (num_envs, num_actors, 3)
nut_pos = self.cfg_base.env.table_height + self.bolt_shank_lengths[env_ids]
self.root_pos[env_ids, self.nut_actor_id_env] = \
nut_pos * torch.tensor([0.0, 0.0, 1.0], device=self.device).repeat(len(env_ids), 1)
nut_rot = self.cfg_task.randomize.nut_rot_initial * torch.ones((len(env_ids), 1), device=self.device) * math.pi / 180.0
self.root_quat[env_ids, self.nut_actor_id_env] = torch.cat((torch.zeros((len(env_ids), 1), device=self.device),
torch.zeros((len(env_ids), 1), device=self.device),
torch.sin(nut_rot * 0.5),
torch.cos(nut_rot * 0.5)),
dim=-1)
self.root_linvel[env_ids, self.nut_actor_id_env] = 0.0
self.root_angvel[env_ids, self.nut_actor_id_env] = 0.0
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state),
gymtorch.unwrap_tensor(self.nut_actor_ids_sim),
len(self.nut_actor_ids_sim))
def _reset_buffers(self, env_ids):
"""Reset buffers."""
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def _set_viewer_params(self):
"""Set viewer parameters."""
cam_pos = gymapi.Vec3(-1.0, -1.0, 1.0)
cam_target = gymapi.Vec3(0.0, 0.0, 0.5)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
def _apply_actions_as_ctrl_targets(self, actions, ctrl_target_gripper_dof_pos, do_scale):
"""Apply actions from policy as position/rotation targets or force/torque targets."""
# Interpret actions as target pos displacements and set pos target
pos_actions = actions[:, 0:3]
if do_scale:
pos_actions = pos_actions @ torch.diag(torch.tensor(self.cfg_task.rl.pos_action_scale, device=self.device))
self.ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions
# Interpret actions as target rot (axis-angle) displacements
rot_actions = actions[:, 3:6]
if self.cfg_task.rl.unidirectional_rot:
rot_actions[:, 2] = -(rot_actions[:, 2] + 1.0) * 0.5 # [-1, 0]
if do_scale:
rot_actions = rot_actions @ torch.diag(torch.tensor(self.cfg_task.rl.rot_action_scale, device=self.device))
# Convert to quat and set rot target
angle = torch.norm(rot_actions, p=2, dim=-1)
axis = rot_actions / angle.unsqueeze(-1)
rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis)
if self.cfg_task.rl.clamp_rot:
rot_actions_quat = torch.where(angle.unsqueeze(-1).repeat(1, 4) > self.cfg_task.rl.clamp_rot_thresh,
rot_actions_quat,
torch.tensor([0.0, 0.0, 0.0, 1.0], device=self.device).repeat(self.num_envs,
1))
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat)
if self.cfg_ctrl['do_force_ctrl']:
# Interpret actions as target forces and target torques
force_actions = actions[:, 6:9]
if self.cfg_task.rl.unidirectional_force:
force_actions[:, 2] = -(force_actions[:, 2] + 1.0) * 0.5 # [-1, 0]
if do_scale:
force_actions = force_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.force_action_scale, device=self.device))
torque_actions = actions[:, 9:12]
if do_scale:
torque_actions = torque_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.torque_action_scale, device=self.device))
self.ctrl_target_fingertip_contact_wrench = torch.cat((force_actions, torque_actions), dim=-1)
self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos
self.generate_ctrl_signals()
def _get_keypoint_dist(self, body):
"""Get keypoint distances."""
axis_length = self.asset_info_franka_table.franka_hand_length + self.asset_info_franka_table.franka_finger_length
if body == 'finger' or body == 'nut':
# Keypoint distance between finger/nut and target
if body == 'finger':
self.keypoint1 = self.fingertip_midpoint_pos
self.keypoint2 = fc.translate_along_local_z(pos=self.keypoint1,
quat=self.fingertip_midpoint_quat,
offset=-axis_length,
device=self.device)
elif body == 'nut':
self.keypoint1 = self.nut_com_pos
self.keypoint2 = fc.translate_along_local_z(pos=self.nut_com_pos,
quat=self.nut_com_quat,
offset=axis_length,
device=self.device)
self.keypoint1_targ = self.target_pos
self.keypoint2_targ = self.keypoint1_targ + torch.tensor([0.0, 0.0, axis_length], device=self.device)
elif body == 'finger_nut':
# Keypoint distance between finger and nut
self.keypoint1 = self.fingerpad_midpoint_pos
self.keypoint2 = fc.translate_along_local_z(pos=self.keypoint1,
quat=self.fingertip_midpoint_quat,
offset=-axis_length,
device=self.device)
self.keypoint1_targ = self.nut_com_pos
self.keypoint2_targ = fc.translate_along_local_z(pos=self.nut_com_pos,
quat=self.nut_com_quat,
offset=axis_length,
device=self.device)
self.keypoint3 = self.keypoint1 + (self.keypoint2 - self.keypoint1) * 1.0 / 3.0
self.keypoint4 = self.keypoint1 + (self.keypoint2 - self.keypoint1) * 2.0 / 3.0
self.keypoint3_targ = self.keypoint1_targ + (self.keypoint2_targ - self.keypoint1_targ) * 1.0 / 3.0
self.keypoint4_targ = self.keypoint1_targ + (self.keypoint2_targ - self.keypoint1_targ) * 2.0 / 3.0
keypoint_dist = torch.norm(self.keypoint1_targ - self.keypoint1, p=2, dim=-1) \
+ torch.norm(self.keypoint2_targ - self.keypoint2, p=2, dim=-1) \
+ torch.norm(self.keypoint3_targ - self.keypoint3, p=2, dim=-1) \
+ torch.norm(self.keypoint4_targ - self.keypoint4, p=2, dim=-1)
return keypoint_dist
def _get_curr_successes(self):
"""Get success mask at current timestep."""
curr_successes = torch.zeros((self.num_envs,), dtype=torch.bool, device=self.device)
# If nut is close enough to target pos
is_close = torch.where(self.nut_dist_to_target < self.thread_pitches.squeeze(-1),
torch.ones_like(curr_successes),
torch.zeros_like(curr_successes))
curr_successes = torch.logical_or(curr_successes, is_close)
return curr_successes
def _get_curr_failures(self, curr_successes):
"""Get failure mask at current timestep."""
curr_failures = torch.zeros((self.num_envs,), dtype=torch.bool, device=self.device)
# If max episode length has been reached
self.is_expired = torch.where(self.progress_buf[:] >= self.cfg_task.rl.max_episode_length,
torch.ones_like(curr_failures),
curr_failures)
# If nut is too far from target pos
self.is_far = torch.where(self.nut_dist_to_target > self.cfg_task.rl.far_error_thresh,
torch.ones_like(curr_failures),
curr_failures)
# If nut has slipped (distance-based definition)
self.is_slipped = \
torch.where(
self.nut_dist_to_fingerpads > self.asset_info_franka_table.franka_fingerpad_length * 0.5 + self.nut_heights.squeeze(-1) * 0.5,
torch.ones_like(curr_failures),
curr_failures)
self.is_slipped = torch.logical_and(self.is_slipped, torch.logical_not(curr_successes)) # ignore slip if successful
# If nut has fallen (i.e., if nut XY pos has drifted from center of bolt and nut Z pos has drifted below top of bolt)
self.is_fallen = torch.logical_and(
torch.norm(self.nut_com_pos[:, 0:2], p=2, dim=-1) > self.bolt_widths.squeeze(-1) * 0.5,
self.nut_com_pos[:, 2] < self.cfg_base.env.table_height + self.bolt_head_heights.squeeze(
-1) + self.bolt_shank_lengths.squeeze(-1) + self.nut_heights.squeeze(-1) * 0.5)
curr_failures = torch.logical_or(curr_failures, self.is_expired)
curr_failures = torch.logical_or(curr_failures, self.is_far)
curr_failures = torch.logical_or(curr_failures, self.is_slipped)
curr_failures = torch.logical_or(curr_failures, self.is_fallen)
return curr_failures
| 19,807 | Python | 50.183462 | 183 | 0.584238 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_task_nut_bolt_pick.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: Class for nut-bolt pick task.
Inherits nut-bolt environment class and abstract task class (not enforced). Can be executed with
python train.py task=FactoryTaskNutBoltPick
"""
import hydra
import omegaconf
import os
import torch
from isaacgym import gymapi, gymtorch
from isaacgymenvs.utils import torch_jit_utils as torch_utils
import isaacgymenvs.tasks.factory.factory_control as fc
from isaacgymenvs.tasks.factory.factory_env_nut_bolt import FactoryEnvNutBolt
from isaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from isaacgymenvs.tasks.factory.factory_schema_config_task import FactorySchemaConfigTask
from isaacgymenvs.utils import torch_jit_utils
class FactoryTaskNutBoltPick(FactoryEnvNutBolt, FactoryABCTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
"""Initialize instance variables. Initialize environment superclass."""
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
self.cfg = cfg
self._get_task_yaml_params()
self._acquire_task_tensors()
self.parse_controller_spec()
if self.cfg_task.sim.disable_gravity:
self.disable_gravity()
if self.viewer is not None:
self._set_viewer_params()
def _get_task_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name='factory_schema_config_task', node=FactorySchemaConfigTask)
self.cfg_task = omegaconf.OmegaConf.create(self.cfg)
self.max_episode_length = self.cfg_task.rl.max_episode_length # required instance var for VecTask
asset_info_path = '../../assets/factory/yaml/factory_asset_info_nut_bolt.yaml' # relative to Gym's Hydra search path (cfg dir)
self.asset_info_nut_bolt = hydra.compose(config_name=asset_info_path)
self.asset_info_nut_bolt = self.asset_info_nut_bolt['']['']['']['']['']['']['assets']['factory']['yaml'] # strip superfluous nesting
ppo_path = 'train/FactoryTaskNutBoltPickPPO.yaml' # relative to Gym's Hydra search path (cfg dir)
self.cfg_ppo = hydra.compose(config_name=ppo_path)
self.cfg_ppo = self.cfg_ppo['train'] # strip superfluous nesting
def _acquire_task_tensors(self):
"""Acquire tensors."""
# Grasp pose tensors
nut_grasp_heights = self.bolt_head_heights + self.nut_heights * 0.5 # nut COM
self.nut_grasp_pos_local = nut_grasp_heights * torch.tensor([0.0, 0.0, 1.0], device=self.device).repeat(
(self.num_envs, 1))
self.nut_grasp_quat_local = torch.tensor([0.0, 1.0, 0.0, 0.0], device=self.device).unsqueeze(0).repeat(
self.num_envs, 1)
# Keypoint tensors
self.keypoint_offsets = self._get_keypoint_offsets(
self.cfg_task.rl.num_keypoints) * self.cfg_task.rl.keypoint_scale
self.keypoints_gripper = torch.zeros((self.num_envs, self.cfg_task.rl.num_keypoints, 3),
dtype=torch.float32,
device=self.device)
self.keypoints_nut = torch.zeros_like(self.keypoints_gripper, device=self.device)
self.identity_quat = torch.tensor([0.0, 0.0, 0.0, 1.0], device=self.device).unsqueeze(0).repeat(self.num_envs,
1)
def _refresh_task_tensors(self):
"""Refresh tensors."""
# Compute pose of nut grasping frame
self.nut_grasp_quat, self.nut_grasp_pos = torch_jit_utils.tf_combine(self.nut_quat,
self.nut_pos,
self.nut_grasp_quat_local,
self.nut_grasp_pos_local)
# Compute pos of keypoints on gripper and nut in world frame
for idx, keypoint_offset in enumerate(self.keypoint_offsets):
self.keypoints_gripper[:, idx] = torch_jit_utils.tf_combine(self.fingertip_midpoint_quat,
self.fingertip_midpoint_pos,
self.identity_quat,
keypoint_offset.repeat(self.num_envs, 1))[1]
self.keypoints_nut[:, idx] = torch_jit_utils.tf_combine(self.nut_grasp_quat,
self.nut_grasp_pos,
self.identity_quat,
keypoint_offset.repeat(self.num_envs, 1))[1]
def pre_physics_step(self, actions):
"""Reset environments. Apply actions from policy. Simulation step called after this method."""
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.actions = actions.clone().to(self.device) # shape = (num_envs, num_actions); values = [-1, 1]
self._apply_actions_as_ctrl_targets(actions=self.actions,
ctrl_target_gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
do_scale=True)
def post_physics_step(self):
"""Step buffers. Refresh tensors. Compute observations and reward. Reset environments."""
self.progress_buf[:] += 1
# In this policy, episode length is constant
is_last_step = (self.progress_buf[0] == self.max_episode_length - 1)
if self.cfg_task.env.close_and_lift:
# At this point, robot has executed RL policy. Now close gripper and lift (open-loop)
if is_last_step:
self._close_gripper(sim_steps=self.cfg_task.env.num_gripper_close_sim_steps)
self._lift_gripper(sim_steps=self.cfg_task.env.num_gripper_lift_sim_steps)
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
self.compute_observations()
self.compute_reward()
def compute_observations(self):
"""Compute observations."""
# Shallow copies of tensors
obs_tensors = [self.fingertip_midpoint_pos,
self.fingertip_midpoint_quat,
self.fingertip_midpoint_linvel,
self.fingertip_midpoint_angvel,
self.nut_grasp_pos,
self.nut_grasp_quat]
self.obs_buf = torch.cat(obs_tensors, dim=-1) # shape = (num_envs, num_observations)
return self.obs_buf
def compute_reward(self):
"""Update reward and reset buffers."""
self._update_reset_buf()
self._update_rew_buf()
def _update_reset_buf(self):
"""Assign environments for reset if successful or failed."""
# If max episode length has been reached
self.reset_buf[:] = torch.where(self.progress_buf[:] >= self.max_episode_length - 1,
torch.ones_like(self.reset_buf),
self.reset_buf)
def _update_rew_buf(self):
"""Compute reward at current timestep."""
keypoint_reward = -self._get_keypoint_dist()
action_penalty = torch.norm(self.actions, p=2, dim=-1) * self.cfg_task.rl.action_penalty_scale
self.rew_buf[:] = keypoint_reward * self.cfg_task.rl.keypoint_reward_scale \
- action_penalty * self.cfg_task.rl.action_penalty_scale
# In this policy, episode length is constant across all envs
is_last_step = (self.progress_buf[0] == self.max_episode_length - 1)
if is_last_step:
# Check if nut is picked up and above table
lift_success = self._check_lift_success(height_multiple=3.0)
self.rew_buf[:] += lift_success * self.cfg_task.rl.success_bonus
self.extras['successes'] = torch.mean(lift_success.float())
def reset_idx(self, env_ids):
"""Reset specified environments."""
self._reset_franka(env_ids)
self._reset_object(env_ids)
self._randomize_gripper_pose(env_ids, sim_steps=self.cfg_task.env.num_gripper_move_sim_steps)
self._reset_buffers(env_ids)
def _reset_franka(self, env_ids):
"""Reset DOF states and DOF targets of Franka."""
self.dof_pos[env_ids] = torch.cat(
(torch.tensor(self.cfg_task.randomize.franka_arm_initial_dof_pos, device=self.device),
torch.tensor([self.asset_info_franka_table.franka_gripper_width_max], device=self.device),
torch.tensor([self.asset_info_franka_table.franka_gripper_width_max], device=self.device)),
dim=-1).unsqueeze(0).repeat((self.num_envs, 1)) # shape = (num_envs, num_dofs)
self.dof_vel[env_ids] = 0.0 # shape = (num_envs, num_dofs)
self.ctrl_target_dof_pos[env_ids] = self.dof_pos[env_ids]
multi_env_ids_int32 = self.franka_actor_ids_sim[env_ids].flatten()
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(multi_env_ids_int32),
len(multi_env_ids_int32))
def _reset_object(self, env_ids):
"""Reset root states of nut and bolt."""
# shape of root_pos = (num_envs, num_actors, 3)
# shape of root_quat = (num_envs, num_actors, 4)
# shape of root_linvel = (num_envs, num_actors, 3)
# shape of root_angvel = (num_envs, num_actors, 3)
# Randomize root state of nut
nut_noise_xy = 2 * (torch.rand((self.num_envs, 2), dtype=torch.float32, device=self.device) - 0.5) # [-1, 1]
nut_noise_xy = nut_noise_xy @ torch.diag(
torch.tensor(self.cfg_task.randomize.nut_pos_xy_initial_noise, device=self.device))
self.root_pos[env_ids, self.nut_actor_id_env, 0] = self.cfg_task.randomize.nut_pos_xy_initial[0] + nut_noise_xy[
env_ids, 0]
self.root_pos[env_ids, self.nut_actor_id_env, 1] = self.cfg_task.randomize.nut_pos_xy_initial[1] + nut_noise_xy[
env_ids, 1]
self.root_pos[
env_ids, self.nut_actor_id_env, 2] = self.cfg_base.env.table_height - self.bolt_head_heights.squeeze(-1)
self.root_quat[env_ids, self.nut_actor_id_env] = torch.tensor([0.0, 0.0, 0.0, 1.0], dtype=torch.float32,
device=self.device).repeat(len(env_ids), 1)
self.root_linvel[env_ids, self.nut_actor_id_env] = 0.0
self.root_angvel[env_ids, self.nut_actor_id_env] = 0.0
# Randomize root state of bolt
bolt_noise_xy = 2 * (torch.rand((self.num_envs, 2), dtype=torch.float32, device=self.device) - 0.5) # [-1, 1]
bolt_noise_xy = bolt_noise_xy @ torch.diag(
torch.tensor(self.cfg_task.randomize.bolt_pos_xy_noise, device=self.device))
self.root_pos[env_ids, self.bolt_actor_id_env, 0] = self.cfg_task.randomize.bolt_pos_xy_initial[0] + \
bolt_noise_xy[env_ids, 0]
self.root_pos[env_ids, self.bolt_actor_id_env, 1] = self.cfg_task.randomize.bolt_pos_xy_initial[1] + \
bolt_noise_xy[env_ids, 1]
self.root_pos[env_ids, self.bolt_actor_id_env, 2] = self.cfg_base.env.table_height
self.root_quat[env_ids, self.bolt_actor_id_env] = torch.tensor([0.0, 0.0, 0.0, 1.0], dtype=torch.float32,
device=self.device).repeat(len(env_ids), 1)
self.root_linvel[env_ids, self.bolt_actor_id_env] = 0.0
self.root_angvel[env_ids, self.bolt_actor_id_env] = 0.0
nut_bolt_actor_ids_sim = torch.cat((self.nut_actor_ids_sim[env_ids],
self.bolt_actor_ids_sim[env_ids]),
dim=0)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state),
gymtorch.unwrap_tensor(nut_bolt_actor_ids_sim),
len(nut_bolt_actor_ids_sim))
def _reset_buffers(self, env_ids):
"""Reset buffers."""
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def _set_viewer_params(self):
"""Set viewer parameters."""
cam_pos = gymapi.Vec3(-1.0, -1.0, 1.0)
cam_target = gymapi.Vec3(0.0, 0.0, 0.5)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
def _apply_actions_as_ctrl_targets(self, actions, ctrl_target_gripper_dof_pos, do_scale):
"""Apply actions from policy as position/rotation targets."""
# Interpret actions as target pos displacements and set pos target
pos_actions = actions[:, 0:3]
if do_scale:
pos_actions = pos_actions @ torch.diag(torch.tensor(self.cfg_task.rl.pos_action_scale, device=self.device))
self.ctrl_target_fingertip_midpoint_pos = self.fingertip_midpoint_pos + pos_actions
# Interpret actions as target rot (axis-angle) displacements
rot_actions = actions[:, 3:6]
if do_scale:
rot_actions = rot_actions @ torch.diag(torch.tensor(self.cfg_task.rl.rot_action_scale, device=self.device))
# Convert to quat and set rot target
angle = torch.norm(rot_actions, p=2, dim=-1)
axis = rot_actions / angle.unsqueeze(-1)
rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis)
if self.cfg_task.rl.clamp_rot:
rot_actions_quat = torch.where(angle.unsqueeze(-1).repeat(1, 4) > self.cfg_task.rl.clamp_rot_thresh,
rot_actions_quat,
torch.tensor([0.0, 0.0, 0.0, 1.0], device=self.device).repeat(self.num_envs,
1))
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(rot_actions_quat, self.fingertip_midpoint_quat)
if self.cfg_ctrl['do_force_ctrl']:
# Interpret actions as target forces and target torques
force_actions = actions[:, 6:9]
if do_scale:
force_actions = force_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.force_action_scale, device=self.device))
torque_actions = actions[:, 9:12]
if do_scale:
torque_actions = torque_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.torque_action_scale, device=self.device))
self.ctrl_target_fingertip_contact_wrench = torch.cat((force_actions, torque_actions), dim=-1)
self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos
self.generate_ctrl_signals()
def _get_keypoint_offsets(self, num_keypoints):
"""Get uniformly-spaced keypoints along a line of unit length, centered at 0."""
keypoint_offsets = torch.zeros((num_keypoints, 3), device=self.device)
keypoint_offsets[:, -1] = torch.linspace(0.0, 1.0, num_keypoints, device=self.device) - 0.5
return keypoint_offsets
def _get_keypoint_dist(self):
"""Get keypoint distance."""
keypoint_dist = torch.sum(torch.norm(self.keypoints_nut - self.keypoints_gripper, p=2, dim=-1), dim=-1)
return keypoint_dist
def _close_gripper(self, sim_steps=20):
"""Fully close gripper using controller. Called outside RL loop (i.e., after last step of episode)."""
self._move_gripper_to_dof_pos(gripper_dof_pos=0.0, sim_steps=sim_steps)
def _move_gripper_to_dof_pos(self, gripper_dof_pos, sim_steps=20):
"""Move gripper fingers to specified DOF position using controller."""
delta_hand_pose = torch.zeros((self.num_envs, self.cfg_task.env.numActions),
device=self.device) # No hand motion
self._apply_actions_as_ctrl_targets(delta_hand_pose, gripper_dof_pos, do_scale=False)
# Step sim
for _ in range(sim_steps):
self.render()
self.gym.simulate(self.sim)
def _lift_gripper(self, franka_gripper_width=0.0, lift_distance=0.3, sim_steps=20):
"""Lift gripper by specified distance. Called outside RL loop (i.e., after last step of episode)."""
delta_hand_pose = torch.zeros([self.num_envs, 6], device=self.device)
delta_hand_pose[:, 2] = lift_distance
# Step sim
for _ in range(sim_steps):
self._apply_actions_as_ctrl_targets(delta_hand_pose, franka_gripper_width, do_scale=False)
self.render()
self.gym.simulate(self.sim)
def _check_lift_success(self, height_multiple):
"""Check if nut is above table by more than specified multiple times height of nut."""
lift_success = torch.where(
self.nut_pos[:, 2] > self.cfg_base.env.table_height + self.nut_heights.squeeze(-1) * height_multiple,
torch.ones((self.num_envs,), device=self.device),
torch.zeros((self.num_envs,), device=self.device))
return lift_success
def _randomize_gripper_pose(self, env_ids, sim_steps):
"""Move gripper to random pose."""
# Set target pos above table
self.ctrl_target_fingertip_midpoint_pos = \
torch.tensor([0.0, 0.0, self.cfg_base.env.table_height], device=self.device) \
+ torch.tensor(self.cfg_task.randomize.fingertip_midpoint_pos_initial, device=self.device)
self.ctrl_target_fingertip_midpoint_pos = self.ctrl_target_fingertip_midpoint_pos.unsqueeze(0).repeat(self.num_envs, 1)
fingertip_midpoint_pos_noise = \
2 * (torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) - 0.5) # [-1, 1]
fingertip_midpoint_pos_noise = \
fingertip_midpoint_pos_noise @ torch.diag(torch.tensor(self.cfg_task.randomize.fingertip_midpoint_pos_noise,
device=self.device))
self.ctrl_target_fingertip_midpoint_pos += fingertip_midpoint_pos_noise
# Set target rot
ctrl_target_fingertip_midpoint_euler = torch.tensor(self.cfg_task.randomize.fingertip_midpoint_rot_initial,
device=self.device).unsqueeze(0).repeat(self.num_envs, 1)
fingertip_midpoint_rot_noise = \
2 * (torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device) - 0.5) # [-1, 1]
fingertip_midpoint_rot_noise = fingertip_midpoint_rot_noise @ torch.diag(
torch.tensor(self.cfg_task.randomize.fingertip_midpoint_rot_noise, device=self.device))
ctrl_target_fingertip_midpoint_euler += fingertip_midpoint_rot_noise
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz(
ctrl_target_fingertip_midpoint_euler[:, 0],
ctrl_target_fingertip_midpoint_euler[:, 1],
ctrl_target_fingertip_midpoint_euler[:, 2])
# Step sim and render
for _ in range(sim_steps):
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
pos_error, axis_angle_error = fc.get_pose_error(
fingertip_midpoint_pos=self.fingertip_midpoint_pos,
fingertip_midpoint_quat=self.fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
jacobian_type=self.cfg_ctrl['jacobian_type'],
rot_error_type='axis_angle')
delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1)
actions = torch.zeros((self.num_envs, self.cfg_task.env.numActions), device=self.device)
actions[:, :6] = delta_hand_pose
self._apply_actions_as_ctrl_targets(actions=actions,
ctrl_target_gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
do_scale=False)
self.gym.simulate(self.sim)
self.render()
self.dof_vel[env_ids, :] = torch.zeros_like(self.dof_vel[env_ids])
# Set DOF state
multi_env_ids_int32 = self.franka_actor_ids_sim[env_ids].flatten()
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(multi_env_ids_int32),
len(multi_env_ids_int32))
| 23,069 | Python | 50.039823 | 141 | 0.593654 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_schema_class_base.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: abstract base class for base class.
Inherits ABC class. Inherited by base class. Defines template for base class.
"""
from abc import ABC, abstractmethod
class FactoryABCBase(ABC):
@abstractmethod
def __init__(self):
"""Initialize instance variables. Initialize VecTask superclass."""
pass
@abstractmethod
def _get_base_yaml_params(self):
"""Initialize instance variables from YAML files."""
pass
@abstractmethod
def create_sim(self):
"""Set sim and PhysX params. Create sim object, ground plane, and envs."""
pass
@abstractmethod
def _create_ground_plane(self):
"""Set ground plane params. Add plane."""
pass
@abstractmethod
def import_franka_assets(self):
"""Set Franka and table asset options. Import assets."""
pass
@abstractmethod
def acquire_base_tensors(self):
"""Acquire and wrap tensors. Create views."""
pass
@abstractmethod
def refresh_base_tensors(self):
"""Refresh tensors."""
# NOTE: Tensor refresh functions should be called once per step, before setters.
pass
@abstractmethod
def parse_controller_spec(self):
"""Parse controller specification into lower-level controller configuration."""
pass
@abstractmethod
def generate_ctrl_signals(self):
"""Get Jacobian. Set Franka DOF position targets or DOF torques."""
pass
@abstractmethod
def enable_gravity(self):
"""Enable gravity."""
pass
@abstractmethod
def disable_gravity(self):
"""Disable gravity."""
pass
@abstractmethod
def export_scene(self):
"""Export scene to USD."""
pass
@abstractmethod
def extract_poses(self):
"""Extract poses of all bodies."""
pass
| 3,432 | Python | 32.330097 | 88 | 0.697552 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_task_insertion.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: Class for insertion task.
Inherits insertion environment class and abstract task class (not enforced). Can be executed with
python train.py task=FactoryTaskInsertion
Only the environment is provided; training a successful RL policy is an open research problem left to the user.
"""
import hydra
import math
import omegaconf
import os
import torch
from isaacgym import gymapi, gymtorch
from isaacgymenvs.tasks.factory.factory_env_insertion import FactoryEnvInsertion
from isaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from isaacgymenvs.tasks.factory.factory_schema_config_task import FactorySchemaConfigTask
class FactoryTaskInsertion(FactoryEnvInsertion, FactoryABCTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
"""Initialize instance variables. Initialize task superclass."""
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
self.cfg = cfg
self._get_task_yaml_params()
if self.viewer != None:
self._set_viewer_params()
if self.cfg_base.mode.export_scene:
self.export_scene(label='franka_task_insertion')
def _get_task_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name='factory_schema_config_task', node=FactorySchemaConfigTask)
self.cfg_task = omegaconf.OmegaConf.create(self.cfg)
self.max_episode_length = self.cfg_task.rl.max_episode_length # required instance var for VecTask
asset_info_path = '../../assets/factory/yaml/factory_asset_info_insertion.yaml' # relative to Gym's Hydra search path (cfg dir)
self.asset_info_insertion = hydra.compose(config_name=asset_info_path)
self.asset_info_insertion = self.asset_info_insertion['']['']['']['']['']['']['assets']['factory']['yaml'] # strip superfluous nesting
ppo_path = 'train/FactoryTaskInsertionPPO.yaml' # relative to Gym's Hydra search path (cfg dir)
self.cfg_ppo = hydra.compose(config_name=ppo_path)
self.cfg_ppo = self.cfg_ppo['train'] # strip superfluous nesting
def _acquire_task_tensors(self):
"""Acquire tensors."""
pass
def _refresh_task_tensors(self):
"""Refresh tensors."""
pass
def pre_physics_step(self, actions):
"""Reset environments. Apply actions from policy as position/rotation targets, force/torque targets, and/or PD gains."""
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self._actions = actions.clone().to(self.device) # shape = (num_envs, num_actions); values = [-1, 1]
def post_physics_step(self):
"""Step buffers. Refresh tensors. Compute observations and reward."""
self.progress_buf[:] += 1
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
self.compute_observations()
self.compute_reward()
def compute_observations(self):
"""Compute observations."""
return self.obs_buf # shape = (num_envs, num_observations)
def compute_reward(self):
"""Detect successes and failures. Update reward and reset buffers."""
self._update_rew_buf()
self._update_reset_buf()
def _update_rew_buf(self):
"""Compute reward at current timestep."""
pass
def _update_reset_buf(self):
"""Assign environments for reset if successful or failed."""
pass
def reset_idx(self, env_ids):
"""Reset specified environments."""
self._reset_franka(env_ids)
self._reset_object(env_ids)
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def _reset_franka(self, env_ids):
"""Reset DOF states and DOF targets of Franka."""
# shape of dof_pos = (num_envs, num_dofs)
# shape of dof_vel = (num_envs, num_dofs)
# Initialize Franka to middle of joint limits, plus joint noise
franka_dof_props = self.gym.get_actor_dof_properties(self.env_ptrs[0],
self.franka_handles[0]) # same across all envs
lower_lims = franka_dof_props['lower']
upper_lims = franka_dof_props['upper']
self.dof_pos[:, 0:self.franka_num_dofs] = torch.tensor((lower_lims + upper_lims) * 0.5, device=self.device) \
+ (torch.rand((self.num_envs, 1),
device=self.device) * 2.0 - 1.0) * self.cfg_task.randomize.joint_noise * math.pi / 180
self.dof_vel[env_ids, 0:self.franka_num_dofs] = 0.0
franka_actor_ids_sim_int32 = self.franka_actor_ids_sim.to(dtype=torch.int32, device=self.device)[env_ids]
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(franka_actor_ids_sim_int32),
len(franka_actor_ids_sim_int32))
self.ctrl_target_dof_pos[env_ids, 0:self.franka_num_dofs] = self.dof_pos[env_ids, 0:self.franka_num_dofs]
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(self.ctrl_target_dof_pos))
def _reset_object(self, env_ids):
"""Reset root state of plug."""
# shape of root_pos = (num_envs, num_actors, 3)
# shape of root_quat = (num_envs, num_actors, 4)
# shape of root_linvel = (num_envs, num_actors, 3)
# shape of root_angvel = (num_envs, num_actors, 3)
if self.cfg_task.randomize.initial_state == 'random':
self.root_pos[env_ids, self.plug_actor_id_env] = \
torch.cat(((torch.rand((self.num_envs, 1), device=self.device) * 2.0 - 1.0) * self.cfg_task.randomize.plug_noise_xy,
self.cfg_task.randomize.plug_bias_y + (torch.rand((self.num_envs, 1), device=self.device) * 2.0 - 1.0) * self.cfg_task.randomize.plug_noise_xy,
torch.ones((self.num_envs, 1), device=self.device) * (self.cfg_base.env.table_height + self.cfg_task.randomize.plug_bias_z)), dim=1)
elif self.cfg_task.randomize.initial_state == 'goal':
self.root_pos[env_ids, self.plug_actor_id_env] = torch.tensor([0.0, 0.0, self.cfg_base.env.table_height],
device=self.device)
self.root_linvel[env_ids, self.plug_actor_id_env] = 0.0
self.root_angvel[env_ids, self.plug_actor_id_env] = 0.0
plug_actor_ids_sim_int32 = self.plug_actor_ids_sim.to(dtype=torch.int32, device=self.device)
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state),
gymtorch.unwrap_tensor(plug_actor_ids_sim_int32[env_ids]),
len(plug_actor_ids_sim_int32[env_ids]))
def _reset_buffers(self, env_ids):
"""Reset buffers. """
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def _set_viewer_params(self):
"""Set viewer parameters."""
cam_pos = gymapi.Vec3(-1.0, -1.0, 1.0)
cam_target = gymapi.Vec3(0.0, 0.0, 0.5)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
| 9,283 | Python | 45.42 | 170 | 0.636971 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_env_insertion.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: class for insertion env.
Inherits base class and abstract environment class. Inherited by insertion task class. Not directly executed.
Configuration defined in FactoryEnvInsertion.yaml. Asset info defined in factory_asset_info_insertion.yaml.
"""
import hydra
import numpy as np
import os
import torch
from isaacgym import gymapi
from isaacgymenvs.tasks.factory.factory_base import FactoryBase
from isaacgymenvs.tasks.factory.factory_schema_class_env import FactoryABCEnv
from isaacgymenvs.tasks.factory.factory_schema_config_env import FactorySchemaConfigEnv
class FactoryEnvInsertion(FactoryBase, FactoryABCEnv):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
"""Initialize instance variables. Initialize environment superclass. Acquire tensors."""
self._get_env_yaml_params()
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
self.acquire_base_tensors() # defined in superclass
self._acquire_env_tensors()
self.refresh_base_tensors() # defined in superclass
self.refresh_env_tensors()
def _get_env_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name='factory_schema_config_env', node=FactorySchemaConfigEnv)
config_path = 'task/FactoryEnvInsertion.yaml' # relative to Gym's Hydra search path (cfg dir)
self.cfg_env = hydra.compose(config_name=config_path)
self.cfg_env = self.cfg_env['task'] # strip superfluous nesting
asset_info_path = '../../assets/factory/yaml/factory_asset_info_insertion.yaml' # relative to Gym's Hydra search path (cfg dir)
self.asset_info_insertion = hydra.compose(config_name=asset_info_path)
self.asset_info_insertion = self.asset_info_insertion['']['']['']['']['']['']['assets']['factory']['yaml'] # strip superfluous nesting
def create_envs(self):
"""Set env options. Import assets. Create actors."""
lower = gymapi.Vec3(-self.cfg_base.env.env_spacing, -self.cfg_base.env.env_spacing, 0.0)
upper = gymapi.Vec3(self.cfg_base.env.env_spacing, self.cfg_base.env.env_spacing, self.cfg_base.env.env_spacing)
num_per_row = int(np.sqrt(self.num_envs))
self.print_sdf_warning()
franka_asset, table_asset = self.import_franka_assets()
plug_assets, socket_assets = self._import_env_assets()
self._create_actors(lower, upper, num_per_row, franka_asset, plug_assets, socket_assets, table_asset)
def _import_env_assets(self):
"""Set plug and socket asset options. Import assets."""
urdf_root = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'assets', 'factory', 'urdf')
plug_options = gymapi.AssetOptions()
plug_options.flip_visual_attachments = False
plug_options.fix_base_link = False
plug_options.thickness = 0.0 # default = 0.02
plug_options.armature = 0.0 # default = 0.0
plug_options.use_physx_armature = True
plug_options.linear_damping = 0.0 # default = 0.0
plug_options.max_linear_velocity = 1000.0 # default = 1000.0
plug_options.angular_damping = 0.0 # default = 0.5
plug_options.max_angular_velocity = 64.0 # default = 64.0
plug_options.disable_gravity = False
plug_options.enable_gyroscopic_forces = True
plug_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
plug_options.use_mesh_materials = False
if self.cfg_base.mode.export_scene:
plug_options.mesh_normal_mode = gymapi.COMPUTE_PER_FACE
socket_options = gymapi.AssetOptions()
socket_options.flip_visual_attachments = False
socket_options.fix_base_link = True
socket_options.thickness = 0.0 # default = 0.02
socket_options.armature = 0.0 # default = 0.0
socket_options.use_physx_armature = True
socket_options.linear_damping = 0.0 # default = 0.0
socket_options.max_linear_velocity = 1000.0 # default = 1000.0
socket_options.angular_damping = 0.0 # default = 0.5
socket_options.max_angular_velocity = 64.0 # default = 64.0
socket_options.disable_gravity = False
socket_options.enable_gyroscopic_forces = True
socket_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
socket_options.use_mesh_materials = False
if self.cfg_base.mode.export_scene:
socket_options.mesh_normal_mode = gymapi.COMPUTE_PER_FACE
plug_assets = []
socket_assets = []
for subassembly in self.cfg_env.env.desired_subassemblies:
components = list(self.asset_info_insertion[subassembly])
plug_file = self.asset_info_insertion[subassembly][components[0]]['urdf_path'] + '.urdf'
socket_file = self.asset_info_insertion[subassembly][components[1]]['urdf_path'] + '.urdf'
plug_options.density = self.asset_info_insertion[subassembly][components[0]]['density']
socket_options.density = self.asset_info_insertion[subassembly][components[1]]['density']
plug_asset = self.gym.load_asset(self.sim, urdf_root, plug_file, plug_options)
socket_asset = self.gym.load_asset(self.sim, urdf_root, socket_file, socket_options)
plug_assets.append(plug_asset)
socket_assets.append(socket_asset)
return plug_assets, socket_assets
def _create_actors(self, lower, upper, num_per_row, franka_asset, plug_assets, socket_assets, table_asset):
"""Set initial actor poses. Create actors. Set shape and DOF properties."""
franka_pose = gymapi.Transform()
franka_pose.p.x = self.cfg_base.env.franka_depth
franka_pose.p.y = 0.0
franka_pose.p.z = 0.0
franka_pose.r = gymapi.Quat(0.0, 0.0, 1.0, 0.0)
table_pose = gymapi.Transform()
table_pose.p.x = 0.0
table_pose.p.y = 0.0
table_pose.p.z = self.cfg_base.env.table_height * 0.5
table_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
self.env_ptrs = []
self.franka_handles = []
self.plug_handles = []
self.socket_handles = []
self.table_handles = []
self.shape_ids = []
self.franka_actor_ids_sim = [] # within-sim indices
self.plug_actor_ids_sim = [] # within-sim indices
self.socket_actor_ids_sim = [] # within-sim indices
self.table_actor_ids_sim = [] # within-sim indices
actor_count = 0
for i in range(self.num_envs):
env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row)
if self.cfg_env.sim.disable_franka_collisions:
franka_handle = self.gym.create_actor(env_ptr, franka_asset, franka_pose, 'franka', i + self.num_envs,
0, 0)
else:
franka_handle = self.gym.create_actor(env_ptr, franka_asset, franka_pose, 'franka', i, 0, 0)
self.franka_actor_ids_sim.append(actor_count)
actor_count += 1
j = np.random.randint(0, len(self.cfg_env.env.desired_subassemblies))
subassembly = self.cfg_env.env.desired_subassemblies[j]
components = list(self.asset_info_insertion[subassembly])
plug_pose = gymapi.Transform()
plug_pose.p.x = 0.0
plug_pose.p.y = self.cfg_env.env.plug_lateral_offset
plug_pose.p.z = self.cfg_base.env.table_height
plug_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
plug_handle = self.gym.create_actor(env_ptr, plug_assets[j], plug_pose, 'plug', i, 0, 0)
self.plug_actor_ids_sim.append(actor_count)
actor_count += 1
socket_pose = gymapi.Transform()
socket_pose.p.x = 0.0
socket_pose.p.y = 0.0
socket_pose.p.z = self.cfg_base.env.table_height
socket_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
socket_handle = self.gym.create_actor(env_ptr, socket_assets[j], socket_pose, 'socket', i, 0, 0)
self.socket_actor_ids_sim.append(actor_count)
actor_count += 1
table_handle = self.gym.create_actor(env_ptr, table_asset, table_pose, 'table', i, 0, 0)
self.table_actor_ids_sim.append(actor_count)
actor_count += 1
link7_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_link7', gymapi.DOMAIN_ACTOR)
hand_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_hand', gymapi.DOMAIN_ACTOR)
left_finger_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_leftfinger',
gymapi.DOMAIN_ACTOR)
right_finger_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_rightfinger',
gymapi.DOMAIN_ACTOR)
self.shape_ids = [link7_id, hand_id, left_finger_id, right_finger_id]
franka_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, franka_handle)
for shape_id in self.shape_ids:
franka_shape_props[shape_id].friction = self.cfg_base.env.franka_friction
franka_shape_props[shape_id].rolling_friction = 0.0 # default = 0.0
franka_shape_props[shape_id].torsion_friction = 0.0 # default = 0.0
franka_shape_props[shape_id].restitution = 0.0 # default = 0.0
franka_shape_props[shape_id].compliance = 0.0 # default = 0.0
franka_shape_props[shape_id].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, franka_handle, franka_shape_props)
plug_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, plug_handle)
plug_shape_props[0].friction = self.asset_info_insertion[subassembly][components[0]]['friction']
plug_shape_props[0].rolling_friction = 0.0 # default = 0.0
plug_shape_props[0].torsion_friction = 0.0 # default = 0.0
plug_shape_props[0].restitution = 0.0 # default = 0.0
plug_shape_props[0].compliance = 0.0 # default = 0.0
plug_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, plug_handle, plug_shape_props)
socket_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, socket_handle)
socket_shape_props[0].friction = self.asset_info_insertion[subassembly][components[1]]['friction']
socket_shape_props[0].rolling_friction = 0.0 # default = 0.0
socket_shape_props[0].torsion_friction = 0.0 # default = 0.0
socket_shape_props[0].restitution = 0.0 # default = 0.0
socket_shape_props[0].compliance = 0.0 # default = 0.0
socket_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, socket_handle, socket_shape_props)
table_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, table_handle)
table_shape_props[0].friction = self.cfg_base.env.table_friction
table_shape_props[0].rolling_friction = 0.0 # default = 0.0
table_shape_props[0].torsion_friction = 0.0 # default = 0.0
table_shape_props[0].restitution = 0.0 # default = 0.0
table_shape_props[0].compliance = 0.0 # default = 0.0
table_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, table_handle, table_shape_props)
self.franka_num_dofs = self.gym.get_actor_dof_count(env_ptr, franka_handle)
self.gym.enable_actor_dof_force_sensors(env_ptr, franka_handle)
self.env_ptrs.append(env_ptr)
self.franka_handles.append(franka_handle)
self.plug_handles.append(plug_handle)
self.socket_handles.append(socket_handle)
self.table_handles.append(table_handle)
self.num_actors = int(actor_count / self.num_envs) # per env
self.num_bodies = self.gym.get_env_rigid_body_count(env_ptr) # per env
self.num_dofs = self.gym.get_env_dof_count(env_ptr) # per env
# For setting targets
self.franka_actor_ids_sim = torch.tensor(self.franka_actor_ids_sim, dtype=torch.int32, device=self.device)
self.plug_actor_ids_sim = torch.tensor(self.plug_actor_ids_sim, dtype=torch.int32, device=self.device)
self.socket_actor_ids_sim = torch.tensor(self.socket_actor_ids_sim, dtype=torch.int32, device=self.device)
# For extracting root pos/quat
self.plug_actor_id_env = self.gym.find_actor_index(env_ptr, 'plug', gymapi.DOMAIN_ENV)
self.socket_actor_id_env = self.gym.find_actor_index(env_ptr, 'socket', gymapi.DOMAIN_ENV)
# For extracting body pos/quat, force, and Jacobian
self.plug_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, plug_handle, 'plug', gymapi.DOMAIN_ENV)
self.socket_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, socket_handle, 'socket',
gymapi.DOMAIN_ENV)
self.hand_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_hand',
gymapi.DOMAIN_ENV)
self.left_finger_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_leftfinger',
gymapi.DOMAIN_ENV)
self.right_finger_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle,
'panda_rightfinger', gymapi.DOMAIN_ENV)
self.fingertip_centered_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle,
'panda_fingertip_centered',
gymapi.DOMAIN_ENV)
def _acquire_env_tensors(self):
"""Acquire and wrap tensors. Create views."""
self.plug_pos = self.root_pos[:, self.plug_actor_id_env, 0:3]
self.plug_quat = self.root_quat[:, self.plug_actor_id_env, 0:4]
self.plug_linvel = self.root_linvel[:, self.plug_actor_id_env, 0:3]
self.plug_angvel = self.root_angvel[:, self.plug_actor_id_env, 0:3]
self.socket_pos = self.root_pos[:, self.socket_actor_id_env, 0:3]
self.socket_quat = self.root_quat[:, self.socket_actor_id_env, 0:4]
# TODO: Define socket height and plug height params in asset info YAML.
# self.plug_com_pos = self.translate_along_local_z(pos=self.plug_pos,
# quat=self.plug_quat,
# offset=self.socket_heights + self.plug_heights * 0.5,
# device=self.device)
self.plug_com_quat = self.plug_quat # always equal
# self.plug_com_linvel = self.plug_linvel + torch.cross(self.plug_angvel,
# (self.plug_com_pos - self.plug_pos),
# dim=1)
self.plug_com_angvel = self.plug_angvel # always equal
def refresh_env_tensors(self):
"""Refresh tensors."""
# NOTE: Tensor refresh functions should be called once per step, before setters.
# TODO: Define socket height and plug height params in asset info YAML.
# self.plug_com_pos = self.translate_along_local_z(pos=self.plug_pos,
# quat=self.plug_quat,
# offset=self.socket_heights + self.plug_heights * 0.5,
# device=self.device)
# self.plug_com_linvel = self.plug_linvel + torch.cross(self.plug_angvel,
# (self.plug_com_pos - self.plug_pos),
# dim=1)
| 18,207 | Python | 55.722741 | 143 | 0.612512 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_schema_config_base.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: schema for base class configuration.
Used by Hydra. Defines template for base class YAML file.
"""
from dataclasses import dataclass
@dataclass
class Mode:
export_scene: bool # export scene to USD
export_states: bool # export states to NPY
@dataclass
class PhysX:
solver_type: int # default = 1 (Temporal Gauss-Seidel)
num_threads: int
num_subscenes: int
use_gpu: bool
num_position_iterations: int # number of position iterations for solver (default = 4)
num_velocity_iterations: int # number of velocity iterations for solver (default = 1)
contact_offset: float # default = 0.02
rest_offset: float # default = 0.001
bounce_threshold_velocity: float # default = 0.01
max_depenetration_velocity: float # default = 100.0
friction_offset_threshold: float # default = 0.04
friction_correlation_distance: float # default = 0.025
max_gpu_contact_pairs: int # default = 1024 * 1024
default_buffer_size_multiplier: float
contact_collection: int # 0: CC_NEVER (do not collect contact info), 1: CC_LAST_SUBSTEP (collect contact info on last substep), 2: CC_ALL_SUBSTEPS (collect contact info at all substeps)
@dataclass
class Sim:
dt: float # timestep size (default = 1.0 / 60.0)
num_substeps: int # number of substeps (default = 2)
up_axis: str
use_gpu_pipeline: bool
gravity: list # gravitational acceleration vector
add_damping: bool # add damping to stabilize gripper-object interactions
physx: PhysX
@dataclass
class Env:
env_spacing: float # lateral offset between envs
franka_depth: float # depth offset of Franka base relative to env origin
table_height: float # height of table
franka_friction: float # coefficient of friction associated with Franka
table_friction: float # coefficient of friction associated with table
@dataclass
class FactorySchemaConfigBase:
mode: Mode
sim: Sim
env: Env
| 3,523 | Python | 39.505747 | 190 | 0.741981 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_env_nut_bolt.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: class for nut-bolt env.
Inherits base class and abstract environment class. Inherited by nut-bolt task classes. Not directly executed.
Configuration defined in FactoryEnvNutBolt.yaml. Asset info defined in factory_asset_info_nut_bolt.yaml.
"""
import hydra
import numpy as np
import os
import torch
from isaacgym import gymapi
from isaacgymenvs.tasks.factory.factory_base import FactoryBase
import isaacgymenvs.tasks.factory.factory_control as fc
from isaacgymenvs.tasks.factory.factory_schema_class_env import FactoryABCEnv
from isaacgymenvs.tasks.factory.factory_schema_config_env import FactorySchemaConfigEnv
class FactoryEnvNutBolt(FactoryBase, FactoryABCEnv):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
"""Initialize instance variables. Initialize environment superclass. Acquire tensors."""
self._get_env_yaml_params()
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
self.acquire_base_tensors() # defined in superclass
self._acquire_env_tensors()
self.refresh_base_tensors() # defined in superclass
self.refresh_env_tensors()
def _get_env_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name='factory_schema_config_env', node=FactorySchemaConfigEnv)
config_path = 'task/FactoryEnvNutBolt.yaml' # relative to Hydra search path (cfg dir)
self.cfg_env = hydra.compose(config_name=config_path)
self.cfg_env = self.cfg_env['task'] # strip superfluous nesting
asset_info_path = '../../assets/factory/yaml/factory_asset_info_nut_bolt.yaml'
self.asset_info_nut_bolt = hydra.compose(config_name=asset_info_path)
self.asset_info_nut_bolt = self.asset_info_nut_bolt['']['']['']['']['']['']['assets']['factory']['yaml'] # strip superfluous nesting
def create_envs(self):
"""Set env options. Import assets. Create actors."""
lower = gymapi.Vec3(-self.cfg_base.env.env_spacing, -self.cfg_base.env.env_spacing, 0.0)
upper = gymapi.Vec3(self.cfg_base.env.env_spacing, self.cfg_base.env.env_spacing, self.cfg_base.env.env_spacing)
num_per_row = int(np.sqrt(self.num_envs))
self.print_sdf_warning()
franka_asset, table_asset = self.import_franka_assets()
nut_asset, bolt_asset = self._import_env_assets()
self._create_actors(lower, upper, num_per_row, franka_asset, nut_asset, bolt_asset, table_asset)
def _import_env_assets(self):
"""Set nut and bolt asset options. Import assets."""
urdf_root = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'assets', 'factory', 'urdf')
nut_options = gymapi.AssetOptions()
nut_options.flip_visual_attachments = False
nut_options.fix_base_link = False
nut_options.thickness = 0.0 # default = 0.02
nut_options.armature = 0.0 # default = 0.0
nut_options.use_physx_armature = True
nut_options.linear_damping = 0.0 # default = 0.0
nut_options.max_linear_velocity = 1000.0 # default = 1000.0
nut_options.angular_damping = 0.0 # default = 0.5
nut_options.max_angular_velocity = 64.0 # default = 64.0
nut_options.disable_gravity = False
nut_options.enable_gyroscopic_forces = True
nut_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
nut_options.use_mesh_materials = False
if self.cfg_base.mode.export_scene:
nut_options.mesh_normal_mode = gymapi.COMPUTE_PER_FACE
bolt_options = gymapi.AssetOptions()
bolt_options.flip_visual_attachments = False
bolt_options.fix_base_link = True
bolt_options.thickness = 0.0 # default = 0.02
bolt_options.armature = 0.0 # default = 0.0
bolt_options.use_physx_armature = True
bolt_options.linear_damping = 0.0 # default = 0.0
bolt_options.max_linear_velocity = 1000.0 # default = 1000.0
bolt_options.angular_damping = 0.0 # default = 0.5
bolt_options.max_angular_velocity = 64.0 # default = 64.0
bolt_options.disable_gravity = False
bolt_options.enable_gyroscopic_forces = True
bolt_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
bolt_options.use_mesh_materials = False
if self.cfg_base.mode.export_scene:
bolt_options.mesh_normal_mode = gymapi.COMPUTE_PER_FACE
nut_assets = []
bolt_assets = []
for subassembly in self.cfg_env.env.desired_subassemblies:
components = list(self.asset_info_nut_bolt[subassembly])
nut_file = self.asset_info_nut_bolt[subassembly][components[0]]['urdf_path'] + '.urdf'
bolt_file = self.asset_info_nut_bolt[subassembly][components[1]]['urdf_path'] + '.urdf'
nut_options.density = self.cfg_env.env.nut_bolt_density
bolt_options.density = self.cfg_env.env.nut_bolt_density
nut_asset = self.gym.load_asset(self.sim, urdf_root, nut_file, nut_options)
bolt_asset = self.gym.load_asset(self.sim, urdf_root, bolt_file, bolt_options)
nut_assets.append(nut_asset)
bolt_assets.append(bolt_asset)
return nut_assets, bolt_assets
def _create_actors(self, lower, upper, num_per_row, franka_asset, nut_assets, bolt_assets, table_asset):
"""Set initial actor poses. Create actors. Set shape and DOF properties."""
franka_pose = gymapi.Transform()
franka_pose.p.x = self.cfg_base.env.franka_depth
franka_pose.p.y = 0.0
franka_pose.p.z = 0.0
franka_pose.r = gymapi.Quat(0.0, 0.0, 1.0, 0.0)
table_pose = gymapi.Transform()
table_pose.p.x = 0.0
table_pose.p.y = 0.0
table_pose.p.z = self.cfg_base.env.table_height * 0.5
table_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
self.env_ptrs = []
self.franka_handles = []
self.nut_handles = []
self.bolt_handles = []
self.table_handles = []
self.shape_ids = []
self.franka_actor_ids_sim = [] # within-sim indices
self.nut_actor_ids_sim = [] # within-sim indices
self.bolt_actor_ids_sim = [] # within-sim indices
self.table_actor_ids_sim = [] # within-sim indices
actor_count = 0
self.nut_heights = []
self.nut_widths_max = []
self.bolt_widths = []
self.bolt_head_heights = []
self.bolt_shank_lengths = []
self.thread_pitches = []
for i in range(self.num_envs):
env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row)
if self.cfg_env.sim.disable_franka_collisions:
franka_handle = self.gym.create_actor(env_ptr, franka_asset, franka_pose, 'franka', i + self.num_envs,
0, 0)
else:
franka_handle = self.gym.create_actor(env_ptr, franka_asset, franka_pose, 'franka', i, 0, 0)
self.franka_actor_ids_sim.append(actor_count)
actor_count += 1
j = np.random.randint(0, len(self.cfg_env.env.desired_subassemblies))
subassembly = self.cfg_env.env.desired_subassemblies[j]
components = list(self.asset_info_nut_bolt[subassembly])
nut_pose = gymapi.Transform()
nut_pose.p.x = 0.0
nut_pose.p.y = self.cfg_env.env.nut_lateral_offset
nut_pose.p.z = self.cfg_base.env.table_height
nut_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
nut_handle = self.gym.create_actor(env_ptr, nut_assets[j], nut_pose, 'nut', i, 0, 0)
self.nut_actor_ids_sim.append(actor_count)
actor_count += 1
nut_height = self.asset_info_nut_bolt[subassembly][components[0]]['height']
nut_width_max = self.asset_info_nut_bolt[subassembly][components[0]]['width_max']
self.nut_heights.append(nut_height)
self.nut_widths_max.append(nut_width_max)
bolt_pose = gymapi.Transform()
bolt_pose.p.x = 0.0
bolt_pose.p.y = 0.0
bolt_pose.p.z = self.cfg_base.env.table_height
bolt_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
bolt_handle = self.gym.create_actor(env_ptr, bolt_assets[j], bolt_pose, 'bolt', i, 0, 0)
self.bolt_actor_ids_sim.append(actor_count)
actor_count += 1
bolt_width = self.asset_info_nut_bolt[subassembly][components[1]]['width']
bolt_head_height = self.asset_info_nut_bolt[subassembly][components[1]]['head_height']
bolt_shank_length = self.asset_info_nut_bolt[subassembly][components[1]]['shank_length']
self.bolt_widths.append(bolt_width)
self.bolt_head_heights.append(bolt_head_height)
self.bolt_shank_lengths.append(bolt_shank_length)
thread_pitch = self.asset_info_nut_bolt[subassembly]['thread_pitch']
self.thread_pitches.append(thread_pitch)
table_handle = self.gym.create_actor(env_ptr, table_asset, table_pose, 'table', i, 0, 0)
self.table_actor_ids_sim.append(actor_count)
actor_count += 1
link7_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_link7', gymapi.DOMAIN_ACTOR)
hand_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_hand', gymapi.DOMAIN_ACTOR)
left_finger_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_leftfinger',
gymapi.DOMAIN_ACTOR)
right_finger_id = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_rightfinger',
gymapi.DOMAIN_ACTOR)
self.shape_ids = [link7_id, hand_id, left_finger_id, right_finger_id]
franka_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, franka_handle)
for shape_id in self.shape_ids:
franka_shape_props[shape_id].friction = self.cfg_base.env.franka_friction
franka_shape_props[shape_id].rolling_friction = 0.0 # default = 0.0
franka_shape_props[shape_id].torsion_friction = 0.0 # default = 0.0
franka_shape_props[shape_id].restitution = 0.0 # default = 0.0
franka_shape_props[shape_id].compliance = 0.0 # default = 0.0
franka_shape_props[shape_id].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, franka_handle, franka_shape_props)
nut_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, nut_handle)
nut_shape_props[0].friction = self.cfg_env.env.nut_bolt_friction
nut_shape_props[0].rolling_friction = 0.0 # default = 0.0
nut_shape_props[0].torsion_friction = 0.0 # default = 0.0
nut_shape_props[0].restitution = 0.0 # default = 0.0
nut_shape_props[0].compliance = 0.0 # default = 0.0
nut_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, nut_handle, nut_shape_props)
bolt_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, bolt_handle)
bolt_shape_props[0].friction = self.cfg_env.env.nut_bolt_friction
bolt_shape_props[0].rolling_friction = 0.0 # default = 0.0
bolt_shape_props[0].torsion_friction = 0.0 # default = 0.0
bolt_shape_props[0].restitution = 0.0 # default = 0.0
bolt_shape_props[0].compliance = 0.0 # default = 0.0
bolt_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, bolt_handle, bolt_shape_props)
table_shape_props = self.gym.get_actor_rigid_shape_properties(env_ptr, table_handle)
table_shape_props[0].friction = self.cfg_base.env.table_friction
table_shape_props[0].rolling_friction = 0.0 # default = 0.0
table_shape_props[0].torsion_friction = 0.0 # default = 0.0
table_shape_props[0].restitution = 0.0 # default = 0.0
table_shape_props[0].compliance = 0.0 # default = 0.0
table_shape_props[0].thickness = 0.0 # default = 0.0
self.gym.set_actor_rigid_shape_properties(env_ptr, table_handle, table_shape_props)
self.franka_num_dofs = self.gym.get_actor_dof_count(env_ptr, franka_handle)
self.gym.enable_actor_dof_force_sensors(env_ptr, franka_handle)
self.env_ptrs.append(env_ptr)
self.franka_handles.append(franka_handle)
self.nut_handles.append(nut_handle)
self.bolt_handles.append(bolt_handle)
self.table_handles.append(table_handle)
self.num_actors = int(actor_count / self.num_envs) # per env
self.num_bodies = self.gym.get_env_rigid_body_count(env_ptr) # per env
self.num_dofs = self.gym.get_env_dof_count(env_ptr) # per env
# For setting targets
self.franka_actor_ids_sim = torch.tensor(self.franka_actor_ids_sim, dtype=torch.int32, device=self.device)
self.nut_actor_ids_sim = torch.tensor(self.nut_actor_ids_sim, dtype=torch.int32, device=self.device)
self.bolt_actor_ids_sim = torch.tensor(self.bolt_actor_ids_sim, dtype=torch.int32, device=self.device)
# For extracting root pos/quat
self.nut_actor_id_env = self.gym.find_actor_index(env_ptr, 'nut', gymapi.DOMAIN_ENV)
self.bolt_actor_id_env = self.gym.find_actor_index(env_ptr, 'bolt', gymapi.DOMAIN_ENV)
# For extracting body pos/quat, force, and Jacobian
self.nut_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, nut_handle, 'nut', gymapi.DOMAIN_ENV)
self.bolt_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, bolt_handle, 'bolt', gymapi.DOMAIN_ENV)
self.hand_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_hand',
gymapi.DOMAIN_ENV)
self.left_finger_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle, 'panda_leftfinger',
gymapi.DOMAIN_ENV)
self.right_finger_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle,
'panda_rightfinger', gymapi.DOMAIN_ENV)
self.fingertip_centered_body_id_env = self.gym.find_actor_rigid_body_index(env_ptr, franka_handle,
'panda_fingertip_centered',
gymapi.DOMAIN_ENV)
# For computing body COM pos
self.nut_heights = torch.tensor(self.nut_heights, device=self.device).unsqueeze(-1)
self.bolt_head_heights = torch.tensor(self.bolt_head_heights, device=self.device).unsqueeze(-1)
# For setting initial state
self.nut_widths_max = torch.tensor(self.nut_widths_max, device=self.device).unsqueeze(-1)
self.bolt_shank_lengths = torch.tensor(self.bolt_shank_lengths, device=self.device).unsqueeze(-1)
# For defining success or failure
self.bolt_widths = torch.tensor(self.bolt_widths, device=self.device).unsqueeze(-1)
self.thread_pitches = torch.tensor(self.thread_pitches, device=self.device).unsqueeze(-1)
def _acquire_env_tensors(self):
"""Acquire and wrap tensors. Create views."""
self.nut_pos = self.root_pos[:, self.nut_actor_id_env, 0:3]
self.nut_quat = self.root_quat[:, self.nut_actor_id_env, 0:4]
self.nut_linvel = self.root_linvel[:, self.nut_actor_id_env, 0:3]
self.nut_angvel = self.root_angvel[:, self.nut_actor_id_env, 0:3]
self.bolt_pos = self.root_pos[:, self.bolt_actor_id_env, 0:3]
self.bolt_quat = self.root_quat[:, self.bolt_actor_id_env, 0:4]
self.nut_force = self.contact_force[:, self.nut_body_id_env, 0:3]
self.bolt_force = self.contact_force[:, self.bolt_body_id_env, 0:3]
self.nut_com_pos = fc.translate_along_local_z(pos=self.nut_pos,
quat=self.nut_quat,
offset=self.bolt_head_heights + self.nut_heights * 0.5,
device=self.device)
self.nut_com_quat = self.nut_quat # always equal
self.nut_com_linvel = self.nut_linvel + torch.cross(self.nut_angvel,
(self.nut_com_pos - self.nut_pos),
dim=1)
self.nut_com_angvel = self.nut_angvel # always equal
def refresh_env_tensors(self):
"""Refresh tensors."""
# NOTE: Tensor refresh functions should be called once per step, before setters.
self.nut_com_pos = fc.translate_along_local_z(pos=self.nut_pos,
quat=self.nut_quat,
offset=self.bolt_head_heights + self.nut_heights * 0.5,
device=self.device)
self.nut_com_linvel = self.nut_linvel + torch.cross(self.nut_angvel,
(self.nut_com_pos - self.nut_pos),
dim=1)
| 19,505 | Python | 53.486033 | 141 | 0.613176 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_control.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: control module.
Imported by base, environment, and task classes. Not directly executed.
"""
import math
import torch
from isaacgymenvs.utils import torch_jit_utils as torch_utils
def compute_dof_pos_target(cfg_ctrl,
arm_dof_pos,
fingertip_midpoint_pos,
fingertip_midpoint_quat,
jacobian,
ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat,
ctrl_target_gripper_dof_pos,
device):
"""Compute Franka DOF position target to move fingertips towards target pose."""
ctrl_target_dof_pos = torch.zeros((cfg_ctrl['num_envs'], 9), device=device)
pos_error, axis_angle_error = get_pose_error(
fingertip_midpoint_pos=fingertip_midpoint_pos,
fingertip_midpoint_quat=fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat,
jacobian_type=cfg_ctrl['jacobian_type'],
rot_error_type='axis_angle')
delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1)
delta_arm_dof_pos = _get_delta_dof_pos(delta_pose=delta_fingertip_pose,
ik_method=cfg_ctrl['ik_method'],
jacobian=jacobian,
device=device)
ctrl_target_dof_pos[:, 0:7] = arm_dof_pos + delta_arm_dof_pos
ctrl_target_dof_pos[:, 7:9] = ctrl_target_gripper_dof_pos # gripper finger joints
return ctrl_target_dof_pos
def compute_dof_torque(cfg_ctrl,
dof_pos,
dof_vel,
fingertip_midpoint_pos,
fingertip_midpoint_quat,
fingertip_midpoint_linvel,
fingertip_midpoint_angvel,
left_finger_force,
right_finger_force,
jacobian,
arm_mass_matrix,
ctrl_target_gripper_dof_pos,
ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat,
ctrl_target_fingertip_contact_wrench,
device):
"""Compute Franka DOF torque to move fingertips towards target pose."""
# References:
# 1) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf
# 2) Modern Robotics
dof_torque = torch.zeros((cfg_ctrl['num_envs'], 9), device=device)
if cfg_ctrl['gain_space'] == 'joint':
pos_error, axis_angle_error = get_pose_error(
fingertip_midpoint_pos=fingertip_midpoint_pos,
fingertip_midpoint_quat=fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat,
jacobian_type=cfg_ctrl['jacobian_type'],
rot_error_type='axis_angle')
delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1)
# Set tau = k_p * joint_pos_error - k_d * joint_vel_error (ETH eq. 3.72)
delta_arm_dof_pos = _get_delta_dof_pos(delta_pose=delta_fingertip_pose,
ik_method=cfg_ctrl['ik_method'],
jacobian=jacobian,
device=device)
dof_torque[:, 0:7] = cfg_ctrl['joint_prop_gains'] * delta_arm_dof_pos + \
cfg_ctrl['joint_deriv_gains'] * (0.0 - dof_vel[:, 0:7])
if cfg_ctrl['do_inertial_comp']:
# Set tau = M * tau, where M is the joint-space mass matrix
arm_mass_matrix_joint = arm_mass_matrix
dof_torque[:, 0:7] = (arm_mass_matrix_joint @ dof_torque[:, 0:7].unsqueeze(-1)).squeeze(-1)
elif cfg_ctrl['gain_space'] == 'task':
task_wrench = torch.zeros((cfg_ctrl['num_envs'], 6), device=device)
if cfg_ctrl['do_motion_ctrl']:
pos_error, axis_angle_error = get_pose_error(
fingertip_midpoint_pos=fingertip_midpoint_pos,
fingertip_midpoint_quat=fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat,
jacobian_type=cfg_ctrl['jacobian_type'],
rot_error_type='axis_angle')
delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1)
# Set tau = k_p * task_pos_error - k_d * task_vel_error (building towards eq. 3.96-3.98)
task_wrench_motion = _apply_task_space_gains(delta_fingertip_pose=delta_fingertip_pose,
fingertip_midpoint_linvel=fingertip_midpoint_linvel,
fingertip_midpoint_angvel=fingertip_midpoint_angvel,
task_prop_gains=cfg_ctrl['task_prop_gains'],
task_deriv_gains=cfg_ctrl['task_deriv_gains'])
if cfg_ctrl['do_inertial_comp']:
# Set tau = Lambda * tau, where Lambda is the task-space mass matrix
jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2)
arm_mass_matrix_task = torch.inverse(jacobian @ torch.inverse(arm_mass_matrix) @ jacobian_T) # ETH eq. 3.86; geometric Jacobian is assumed
task_wrench_motion = (arm_mass_matrix_task @ task_wrench_motion.unsqueeze(-1)).squeeze(-1)
task_wrench = task_wrench + torch.tensor(cfg_ctrl['motion_ctrl_axes'], device=device).unsqueeze(0) * task_wrench_motion
if cfg_ctrl['do_force_ctrl']:
# Set tau = tau + F_t, where F_t is the target contact wrench
task_wrench_force = torch.zeros((cfg_ctrl['num_envs'], 6), device=device)
task_wrench_force = task_wrench_force + ctrl_target_fingertip_contact_wrench # open-loop force control (building towards ETH eq. 3.96-3.98)
if cfg_ctrl['force_ctrl_method'] == 'closed':
force_error, torque_error = _get_wrench_error(
left_finger_force=left_finger_force,
right_finger_force=right_finger_force,
ctrl_target_fingertip_contact_wrench=ctrl_target_fingertip_contact_wrench,
num_envs=cfg_ctrl['num_envs'],
device=device)
# Set tau = tau + k_p * contact_wrench_error
task_wrench_force = task_wrench_force + cfg_ctrl['wrench_prop_gains'] * torch.cat(
(force_error, torque_error), dim=1) # part of Modern Robotics eq. 11.61
task_wrench = task_wrench + torch.tensor(cfg_ctrl['force_ctrl_axes'], device=device).unsqueeze(
0) * task_wrench_force
# Set tau = J^T * tau, i.e., map tau into joint space as desired
jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2)
dof_torque[:, 0:7] = (jacobian_T @ task_wrench.unsqueeze(-1)).squeeze(-1)
dof_torque[:, 7:9] = cfg_ctrl['gripper_prop_gains'] * (ctrl_target_gripper_dof_pos - dof_pos[:, 7:9]) + \
cfg_ctrl['gripper_deriv_gains'] * (0.0 - dof_vel[:, 7:9]) # gripper finger joints
dof_torque = torch.clamp(dof_torque, min=-100.0, max=100.0)
return dof_torque
def get_pose_error(fingertip_midpoint_pos,
fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat,
jacobian_type,
rot_error_type):
"""Compute task-space error between target Franka fingertip pose and current pose."""
# Reference: https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf
# Compute pos error
pos_error = ctrl_target_fingertip_midpoint_pos - fingertip_midpoint_pos
# Compute rot error
if jacobian_type == 'geometric': # See example 2.9.8; note use of J_g and transformation between rotation vectors
# Compute quat error (i.e., difference quat)
# Reference: https://personal.utdallas.edu/~sxb027100/dock/quat.html
fingertip_midpoint_quat_norm = torch_utils.quat_mul(fingertip_midpoint_quat,
torch_utils.quat_conjugate(fingertip_midpoint_quat))[:, 3] # scalar component
fingertip_midpoint_quat_inv = torch_utils.quat_conjugate(
fingertip_midpoint_quat) / fingertip_midpoint_quat_norm.unsqueeze(-1)
quat_error = torch_utils.quat_mul(ctrl_target_fingertip_midpoint_quat, fingertip_midpoint_quat_inv)
# Convert to axis-angle error
axis_angle_error = axis_angle_from_quat(quat_error)
elif jacobian_type == 'analytic': # See example 2.9.7; note use of J_a and difference of rotation vectors
# Compute axis-angle error
axis_angle_error = axis_angle_from_quat(ctrl_target_fingertip_midpoint_quat)\
- axis_angle_from_quat(fingertip_midpoint_quat)
if rot_error_type == 'quat':
return pos_error, quat_error
elif rot_error_type == 'axis_angle':
return pos_error, axis_angle_error
def _get_wrench_error(left_finger_force,
right_finger_force,
ctrl_target_fingertip_contact_wrench,
num_envs,
device):
"""Compute task-space error between target Franka fingertip contact wrench and current wrench."""
fingertip_contact_wrench = torch.zeros((num_envs, 6), device=device)
fingertip_contact_wrench[:, 0:3] = left_finger_force + right_finger_force # net contact force on fingers
# Cols 3 to 6 are all zeros, as we do not have enough information
force_error = ctrl_target_fingertip_contact_wrench[:, 0:3] - (-fingertip_contact_wrench[:, 0:3])
torque_error = ctrl_target_fingertip_contact_wrench[:, 3:6] - (-fingertip_contact_wrench[:, 3:6])
return force_error, torque_error
def _get_delta_dof_pos(delta_pose, ik_method, jacobian, device):
"""Get delta Franka DOF position from delta pose using specified IK method."""
# References:
# 1) https://www.cs.cmu.edu/~15464-s13/lectures/lecture6/iksurvey.pdf
# 2) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf (p. 47)
if ik_method == 'pinv': # Jacobian pseudoinverse
k_val = 1.0
jacobian_pinv = torch.linalg.pinv(jacobian)
delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1)
delta_dof_pos = delta_dof_pos.squeeze(-1)
elif ik_method == 'trans': # Jacobian transpose
k_val = 1.0
jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2)
delta_dof_pos = k_val * jacobian_T @ delta_pose.unsqueeze(-1)
delta_dof_pos = delta_dof_pos.squeeze(-1)
elif ik_method == 'dls': # damped least squares (Levenberg-Marquardt)
lambda_val = 0.1
jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2)
lambda_matrix = (lambda_val ** 2) * torch.eye(n=jacobian.shape[1], device=device)
delta_dof_pos = jacobian_T @ torch.inverse(jacobian @ jacobian_T + lambda_matrix) @ delta_pose.unsqueeze(-1)
delta_dof_pos = delta_dof_pos.squeeze(-1)
elif ik_method == 'svd': # adaptive SVD
k_val = 1.0
U, S, Vh = torch.linalg.svd(jacobian)
S_inv = 1. / S
min_singular_value = 1.0e-5
S_inv = torch.where(S > min_singular_value, S_inv, torch.zeros_like(S_inv))
jacobian_pinv = torch.transpose(Vh, dim0=1, dim1=2)[:, :, :6] @ torch.diag_embed(S_inv) @ torch.transpose(U, dim0=1, dim1=2)
delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1)
delta_dof_pos = delta_dof_pos.squeeze(-1)
return delta_dof_pos
def _apply_task_space_gains(delta_fingertip_pose,
fingertip_midpoint_linvel,
fingertip_midpoint_angvel,
task_prop_gains,
task_deriv_gains):
"""Interpret PD gains as task-space gains. Apply to task-space error."""
task_wrench = torch.zeros_like(delta_fingertip_pose)
# Apply gains to lin error components
lin_error = delta_fingertip_pose[:, 0:3]
task_wrench[:, 0:3] = task_prop_gains[:, 0:3] * lin_error + \
task_deriv_gains[:, 0:3] * (0.0 - fingertip_midpoint_linvel)
# Apply gains to rot error components
rot_error = delta_fingertip_pose[:, 3:6]
task_wrench[:, 3:6] = task_prop_gains[:, 3:6] * rot_error + \
task_deriv_gains[:, 3:6] * (0.0 - fingertip_midpoint_angvel)
return task_wrench
def get_analytic_jacobian(fingertip_quat, fingertip_jacobian, num_envs, device):
"""Convert geometric Jacobian to analytic Jacobian."""
# Reference: https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf
# NOTE: Gym returns world-space geometric Jacobians by default
batch = num_envs
# Overview:
# x = [x_p; x_r]
# From eq. 2.189 and 2.192, x_dot = J_a @ q_dot = (E_inv @ J_g) @ q_dot
# From eq. 2.191, E = block(E_p, E_r); thus, E_inv = block(E_p_inv, E_r_inv)
# Eq. 2.12 gives an expression for E_p_inv
# Eq. 2.107 gives an expression for E_r_inv
# Compute E_inv_top (i.e., [E_p_inv, 0])
I = torch.eye(3, device=device)
E_p_inv = I.repeat((batch, 1)).reshape(batch, 3, 3)
E_inv_top = torch.cat((E_p_inv, torch.zeros((batch, 3, 3), device=device)), dim=2)
# Compute E_inv_bottom (i.e., [0, E_r_inv])
fingertip_axis_angle = axis_angle_from_quat(fingertip_quat)
fingertip_axis_angle_cross = get_skew_symm_matrix(fingertip_axis_angle, device=device)
fingertip_angle = torch.linalg.vector_norm(fingertip_axis_angle, dim=1)
factor_1 = 1 / (fingertip_angle ** 2)
factor_2 = 1 - fingertip_angle * 0.5 * torch.sin(fingertip_angle) / (1 - torch.cos(fingertip_angle))
factor_3 = factor_1 * factor_2
E_r_inv = I \
- 1 * 0.5 * fingertip_axis_angle_cross \
+ (fingertip_axis_angle_cross @ fingertip_axis_angle_cross) * factor_3.unsqueeze(-1).repeat((1, 3 * 3)).reshape((batch, 3, 3))
E_inv_bottom = torch.cat((torch.zeros((batch, 3, 3), device=device), E_r_inv), dim=2)
E_inv = torch.cat((E_inv_top.reshape((batch, 3 * 6)), E_inv_bottom.reshape((batch, 3 * 6))), dim=1).reshape((batch, 6, 6))
J_a = E_inv @ fingertip_jacobian
return J_a
def get_skew_symm_matrix(vec, device):
"""Convert vector to skew-symmetric matrix."""
# Reference: https://en.wikipedia.org/wiki/Cross_product#Conversion_to_matrix_multiplication
batch = vec.shape[0]
I = torch.eye(3, device=device)
skew_symm = torch.transpose(torch.cross(vec.repeat((1, 3)).reshape((batch * 3, 3)),
I.repeat((batch, 1)))
.reshape(batch, 3, 3),
dim0=1,
dim1=2)
return skew_symm
def translate_along_local_z(pos, quat, offset, device):
"""Translate global body position along local Z-axis and express in global coordinates."""
num_vecs = pos.shape[0]
offset_vec = offset * torch.tensor([0.0, 0.0, 1.0], device=device).repeat((num_vecs, 1))
_, translated_pos = torch_utils.tf_combine(q1=quat,
t1=pos,
q2=torch.tensor([0.0, 0.0, 0.0, 1.0], device=device).repeat((num_vecs, 1)),
t2=offset_vec)
return translated_pos
def axis_angle_from_euler(euler):
"""Convert tensor of Euler angles to tensor of axis-angles."""
quat = torch_utils.quat_from_euler_xyz(roll=euler[:, 0], pitch=euler[:, 1], yaw=euler[:, 2])
quat = quat * torch.sign(quat[:, 3]).unsqueeze(-1) # smaller rotation
axis_angle = axis_angle_from_quat(quat)
return axis_angle
def axis_angle_from_quat(quat, eps=1.0e-6):
"""Convert tensor of quaternions to tensor of axis-angles."""
# Reference: https://github.com/facebookresearch/pytorch3d/blob/bee31c48d3d36a8ea268f9835663c52ff4a476ec/pytorch3d/transforms/rotation_conversions.py#L516-L544
mag = torch.linalg.norm(quat[:, 0:3], dim=1)
half_angle = torch.atan2(mag, quat[:, 3])
angle = 2.0 * half_angle
sin_half_angle_over_angle = torch.where(torch.abs(angle) > eps,
torch.sin(half_angle) / angle,
1 / 2 - angle ** 2.0 / 48)
axis_angle = quat[:, 0:3] / sin_half_angle_over_angle.unsqueeze(-1)
return axis_angle
def axis_angle_from_quat_naive(quat):
"""Convert tensor of quaternions to tensor of axis-angles."""
# Reference: https://en.wikipedia.org/wiki/quats_and_spatial_rotation#Recovering_the_axis-angle_representation
# NOTE: Susceptible to undesirable behavior due to divide-by-zero
mag = torch.linalg.vector_norm(quat[:, 0:3], dim=1) # zero when quat = [0, 0, 0, 1]
axis = quat[:, 0:3] / mag.unsqueeze(-1)
angle = 2.0 * torch.atan2(mag, quat[:, 3])
axis_angle = axis * angle.unsqueeze(-1)
return axis_angle
def get_rand_quat(num_quats, device):
"""Generate tensor of random quaternions."""
# Reference: http://planning.cs.uiuc.edu/node198.html
u = torch.rand((num_quats, 3), device=device)
quat = torch.zeros((num_quats, 4), device=device)
quat[:, 0] = torch.sqrt(1 - u[:, 0]) * torch.sin(2 * math.pi * u[:, 1])
quat[:, 1] = torch.sqrt(1 - u[:, 0]) * torch.cos(2 * math.pi * u[:, 1])
quat[:, 2] = torch.sqrt(u[:, 0]) * torch.sin(2 * math.pi * u[:, 2])
quat[:, 3] = torch.sqrt(u[:, 0]) * torch.cos(2 * math.pi * u[:, 2])
return quat
def get_nonrand_quat(num_quats, rot_perturbation, device):
"""Generate tensor of non-random quaternions by composing random Euler rotations."""
quat = torch_utils.quat_from_euler_xyz(
torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation,
torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation,
torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0 - rot_perturbation)
return quat
| 20,557 | Python | 47.947619 | 163 | 0.608357 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/factory/factory_task_gears.py | # Copyright (c) 2021-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: Class for gears task.
Inherits gears environment class and abstract task class (not inforced). Can be executed with
python train.py task=FactoryTaskGears
Only the environment is provided; training a successful RL policy is an open research problem left to the user.
"""
import hydra
import math
import omegaconf
import os
import torch
from isaacgym import gymapi, gymtorch
from isaacgymenvs.tasks.factory.factory_env_gears import FactoryEnvGears
from isaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from isaacgymenvs.tasks.factory.factory_schema_config_task import FactorySchemaConfigTask
class FactoryTaskGears(FactoryEnvGears, FactoryABCTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
"""Initialize instance variables. Initialize task superclass."""
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
self.cfg = cfg
self._get_task_yaml_params()
if self.viewer != None:
self._set_viewer_params()
if self.cfg_base.mode.export_scene:
self.export_scene(label='factory_task_gears')
def _get_task_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name='factory_schema_config_task', node=FactorySchemaConfigTask)
self.cfg_task = omegaconf.OmegaConf.create(self.cfg)
self.max_episode_length = self.cfg_task.rl.max_episode_length # required instance var for VecTask
asset_info_path = '../../assets/factory/yaml/factory_asset_info_gears.yaml' # relative to Gym's Hydra search path (cfg dir)
self.asset_info_gears = hydra.compose(config_name=asset_info_path)
self.asset_info_gears = self.asset_info_gears['']['']['']['']['']['']['assets']['factory']['yaml'] # strip superfluous nesting
ppo_path = 'train/FactoryTaskGearsPPO.yaml' # relative to Gym's Hydra search path (cfg dir)
self.cfg_ppo = hydra.compose(config_name=ppo_path)
self.cfg_ppo = self.cfg_ppo['train'] # strip superfluous nesting
def _acquire_task_tensors(self):
"""Acquire tensors."""
pass
def _refresh_task_tensors(self):
"""Refresh tensors."""
pass
def pre_physics_step(self, actions):
"""Reset environments. Apply actions from policy as position/rotation targets, force/torque targets, and/or PD gains."""
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self._actions = actions.clone().to(self.device) # shape = (num_envs, num_actions); values = [-1, 1]
def post_physics_step(self):
"""Step buffers. Refresh tensors. Compute observations and reward."""
self.progress_buf[:] += 1
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
self.compute_observations()
self.compute_reward()
def compute_observations(self):
"""Compute observations."""
return self.obs_buf # shape = (num_envs, num_observations)
def compute_reward(self):
"""Detect successes and failures. Update reward and reset buffers."""
self._update_rew_buf()
self._update_reset_buf()
def _update_rew_buf(self):
"""Compute reward at current timestep."""
pass
def _update_reset_buf(self):
"""Assign environments for reset if successful or failed."""
pass
def reset_idx(self, env_ids):
"""Reset specified environments."""
self._reset_franka(env_ids)
self._reset_object(env_ids)
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def _reset_franka(self, env_ids):
"""Reset DOF states and DOF targets of Franka."""
# shape of dof_pos = (num_envs, num_dofs)
# shape of dof_vel = (num_envs, num_dofs)
# Initialize Franka to middle of joint limits, plus joint noise
franka_dof_props = self.gym.get_actor_dof_properties(self.env_ptrs[0],
self.franka_handles[0]) # same across all envs
lower_lims = franka_dof_props['lower']
upper_lims = franka_dof_props['upper']
self.dof_pos[:, 0:self.franka_num_dofs] = torch.tensor((lower_lims + upper_lims) * 0.5, device=self.device) \
+ (torch.rand((self.num_envs, 1), device=self.device) * 2.0 - 1.0) * self.cfg_task.randomize.joint_noise * math.pi / 180
self.dof_vel[env_ids, 0:self.franka_num_dofs] = 0.0
franka_actor_ids_sim_int32 = self.franka_actor_ids_sim.to(dtype=torch.int32, device=self.device)[env_ids]
self.gym.set_dof_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(franka_actor_ids_sim_int32),
len(franka_actor_ids_sim_int32))
self.ctrl_target_dof_pos[env_ids, 0:self.franka_num_dofs] = self.dof_pos[env_ids, 0:self.franka_num_dofs]
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(self.ctrl_target_dof_pos))
def _reset_object(self, env_ids):
"""Reset root state of gears."""
# shape of root_pos = (num_envs, num_actors, 3)
# shape of root_quat = (num_envs, num_actors, 4)
# shape of root_linvel = (num_envs, num_actors, 3)
# shape of root_angvel = (num_envs, num_actors, 3)
if self.cfg_task.randomize.initial_state == 'random':
self.root_pos[env_ids, self.gear_small_actor_id_env] = \
torch.cat(((torch.rand((self.num_envs, 1), device=self.device) * 2.0 - 1.0) * self.cfg_task.randomize.gears_noise_xy,
- self.cfg_task.randomize.gears_bias_y + (torch.rand((self.num_envs, 1), device=self.device) * 2.0 - 1.0) * self.cfg_task.randomize.gears_noise_xy,
torch.ones((self.num_envs, 1), device=self.device) * (self.cfg_base.env.table_height + self.cfg_task.randomize.gears_bias_z)
), dim=1)
self.root_pos[env_ids, self.gear_medium_actor_id_env] = \
torch.cat(((torch.rand((self.num_envs, 1), device=self.device) * 2.0 - 1.0) * self.cfg_task.randomize.gears_noise_xy,
self.cfg_task.randomize.gears_bias_y + (torch.rand((self.num_envs, 1), device=self.device) * 2.0 - 1.0) * self.cfg_task.randomize.gears_noise_xy,
torch.ones((self.num_envs, 1), device=self.device) * (self.cfg_base.env.table_height + self.cfg_task.randomize.gears_bias_z)
), dim=1)
self.root_pos[env_ids, self.gear_large_actor_id_env] = \
torch.cat(((torch.rand((self.num_envs, 1), device=self.device) * 2.0 - 1.0) * self.cfg_task.randomize.gears_noise_xy,
- self.cfg_task.randomize.gears_bias_y + (torch.rand((self.num_envs, 1), device=self.device) * 2.0 - 1.0) * self.cfg_task.randomize.gears_noise_xy,
torch.ones((self.num_envs, 1), device=self.device) * (self.cfg_base.env.table_height + self.cfg_task.randomize.gears_bias_z)), dim=1)
elif self.cfg_task.randomize.initial_state == 'goal':
self.root_pos[env_ids, self.gear_small_actor_id_env] = torch.tensor(
[0.0, 0.0, self.cfg_base.env.table_height], device=self.device)
self.root_pos[env_ids, self.gear_medium_actor_id_env] = torch.tensor(
[0.0, 0.0, self.cfg_base.env.table_height], device=self.device)
self.root_pos[env_ids, self.gear_large_actor_id_env] = torch.tensor(
[0.0, 0.0, self.cfg_base.env.table_height], device=self.device)
self.root_linvel[env_ids, self.gear_small_actor_id_env] = 0.0
self.root_angvel[env_ids, self.gear_small_actor_id_env] = 0.0
self.root_linvel[env_ids, self.gear_medium_actor_id_env] = 0.0
self.root_angvel[env_ids, self.gear_medium_actor_id_env] = 0.0
self.root_linvel[env_ids, self.gear_large_actor_id_env] = 0.0
self.root_angvel[env_ids, self.gear_large_actor_id_env] = 0.0
gear_small_actor_ids_sim_int32 = self.gear_small_actor_ids_sim.to(dtype=torch.int32, device=self.device)
gear_medium_actor_ids_sim_int32 = self.gear_medium_actor_ids_sim.to(dtype=torch.int32, device=self.device)
gear_large_actor_ids_sim_int32 = self.gear_large_actor_ids_sim.to(dtype=torch.int32, device=self.device)
gears_actor_ids_sim_int32 = torch.cat((gear_small_actor_ids_sim_int32[env_ids],
gear_medium_actor_ids_sim_int32[env_ids],
gear_large_actor_ids_sim_int32[env_ids]))
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_state),
gymtorch.unwrap_tensor(gears_actor_ids_sim_int32),
len(gear_small_actor_ids_sim_int32[env_ids]) +
len(gear_medium_actor_ids_sim_int32[env_ids]) +
len(gear_large_actor_ids_sim_int32[env_ids])
)
def _reset_buffers(self, env_ids):
"""Reset buffers. """
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def _set_viewer_params(self):
"""Set viewer parameters."""
cam_pos = gymapi.Vec3(-1.0, -1.0, 1.0)
cam_target = gymapi.Vec3(0.0, 0.0, 0.5)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
| 11,642 | Python | 50.290749 | 174 | 0.624549 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/allegro_kuka/generate_cuboids.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
from os.path import join
from typing import Callable, List
from jinja2 import Environment, FileSystemLoader, select_autoescape
FilterFunc = Callable[[List[int]], bool]
def generate_assets(
scales, min_volume, max_volume, generated_assets_dir, base_mesh, base_cube_size_m, filter_funcs: List[FilterFunc]
):
template_dir = join(os.path.dirname(os.path.abspath(__file__)), "../../../assets/asset_templates")
print(f"Assets template dir: {template_dir}")
env = Environment(
loader=FileSystemLoader(template_dir),
autoescape=select_autoescape(),
)
template = env.get_template("cube_multicolor_allegro.urdf.template") # <-- pass as function parameter?
idx = 0
for x_scale in scales:
for y_scale in scales:
for z_scale in scales:
volume = x_scale * y_scale * z_scale / (100 * 100 * 100)
if volume > max_volume:
continue
if volume < min_volume:
continue
curr_scales = [x_scale, y_scale, z_scale]
curr_scales.sort()
filtered = False
for filter_func in filter_funcs:
if filter_func(curr_scales):
filtered = True
if filtered:
continue
asset = template.render(
base_mesh=base_mesh,
x_scale=base_cube_size_m * (x_scale / 100),
y_scale=base_cube_size_m * (y_scale / 100),
z_scale=base_cube_size_m * (z_scale / 100),
)
fname = f"{idx:03d}_cube_{x_scale}_{y_scale}_{z_scale}.urdf"
idx += 1
with open(join(generated_assets_dir, fname), "w") as fobj:
fobj.write(asset)
def filter_thin_plates(scales: List[int]) -> bool:
"""
Skip cuboids where one dimension is much smaller than the other two - these are very hard to grasp.
We return true if object needs to be skipped.
"""
scales = sorted(scales)
return scales[0] * 3 <= scales[1]
def generate_default_cube(assets_dir, base_mesh, base_cube_size_m):
scales = [100]
min_volume = max_volume = 1.0
generate_assets(scales, min_volume, max_volume, assets_dir, base_mesh, base_cube_size_m, [])
def generate_small_cuboids(assets_dir, base_mesh, base_cube_size_m):
scales = [100, 50, 66, 75, 90, 110, 125, 150, 175, 200, 250, 300]
min_volume = 1.0
max_volume = 2.5
generate_assets(scales, min_volume, max_volume, assets_dir, base_mesh, base_cube_size_m, [])
def generate_big_cuboids(assets_dir, base_mesh, base_cube_size_m):
scales = [100, 125, 150, 200, 250, 300, 350]
min_volume = 2.5
max_volume = 15.0
generate_assets(scales, min_volume, max_volume, assets_dir, base_mesh, base_cube_size_m, [filter_thin_plates])
def filter_non_elongated(scales: List[int]) -> bool:
"""
Skip cuboids that are not elongated. One dimension should be significantly larger than the other two.
We return true if object needs to be skipped.
"""
scales = sorted(scales)
return scales[2] <= scales[0] * 3 or scales[2] <= scales[1] * 3
def generate_sticks(assets_dir, base_mesh, base_cube_size_m):
scales = [100, 50, 75, 200, 300, 400, 500, 600]
min_volume = 2.5
max_volume = 6.0
generate_assets(
scales,
min_volume,
max_volume,
assets_dir,
base_mesh,
base_cube_size_m,
[filter_thin_plates, filter_non_elongated],
)
| 5,157 | Python | 37.492537 | 117 | 0.645143 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/allegro_kuka/allegro_kuka_two_arms_regrasping.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from typing import List, Tuple
import torch
from isaacgym import gymapi
from torch import Tensor
from isaacgymenvs.utils.torch_jit_utils import to_torch, torch_rand_float
from isaacgymenvs.tasks.allegro_kuka.allegro_kuka_two_arms import AllegroKukaTwoArmsBase
from isaacgymenvs.tasks.allegro_kuka.allegro_kuka_utils import tolerance_curriculum, tolerance_successes_objective
class AllegroKukaTwoArmsRegrasping(AllegroKukaTwoArmsBase):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.goal_object_indices = []
self.goal_asset = None
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
def _object_keypoint_offsets(self):
"""Regrasping task uses only a single object keypoint since we do not care about object orientation."""
return [[0, 0, 0]]
def _load_additional_assets(self, object_asset_root, arm_y_offset: float):
goal_asset_options = gymapi.AssetOptions()
goal_asset_options.disable_gravity = True
self.goal_asset = self.gym.load_asset(
self.sim, object_asset_root, self.asset_files_dict["ball"], goal_asset_options
)
goal_rb_count = self.gym.get_asset_rigid_body_count(self.goal_asset)
goal_shapes_count = self.gym.get_asset_rigid_shape_count(self.goal_asset)
return goal_rb_count, goal_shapes_count
def _create_additional_objects(self, env_ptr, env_idx, object_asset_idx):
goal_start_pose = gymapi.Transform()
goal_asset = self.goal_asset
goal_handle = self.gym.create_actor(
env_ptr, goal_asset, goal_start_pose, "goal_object", env_idx + self.num_envs, 0, 0
)
self.gym.set_actor_scale(env_ptr, goal_handle, 0.5)
self.gym.set_rigid_body_color(env_ptr, goal_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
goal_object_idx = self.gym.get_actor_index(env_ptr, goal_handle, gymapi.DOMAIN_SIM)
self.goal_object_indices.append(goal_object_idx)
def _after_envs_created(self):
self.goal_object_indices = to_torch(self.goal_object_indices, dtype=torch.long, device=self.device)
def _reset_target(self, env_ids: Tensor) -> None:
# sample random target location in some volume
target_volume_origin = self.target_volume_origin
target_volume_extent = self.target_volume_extent
target_volume_min_coord = target_volume_origin + target_volume_extent[:, 0]
target_volume_max_coord = target_volume_origin + target_volume_extent[:, 1]
target_volume_size = target_volume_max_coord - target_volume_min_coord
rand_pos_floats = torch_rand_float(0.0, 1.0, (len(env_ids), 3), device=self.device)
target_coords = target_volume_min_coord + rand_pos_floats * target_volume_size
# let the target be close to 1st or 2nd arm, randomly
left_right_random = torch_rand_float(-1.0, 1.0, (len(env_ids), 1), device=self.device)
x_ofs = 0.75
x_pos = torch.where(
left_right_random > 0,
x_ofs * torch.ones_like(left_right_random),
-x_ofs * torch.ones_like(left_right_random),
)
target_coords[:, 0] += x_pos.squeeze(dim=1)
self.goal_states[env_ids, 0:3] = target_coords
self.root_state_tensor[self.goal_object_indices[env_ids], 0:3] = self.goal_states[env_ids, 0:3]
# we also reset the object to its initial position
self.reset_object_pose(env_ids)
# since we put the object back on the table, also reset the lifting reward
self.lifted_object[env_ids] = False
self.deferred_set_actor_root_state_tensor_indexed(
[self.object_indices[env_ids], self.goal_object_indices[env_ids]]
)
def _extra_object_indices(self, env_ids: Tensor) -> List[Tensor]:
return [self.goal_object_indices[env_ids]]
def compute_kuka_reward(self) -> Tuple[Tensor, Tensor]:
rew_buf, is_success = super().compute_kuka_reward()
return rew_buf, is_success
def _true_objective(self) -> Tensor:
true_objective = tolerance_successes_objective(
self.success_tolerance, self.initial_tolerance, self.target_tolerance, self.successes
)
return true_objective
def _extra_curriculum(self):
self.success_tolerance, self.last_curriculum_update = tolerance_curriculum(
self.last_curriculum_update,
self.frame_since_restart,
self.tolerance_curriculum_interval,
self.prev_episode_successes,
self.success_tolerance,
self.initial_tolerance,
self.target_tolerance,
self.tolerance_curriculum_increment,
)
| 6,376 | Python | 45.889706 | 120 | 0.692597 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/allegro_kuka/allegro_kuka_two_arms.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import os
import tempfile
from copy import copy
from os.path import join
from typing import List, Tuple
from isaacgym import gymapi, gymtorch, gymutil
from torch import Tensor
from isaacgymenvs.tasks.allegro_kuka.allegro_kuka_utils import DofParameters, populate_dof_properties
from isaacgymenvs.tasks.base.vec_task import VecTask
from isaacgymenvs.tasks.allegro_kuka.generate_cuboids import (
generate_big_cuboids,
generate_default_cube,
generate_small_cuboids,
generate_sticks,
)
from isaacgymenvs.utils.torch_jit_utils import *
class AllegroKukaTwoArmsBase(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.frame_since_restart: int = 0 # number of control steps since last restart across all actors
self.hand_arm_asset_file: str = self.cfg["env"]["asset"]["kukaAllegro"]
self.clamp_abs_observations: float = self.cfg["env"]["clampAbsObservations"]
self.num_arms = self.cfg["env"]["numArms"]
assert self.num_arms == 2, f"Only two arms supported, got {self.num_arms}"
self.arm_x_ofs = self.cfg["env"]["armXOfs"]
self.arm_y_ofs = self.cfg["env"]["armYOfs"]
# 4 joints for index, middle, ring, and thumb and 7 for kuka arm
self.num_arm_dofs = 7
self.num_finger_dofs = 4
self.num_allegro_fingertips = 4
self.num_hand_dofs = self.num_finger_dofs * self.num_allegro_fingertips
self.num_hand_arm_dofs = self.num_hand_dofs + self.num_arm_dofs
self.num_allegro_kuka_actions = self.num_hand_arm_dofs * self.num_arms
self.randomize = self.cfg["task"]["randomize"]
self.randomization_params = self.cfg["task"]["randomization_params"]
self.distance_delta_rew_scale = self.cfg["env"]["distanceDeltaRewScale"]
self.lifting_rew_scale = self.cfg["env"]["liftingRewScale"]
self.lifting_bonus = self.cfg["env"]["liftingBonus"]
self.lifting_bonus_threshold = self.cfg["env"]["liftingBonusThreshold"]
self.keypoint_rew_scale = self.cfg["env"]["keypointRewScale"]
# not used in 2-arm task for now
# to fix: add to config
# self.kuka_actions_penalty_scale = self.cfg["env"]["kukaActionsPenaltyScale"]
# self.allegro_actions_penalty_scale = self.cfg["env"]["allegroActionsPenaltyScale"]
self.dof_params: DofParameters = DofParameters.from_cfg(self.cfg)
self.initial_tolerance = self.cfg["env"]["successTolerance"]
self.success_tolerance = self.initial_tolerance
self.target_tolerance = self.cfg["env"]["targetSuccessTolerance"]
self.tolerance_curriculum_increment = self.cfg["env"]["toleranceCurriculumIncrement"]
self.tolerance_curriculum_interval = self.cfg["env"]["toleranceCurriculumInterval"]
self.reach_goal_bonus = self.cfg["env"]["reachGoalBonus"]
self.fall_dist = self.cfg["env"]["fallDistance"]
self.fall_penalty = self.cfg["env"]["fallPenalty"]
self.reset_position_noise_x = self.cfg["env"]["resetPositionNoiseX"]
self.reset_position_noise_y = self.cfg["env"]["resetPositionNoiseY"]
self.reset_position_noise_z = self.cfg["env"]["resetPositionNoiseZ"]
self.reset_rotation_noise = self.cfg["env"]["resetRotationNoise"]
self.reset_dof_pos_noise_fingers = self.cfg["env"]["resetDofPosRandomIntervalFingers"]
self.reset_dof_pos_noise_arm = self.cfg["env"]["resetDofPosRandomIntervalArm"]
self.reset_dof_vel_noise = self.cfg["env"]["resetDofVelRandomInterval"]
self.force_scale = self.cfg["env"].get("forceScale", 0.0)
self.force_prob_range = self.cfg["env"].get("forceProbRange", [0.001, 0.1])
self.force_decay = self.cfg["env"].get("forceDecay", 0.99)
self.force_decay_interval = self.cfg["env"].get("forceDecayInterval", 0.08)
# currently not used in 2-hand env
# self.hand_dof_speed_scale = self.cfg["env"]["dofSpeedScale"]
self.use_relative_control = self.cfg["env"]["useRelativeControl"]
self.act_moving_average = self.cfg["env"]["actionsMovingAverage"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.reset_time = self.cfg["env"].get("resetTime", -1.0)
self.max_consecutive_successes = self.cfg["env"]["maxConsecutiveSuccesses"]
self.success_steps: int = self.cfg["env"]["successSteps"]
# 1.0 means keypoints correspond to the corners of the object
# larger values help the agent to prioritize rotation matching
self.keypoint_scale = self.cfg["env"]["keypointScale"]
# size of the object (i.e. cube) before scaling
self.object_base_size = self.cfg["env"]["objectBaseSize"]
# whether to sample random object dimensions
self.randomize_object_dimensions = self.cfg["env"]["randomizeObjectDimensions"]
self.with_small_cuboids = self.cfg["env"]["withSmallCuboids"]
self.with_big_cuboids = self.cfg["env"]["withBigCuboids"]
self.with_sticks = self.cfg["env"]["withSticks"]
if self.reset_time > 0.0:
self.max_episode_length = int(round(self.reset_time / (self.control_freq_inv * self.sim_params.dt)))
print("Reset time: ", self.reset_time)
print("New episode length: ", self.max_episode_length)
self.object_type = self.cfg["env"]["objectType"]
assert self.object_type in ["block"]
self.asset_files_dict = {
"block": "urdf/objects/cube_multicolor.urdf", # 0.05m box
"table": "urdf/table_wide.urdf",
"bucket": "urdf/objects/bucket.urdf",
"lightbulb": "lightbulb/A60_E27_SI.urdf",
"socket": "E27SocketSimple.urdf",
"ball": "urdf/objects/ball.urdf",
}
self.keypoints_offsets = self._object_keypoint_offsets()
self.num_keypoints = len(self.keypoints_offsets)
self.allegro_fingertips = ["index_link_3", "middle_link_3", "ring_link_3", "thumb_link_3"]
self.fingertip_offsets = np.array(
[[0.05, 0.005, 0], [0.05, 0.005, 0], [0.05, 0.005, 0], [0.06, 0.005, 0]], dtype=np.float32
)
palm_offset = np.array([-0.00, -0.02, 0.16], dtype=np.float32)
self.num_fingertips = len(self.allegro_fingertips)
# can be only "full_state"
self.obs_type = self.cfg["env"]["observationType"]
if not (self.obs_type in ["full_state"]):
raise Exception("Unknown type of observations!")
print("Obs type:", self.obs_type)
num_dof_pos = num_dof_vel = self.num_hand_arm_dofs * self.num_arms
palm_pos_size = 3 * self.num_arms
palm_rot_vel_angvel_size = 10 * self.num_arms
obj_rot_vel_angvel_size = 10
fingertip_rel_pos_size = 3 * self.num_fingertips * self.num_arms
keypoints_rel_palm_size = self.num_keypoints * 3 * self.num_arms
keypoints_rel_goal_size = self.num_keypoints * 3
object_scales_size = 3
max_keypoint_dist_size = 1
lifted_object_flag_size = 1
progress_obs_size = 1 + 1
# commented out for now - not used in 2-hand env
# closest_fingertip_distance_size = self.num_fingertips * self.num_arms
reward_obs_size = 1
self.full_state_size = (
num_dof_pos
+ num_dof_vel
+ palm_pos_size
+ palm_rot_vel_angvel_size
+ obj_rot_vel_angvel_size
+ fingertip_rel_pos_size
+ keypoints_rel_palm_size
+ keypoints_rel_goal_size
+ object_scales_size
+ max_keypoint_dist_size
+ lifted_object_flag_size
+ progress_obs_size
+ reward_obs_size
)
num_states = self.full_state_size
self.num_obs_dict = {
"full_state": self.full_state_size,
}
self.up_axis = "z"
self.fingertip_obs = True
self.cfg["env"]["numObservations"] = self.num_obs_dict[self.obs_type]
self.cfg["env"]["numStates"] = num_states
self.cfg["env"]["numActions"] = self.num_allegro_kuka_actions
self.cfg["device_type"] = sim_device.split(":")[0]
self.cfg["device_id"] = int(sim_device.split(":")[1])
self.cfg["headless"] = headless
super().__init__(
config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id,
headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render,
)
if self.viewer is not None:
cam_pos = gymapi.Vec3(10.0, 5.0, 1.0)
cam_target = gymapi.Vec3(6.0, 5.0, 0.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# volume to sample target position from
target_volume_origin = np.array([0, 0.0, 0.8], dtype=np.float32)
target_volume_extent = np.array([[-0.2, 0.2], [-0.5, 0.5], [-0.12, 0.25]], dtype=np.float32)
self.target_volume_origin = torch.from_numpy(target_volume_origin).to(self.device).float()
self.target_volume_extent = torch.from_numpy(target_volume_extent).to(self.device).float()
# get gym GPU state tensors
actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
rigid_body_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
# create some wrapper tensors for different slices
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.hand_arm_default_dof_pos = torch.zeros(
[self.num_arms, self.num_hand_arm_dofs], dtype=torch.float, device=self.device
)
desired_kuka_pos = torch.tensor([-1.571, 1.571, -0.000, 1.6, -0.000, 1.485, 2.358]) # pose v1
# desired_kuka_pos = torch.tensor([-2.135, 0.843, 1.786, -0.903, -2.262, 1.301, -2.791]) # pose v2
self.hand_arm_default_dof_pos[0, :7] = desired_kuka_pos
desired_kuka_pos = torch.tensor([-1.571, 1.571, -0.000, 1.6, -0.000, 1.485, 2.358]) # pose v1
# desired_kuka_pos = torch.tensor([-2.135, 0.843, 1.786, -0.903, -2.262, 1.301, -2.791]) # pose v2
self.hand_arm_default_dof_pos[1, :7] = desired_kuka_pos
self.pos_noise_coeff = torch.zeros_like(self.hand_arm_default_dof_pos, device=self.device)
self.pos_noise_coeff[:, 0:7] = self.reset_dof_pos_noise_arm
self.pos_noise_coeff[:, 7 : self.num_hand_arm_dofs] = self.reset_dof_pos_noise_fingers
self.pos_noise_coeff = self.pos_noise_coeff.flatten()
self.hand_arm_default_dof_pos = self.hand_arm_default_dof_pos.flatten()
self.arm_hand_dof_state = self.dof_state.view(self.num_envs, -1, 2)[:, : self.num_hand_arm_dofs * self.num_arms]
# this will have dimensions [num_envs, num_arms * num_hand_arm_dofs]
self.arm_hand_dof_pos = self.arm_hand_dof_state[..., 0]
self.arm_hand_dof_vel = self.arm_hand_dof_state[..., 1]
self.rigid_body_states = gymtorch.wrap_tensor(rigid_body_tensor).view(self.num_envs, -1, 13)
self.num_bodies = self.rigid_body_states.shape[1]
self.root_state_tensor = gymtorch.wrap_tensor(actor_root_state_tensor).view(-1, 13)
self.palm_center_offset = torch.from_numpy(palm_offset).to(self.device).repeat((self.num_envs, 1))
self.palm_center_pos = torch.zeros((self.num_envs, self.num_arms, 3), dtype=torch.float, device=self.device)
self.fingertip_offsets = torch.from_numpy(self.fingertip_offsets).to(self.device).repeat((self.num_envs, 1, 1))
self.set_actor_root_state_object_indices: List[Tensor] = []
self.prev_targets = torch.zeros(
(self.num_envs, self.num_arms * self.num_hand_arm_dofs), dtype=torch.float, device=self.device
)
self.cur_targets = torch.zeros(
(self.num_envs, self.num_arms * self.num_hand_arm_dofs), dtype=torch.float, device=self.device
)
self.global_indices = torch.arange(self.num_envs * 3, dtype=torch.int32, device=self.device).view(
self.num_envs, -1
)
self.x_unit_tensor = to_torch([1, 0, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.y_unit_tensor = to_torch([0, 1, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.z_unit_tensor = to_torch([0, 0, 1], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.reset_goal_buf = self.reset_buf.clone()
self.successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.prev_episode_successes = torch.zeros_like(self.successes)
# true objective value for the whole episode, plus saving values for the previous episode
self.true_objective = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.prev_episode_true_objective = torch.zeros_like(self.true_objective)
self.total_successes = 0
self.total_resets = 0
# object apply random forces parameters
self.force_decay = to_torch(self.force_decay, dtype=torch.float, device=self.device)
self.force_prob_range = to_torch(self.force_prob_range, dtype=torch.float, device=self.device)
self.random_force_prob = torch.exp(
(torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(self.num_envs, device=self.device)
+ torch.log(self.force_prob_range[1])
)
self.rb_forces = torch.zeros((self.num_envs, self.num_bodies, 3), dtype=torch.float, device=self.device)
self.action_torques = torch.zeros((self.num_envs, self.num_bodies, 3), dtype=torch.float, device=self.device)
self.obj_keypoint_pos = torch.zeros(
(self.num_envs, self.num_keypoints, 3), dtype=torch.float, device=self.device
)
self.goal_keypoint_pos = torch.zeros(
(self.num_envs, self.num_keypoints, 3), dtype=torch.float, device=self.device
)
# how many steps we were within the goal tolerance
self.near_goal_steps = torch.zeros(self.num_envs, dtype=torch.int, device=self.device)
self.lifted_object = torch.zeros(self.num_envs, dtype=torch.bool, device=self.device)
self.closest_keypoint_max_dist = -torch.ones(self.num_envs, dtype=torch.float, device=self.device)
self.closest_fingertip_dist = -torch.ones(
[self.num_envs, self.num_arms, self.num_fingertips], dtype=torch.float, device=self.device
)
reward_keys = [
"raw_fingertip_delta_rew",
"raw_lifting_rew",
"raw_keypoint_rew",
"fingertip_delta_rew",
"lifting_rew",
"lift_bonus_rew",
"keypoint_rew",
"bonus_rew",
]
self.rewards_episode = {
key: torch.zeros(self.num_envs, dtype=torch.float, device=self.device) for key in reward_keys
}
self.last_curriculum_update = 0
self.episode_root_state_tensors = [[] for _ in range(self.num_envs)]
self.episode_dof_states = [[] for _ in range(self.num_envs)]
self.eval_stats: bool = self.cfg["env"]["evalStats"]
if self.eval_stats:
self.last_success_step = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.success_time = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.total_num_resets = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.successes_count = torch.zeros(
self.max_consecutive_successes + 1, dtype=torch.float, device=self.device
)
from tensorboardX import SummaryWriter
self.eval_summary_dir = "./eval_summaries"
# remove the old directory if it exists
if os.path.exists(self.eval_summary_dir):
import shutil
shutil.rmtree(self.eval_summary_dir)
self.eval_summaries = SummaryWriter(self.eval_summary_dir, flush_secs=3)
# AllegroKukaBase abstract interface - to be overriden in derived classes
def _object_keypoint_offsets(self):
raise NotImplementedError()
def _object_start_pose(self, arms_y_ofs: float, table_pose_dy: float, table_pose_dz: float):
object_start_pose = gymapi.Transform()
object_start_pose.p = gymapi.Vec3()
object_start_pose.p.x = 0.0
pose_dy, pose_dz = table_pose_dy, table_pose_dz + 0.25
object_start_pose.p.y = arms_y_ofs + pose_dy
object_start_pose.p.z = pose_dz
return object_start_pose
def _main_object_assets_and_scales(self, object_asset_root, tmp_assets_dir):
object_asset_files, object_asset_scales = self._box_asset_files_and_scales(object_asset_root, tmp_assets_dir)
if not self.randomize_object_dimensions:
object_asset_files = object_asset_files[:1]
object_asset_scales = object_asset_scales[:1]
# randomize order
files_and_scales = list(zip(object_asset_files, object_asset_scales))
# use fixed seed here to make sure when we restart from checkpoint the distribution of object types is the same
rng = np.random.default_rng(42)
rng.shuffle(files_and_scales)
object_asset_files, object_asset_scales = zip(*files_and_scales)
return object_asset_files, object_asset_scales
def _load_main_object_asset(self):
"""Load manipulated object and goal assets."""
object_asset_options = gymapi.AssetOptions()
object_assets = []
for object_asset_file in self.object_asset_files:
object_asset_dir = os.path.dirname(object_asset_file)
object_asset_fname = os.path.basename(object_asset_file)
object_asset_ = self.gym.load_asset(self.sim, object_asset_dir, object_asset_fname, object_asset_options)
object_assets.append(object_asset_)
object_rb_count = self.gym.get_asset_rigid_body_count(
object_assets[0]
) # assuming all of them have the same rb count
object_shapes_count = self.gym.get_asset_rigid_shape_count(
object_assets[0]
) # assuming all of them have the same rb count
return object_assets, object_rb_count, object_shapes_count
def _load_additional_assets(self, object_asset_root, arm_y_offset: float) -> Tuple[int, int]:
"""
returns: tuple (num_rigid_bodies, num_shapes)
"""
return 0, 0
def _create_additional_objects(self, env_ptr, env_idx, object_asset_idx):
pass
def _after_envs_created(self):
pass
def _extra_reset_rules(self, resets):
return resets
def _reset_target(self, env_ids: Tensor) -> None:
raise NotImplementedError()
def _extra_object_indices(self, env_ids: Tensor) -> List[Tensor]:
return []
def _extra_curriculum(self):
pass
# AllegroKukaBase implementation
def get_env_state(self):
"""
Return serializable environment state to be saved to checkpoint.
Can be used for stateful training sessions, i.e. with adaptive curriculums.
"""
return dict(
success_tolerance=self.success_tolerance,
)
def set_env_state(self, env_state):
if env_state is None:
return
for key in self.get_env_state().keys():
value = env_state.get(key, None)
if value is None:
continue
self.__dict__[key] = value
print(f"Loaded env state value {key}:{value}")
print(f"Success tolerance value after loading from checkpoint: {self.success_tolerance}")
# noinspection PyMethodOverriding
def create_sim(self):
self.dt = self.sim_params.dt
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2 (same as in allegro_hand.py)
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]["envSpacing"], int(np.sqrt(self.num_envs)))
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _box_asset_files_and_scales(self, object_assets_root, generated_assets_dir):
files = []
scales = []
try:
filenames = os.listdir(generated_assets_dir)
for fname in filenames:
if fname.endswith(".urdf"):
os.remove(join(generated_assets_dir, fname))
except Exception as exc:
print(f"Exception {exc} while removing older procedurally-generated urdf assets")
objects_rel_path = os.path.dirname(self.asset_files_dict[self.object_type])
objects_dir = join(object_assets_root, objects_rel_path)
base_mesh = join(objects_dir, "meshes", "cube_multicolor.obj")
generate_default_cube(generated_assets_dir, base_mesh, self.object_base_size)
if self.with_small_cuboids:
generate_small_cuboids(generated_assets_dir, base_mesh, self.object_base_size)
if self.with_big_cuboids:
generate_big_cuboids(generated_assets_dir, base_mesh, self.object_base_size)
if self.with_sticks:
generate_sticks(generated_assets_dir, base_mesh, self.object_base_size)
filenames = os.listdir(generated_assets_dir)
filenames = sorted(filenames)
for fname in filenames:
if fname.endswith(".urdf"):
scale_tokens = os.path.splitext(fname)[0].split("_")[2:]
files.append(join(generated_assets_dir, fname))
scales.append([float(scale_token) / 100 for scale_token in scale_tokens])
return files, scales
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../../assets")
object_asset_root = asset_root
tmp_assets_dir = tempfile.TemporaryDirectory()
self.object_asset_files, self.object_asset_scales = self._main_object_assets_and_scales(
object_asset_root, tmp_assets_dir.name
)
asset_options = gymapi.AssetOptions()
asset_options.fix_base_link = True
asset_options.flip_visual_attachments = False
asset_options.collapse_fixed_joints = True
asset_options.disable_gravity = True
asset_options.thickness = 0.001
asset_options.angular_damping = 0.01
asset_options.linear_damping = 0.01
if self.physics_engine == gymapi.SIM_PHYSX:
asset_options.use_physx_armature = True
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS
print(f"Loading asset {self.hand_arm_asset_file} from {asset_root}")
allegro_kuka_asset = self.gym.load_asset(self.sim, asset_root, self.hand_arm_asset_file, asset_options)
print(f"Loaded asset {allegro_kuka_asset}")
num_hand_arm_bodies = self.gym.get_asset_rigid_body_count(allegro_kuka_asset)
num_hand_arm_shapes = self.gym.get_asset_rigid_shape_count(allegro_kuka_asset)
num_hand_arm_dofs = self.gym.get_asset_dof_count(allegro_kuka_asset)
assert (
self.num_hand_arm_dofs == num_hand_arm_dofs
), f"Number of DOFs in asset {allegro_kuka_asset} is {num_hand_arm_dofs}, but {self.num_hand_arm_dofs} was expected"
max_agg_bodies = all_arms_bodies = num_hand_arm_bodies * self.num_arms
max_agg_shapes = all_arms_shapes = num_hand_arm_shapes * self.num_arms
allegro_rigid_body_names = [
self.gym.get_asset_rigid_body_name(allegro_kuka_asset, i) for i in range(num_hand_arm_bodies)
]
print(f"Allegro num rigid bodies: {num_hand_arm_bodies}")
print(f"Allegro rigid bodies: {allegro_rigid_body_names}")
# allegro_actuated_dof_names = [self.gym.get_asset_actuator_joint_name(allegro_asset, i) for i in range(self.num_allegro_dofs)]
# self.allegro_actuated_dof_indices = [self.gym.find_asset_dof_index(allegro_asset, name) for name in allegro_actuated_dof_names]
hand_arm_dof_props = self.gym.get_asset_dof_properties(allegro_kuka_asset)
arm_hand_dof_lower_limits = []
arm_hand_dof_upper_limits = []
for arm_idx in range(self.num_arms):
for i in range(self.num_hand_arm_dofs):
arm_hand_dof_lower_limits.append(hand_arm_dof_props["lower"][i])
arm_hand_dof_upper_limits.append(hand_arm_dof_props["upper"][i])
# self.allegro_actuated_dof_indices = to_torch(self.allegro_actuated_dof_indices, dtype=torch.long, device=self.device)
self.arm_hand_dof_lower_limits = to_torch(arm_hand_dof_lower_limits, device=self.device)
self.arm_hand_dof_upper_limits = to_torch(arm_hand_dof_upper_limits, device=self.device)
arm_poses = [gymapi.Transform() for _ in range(self.num_arms)]
arm_x_ofs, arm_y_ofs = self.arm_x_ofs, self.arm_y_ofs
for arm_idx, arm_pose in enumerate(arm_poses):
x_ofs = arm_x_ofs * (-1 if arm_idx == 0 else 1)
arm_pose.p = gymapi.Vec3(*get_axis_params(0.0, self.up_axis_idx)) + gymapi.Vec3(x_ofs, arm_y_ofs, 0)
# arm_pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0)
if arm_idx == 0:
# rotate 1st arm 90 degrees to the left
arm_pose.r = gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 0, 1), math.pi / 2)
else:
# rotate 2nd arm 90 degrees to the right
arm_pose.r = gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 0, 1), -math.pi / 2)
object_assets, object_rb_count, object_shapes_count = self._load_main_object_asset()
max_agg_bodies += object_rb_count
max_agg_shapes += object_shapes_count
# load auxiliary objects
table_asset_options = gymapi.AssetOptions()
table_asset_options.disable_gravity = False
table_asset_options.fix_base_link = True
table_asset = self.gym.load_asset(self.sim, asset_root, self.asset_files_dict["table"], table_asset_options)
table_pose = gymapi.Transform()
table_pose.p = gymapi.Vec3()
table_pose.p.x = 0.0
# table_pose_dy, table_pose_dz = -0.8, 0.38
table_pose_dy, table_pose_dz = 0.0, 0.38
table_pose.p.y = arm_y_ofs + table_pose_dy
table_pose.p.z = table_pose_dz
table_rb_count = self.gym.get_asset_rigid_body_count(table_asset)
table_shapes_count = self.gym.get_asset_rigid_shape_count(table_asset)
max_agg_bodies += table_rb_count
max_agg_shapes += table_shapes_count
additional_rb, additional_shapes = self._load_additional_assets(object_asset_root, arm_y_ofs)
max_agg_bodies += additional_rb
max_agg_shapes += additional_shapes
# set up object and goal positions
self.object_start_pose = self._object_start_pose(arm_y_ofs, table_pose_dy, table_pose_dz)
self.envs = []
object_init_state = []
object_scales = []
object_keypoint_offsets = []
allegro_palm_handle = self.gym.find_asset_rigid_body_index(allegro_kuka_asset, "iiwa7_link_7")
fingertip_handles = [
self.gym.find_asset_rigid_body_index(allegro_kuka_asset, name) for name in self.allegro_fingertips
]
self.allegro_palm_handles = []
self.allegro_fingertip_handles = []
for arm_idx in range(self.num_arms):
self.allegro_palm_handles.append(allegro_palm_handle + arm_idx * num_hand_arm_bodies)
self.allegro_fingertip_handles.extend([h + arm_idx * num_hand_arm_bodies for h in fingertip_handles])
# does this rely on the fact that objects are added right after the arms in terms of create_actor()?
self.object_rb_handles = list(range(all_arms_bodies, all_arms_bodies + object_rb_count))
self.arm_indices = torch.empty([self.num_envs, self.num_arms], dtype=torch.long, device=self.device)
self.object_indices = torch.empty(self.num_envs, dtype=torch.long, device=self.device)
assert self.num_envs >= 1
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row)
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
# add arms
for arm_idx in range(self.num_arms):
arm = self.gym.create_actor(env_ptr, allegro_kuka_asset, arm_poses[arm_idx], f"arm{arm_idx}", i, -1, 0)
populate_dof_properties(hand_arm_dof_props, self.dof_params, self.num_arm_dofs, self.num_hand_dofs)
self.gym.set_actor_dof_properties(env_ptr, arm, hand_arm_dof_props)
allegro_hand_idx = self.gym.get_actor_index(env_ptr, arm, gymapi.DOMAIN_SIM)
self.arm_indices[i, arm_idx] = allegro_hand_idx
# add object
object_asset_idx = i % len(object_assets)
object_asset = object_assets[object_asset_idx]
obj_pose = self.object_start_pose
object_handle = self.gym.create_actor(env_ptr, object_asset, obj_pose, "object", i, 0, 0)
pos, rot = obj_pose.p, obj_pose.r
object_init_state.append([pos.x, pos.y, pos.z, rot.x, rot.y, rot.z, rot.w, 0, 0, 0, 0, 0, 0])
object_idx = self.gym.get_actor_index(env_ptr, object_handle, gymapi.DOMAIN_SIM)
self.object_indices[i] = object_idx
object_scale = self.object_asset_scales[object_asset_idx]
object_scales.append(object_scale)
object_offsets = []
for keypoint in self.keypoints_offsets:
keypoint = copy(keypoint)
for coord_idx in range(3):
keypoint[coord_idx] *= object_scale[coord_idx] * self.object_base_size * self.keypoint_scale / 2
object_offsets.append(keypoint)
object_keypoint_offsets.append(object_offsets)
# table object
table_handle = self.gym.create_actor(env_ptr, table_asset, table_pose, "table_object", i, 0, 0)
_table_object_idx = self.gym.get_actor_index(env_ptr, table_handle, gymapi.DOMAIN_SIM)
# task-specific objects (i.e. goal object for reorientation task)
self._create_additional_objects(env_ptr, env_idx=i, object_asset_idx=object_asset_idx)
self.gym.end_aggregate(env_ptr)
self.envs.append(env_ptr)
# we are not using new mass values after DR when calculating random forces applied to an object,
# which should be ok as long as the randomization range is not too big
# noinspection PyUnboundLocalVariable
object_rb_props = self.gym.get_actor_rigid_body_properties(self.envs[0], object_handle)
self.object_rb_masses = [prop.mass for prop in object_rb_props]
self.object_init_state = to_torch(object_init_state, device=self.device, dtype=torch.float).view(
self.num_envs, 13
)
self.goal_states = self.object_init_state.clone()
self.goal_states[:, self.up_axis_idx] -= 0.04
self.goal_init_state = self.goal_states.clone()
self.allegro_fingertip_handles = to_torch(self.allegro_fingertip_handles, dtype=torch.long, device=self.device)
self.object_rb_handles = to_torch(self.object_rb_handles, dtype=torch.long, device=self.device)
self.object_rb_masses = to_torch(self.object_rb_masses, dtype=torch.float, device=self.device)
self.object_scales = to_torch(object_scales, dtype=torch.float, device=self.device)
self.object_keypoint_offsets = to_torch(object_keypoint_offsets, dtype=torch.float, device=self.device)
self._after_envs_created()
try:
# by this point we don't need the temporary folder for procedurally generated assets
tmp_assets_dir.cleanup()
except Exception:
pass
def _distance_delta_rewards(self, lifted_object: Tensor) -> Tensor:
"""Rewards for fingertips approaching the object or penalty for hand getting further away from the object."""
# this is positive if we got closer, negative if we're further away than the closest we've gotten
fingertip_deltas_closest = self.closest_fingertip_dist - self.curr_fingertip_distances
# update the values if finger tips got closer to the object
self.closest_fingertip_dist = torch.minimum(self.closest_fingertip_dist, self.curr_fingertip_distances)
# clip between zero and +inf to turn deltas into rewards
fingertip_deltas = torch.clip(fingertip_deltas_closest, 0, 10)
fingertip_delta_rew = torch.sum(fingertip_deltas, dim=-1)
fingertip_delta_rew = torch.sum(fingertip_delta_rew, dim=-1) # sum over all arms
# vvvv this is commented out for 2 arms: we want the 2nd arm to be relatively close at all times
# add this reward only before the object is lifted off the table
# after this, we should be guided only by keypoint and bonus rewards
# fingertip_delta_rew *= ~lifted_object
return fingertip_delta_rew
def _lifting_reward(self) -> Tuple[Tensor, Tensor, Tensor]:
"""Reward for lifting the object off the table."""
z_lift = 0.05 + self.object_pos[:, 2] - self.object_init_state[:, 2]
lifting_rew = torch.clip(z_lift, 0, 0.5)
# this flag tells us if we lifted an object above a certain height compared to the initial position
lifted_object = (z_lift > self.lifting_bonus_threshold) | self.lifted_object
# Since we stop rewarding the agent for height after the object is lifted, we should give it large positive reward
# to compensate for "lost" opportunity to get more lifting reward for sitting just below the threshold.
# This bonus depends on the max lifting reward (lifting reward coeff * threshold) and the discount factor
# (i.e. the effective future horizon for the agent)
# For threshold 0.15, lifting reward coeff = 3 and gamma 0.995 (effective horizon ~500 steps)
# a value of 300 for the bonus reward seems reasonable
just_lifted_above_threshold = lifted_object & ~self.lifted_object
lift_bonus_rew = self.lifting_bonus * just_lifted_above_threshold
# stop giving lifting reward once we crossed the threshold - now the agent can focus entirely on the
# keypoint reward
lifting_rew *= ~lifted_object
# update the flag that describes whether we lifted an object above the table or not
self.lifted_object = lifted_object
return lifting_rew, lift_bonus_rew, lifted_object
def _keypoint_reward(self, lifted_object: Tensor) -> Tensor:
# this is positive if we got closer, negative if we're further away
max_keypoint_deltas = self.closest_keypoint_max_dist - self.keypoints_max_dist
# update the values if we got closer to the target
self.closest_keypoint_max_dist = torch.minimum(self.closest_keypoint_max_dist, self.keypoints_max_dist)
# clip between zero and +inf to turn deltas into rewards
max_keypoint_deltas = torch.clip(max_keypoint_deltas, 0, 100)
# administer reward only when we already lifted an object from the table
# to prevent the situation where the agent just rolls it around the table
keypoint_rew = max_keypoint_deltas * lifted_object
return keypoint_rew
def _compute_resets(self, is_success):
resets = torch.where(self.object_pos[:, 2] < 0.1, torch.ones_like(self.reset_buf), self.reset_buf) # fall
if self.max_consecutive_successes > 0:
# Reset progress buffer if max_consecutive_successes > 0
self.progress_buf = torch.where(is_success > 0, torch.zeros_like(self.progress_buf), self.progress_buf)
resets = torch.where(self.successes >= self.max_consecutive_successes, torch.ones_like(resets), resets)
resets = torch.where(self.progress_buf >= self.max_episode_length - 1, torch.ones_like(resets), resets)
resets = self._extra_reset_rules(resets)
return resets
def _true_objective(self):
raise NotImplementedError()
def compute_kuka_reward(self) -> Tuple[Tensor, Tensor]:
lifting_rew, lift_bonus_rew, lifted_object = self._lifting_reward()
fingertip_delta_rew = self._distance_delta_rewards(lifted_object)
keypoint_rew = self._keypoint_reward(lifted_object)
keypoint_success_tolerance = self.success_tolerance * self.keypoint_scale
# noinspection PyTypeChecker
near_goal: Tensor = self.keypoints_max_dist <= keypoint_success_tolerance
self.near_goal_steps += near_goal
is_success = self.near_goal_steps >= self.success_steps
goal_resets = is_success
self.successes += is_success
self.reset_goal_buf[:] = goal_resets
self.rewards_episode["raw_fingertip_delta_rew"] += fingertip_delta_rew
self.rewards_episode["raw_lifting_rew"] += lifting_rew
self.rewards_episode["raw_keypoint_rew"] += keypoint_rew
fingertip_delta_rew *= self.distance_delta_rew_scale
lifting_rew *= self.lifting_rew_scale
keypoint_rew *= self.keypoint_rew_scale
# Success bonus: orientation is within `success_tolerance` of goal orientation
# We spread out the reward over "success_steps"
bonus_rew = near_goal * (self.reach_goal_bonus / self.success_steps)
reward = fingertip_delta_rew + lifting_rew + lift_bonus_rew + keypoint_rew + bonus_rew
self.rew_buf[:] = reward
resets = self._compute_resets(is_success)
self.reset_buf[:] = resets
self.extras["successes"] = self.prev_episode_successes.mean()
self.true_objective = self._true_objective()
self.extras["true_objective"] = self.true_objective
# scalars for logging
self.extras["true_objective_mean"] = self.true_objective.mean()
self.extras["true_objective_min"] = self.true_objective.min()
self.extras["true_objective_max"] = self.true_objective.max()
rewards = [
(fingertip_delta_rew, "fingertip_delta_rew"),
(lifting_rew, "lifting_rew"),
(lift_bonus_rew, "lift_bonus_rew"),
(keypoint_rew, "keypoint_rew"),
(bonus_rew, "bonus_rew"),
]
episode_cumulative = dict()
for rew_value, rew_name in rewards:
self.rewards_episode[rew_name] += rew_value
episode_cumulative[rew_name] = rew_value
self.extras["rewards_episode"] = self.rewards_episode
self.extras["episode_cumulative"] = episode_cumulative
return self.rew_buf, is_success
def _eval_stats(self, is_success: Tensor) -> None:
if self.eval_stats:
frame: int = self.frame_since_restart
n_frames = torch.empty_like(self.last_success_step).fill_(frame)
self.success_time = torch.where(is_success, n_frames - self.last_success_step, self.success_time)
self.last_success_step = torch.where(is_success, n_frames, self.last_success_step)
mask_ = self.success_time > 0
if any(mask_):
avg_time_mean = ((self.success_time * mask_).sum(dim=0) / mask_.sum(dim=0)).item()
else:
avg_time_mean = math.nan
self.total_resets = self.total_resets + self.reset_buf.sum()
self.total_successes = self.total_successes + (self.successes * self.reset_buf).sum()
self.total_num_resets += self.reset_buf
reset_ids = self.reset_buf.nonzero().squeeze()
last_successes = self.successes[reset_ids].long()
self.successes_count[last_successes] += 1
if frame % 100 == 0:
# The direct average shows the overall result more quickly, but slightly undershoots long term
# policy performance.
print(f"Max num successes: {self.successes.max().item()}")
print(f"Average consecutive successes: {self.prev_episode_successes.mean().item():.2f}")
print(f"Total num resets: {self.total_num_resets.sum().item()} --> {self.total_num_resets}")
print(f"Reset percentage: {(self.total_num_resets > 0).sum() / self.num_envs:.2%}")
print(f"Last ep successes: {self.prev_episode_successes.mean().item():.2f}")
print(f"Last ep true objective: {self.prev_episode_true_objective.mean().item():.2f}")
self.eval_summaries.add_scalar("last_ep_successes", self.prev_episode_successes.mean().item(), frame)
self.eval_summaries.add_scalar(
"last_ep_true_objective", self.prev_episode_true_objective.mean().item(), frame
)
self.eval_summaries.add_scalar(
"reset_stats/reset_percentage", (self.total_num_resets > 0).sum() / self.num_envs, frame
)
self.eval_summaries.add_scalar("reset_stats/min_num_resets", self.total_num_resets.min().item(), frame)
self.eval_summaries.add_scalar("policy_speed/avg_success_time_frames", avg_time_mean, frame)
frame_time = self.control_freq_inv * self.dt
self.eval_summaries.add_scalar(
"policy_speed/avg_success_time_seconds", avg_time_mean * frame_time, frame
)
self.eval_summaries.add_scalar(
"policy_speed/avg_success_per_minute", 60.0 / (avg_time_mean * frame_time), frame
)
print(f"Policy speed (successes per minute): {60.0 / (avg_time_mean * frame_time):.2f}")
# create a matplotlib bar chart of the self.successes_count
import matplotlib.pyplot as plt
plt.bar(list(range(self.max_consecutive_successes + 1)), self.successes_count.cpu().numpy())
plt.title("Successes histogram")
plt.xlabel("Successes")
plt.ylabel("Frequency")
plt.savefig(f"{self.eval_summary_dir}/successes_histogram.png")
plt.clf()
def compute_observations(self) -> Tuple[Tensor, int]:
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
self.object_state = self.root_state_tensor[self.object_indices, 0:13]
self.object_pose = self.root_state_tensor[self.object_indices, 0:7]
self.object_pos = self.root_state_tensor[self.object_indices, 0:3]
self.object_rot = self.root_state_tensor[self.object_indices, 3:7]
self.object_linvel = self.root_state_tensor[self.object_indices, 7:10]
self.object_angvel = self.root_state_tensor[self.object_indices, 10:13]
self.goal_pose = self.goal_states[:, 0:7]
self.goal_pos = self.goal_states[:, 0:3]
self.goal_rot = self.goal_states[:, 3:7]
self._palm_state = self.rigid_body_states[:, self.allegro_palm_handles]
palm_pos = self._palm_state[..., 0:3] # [num_envs, num_arms, 3]
self._palm_rot = self._palm_state[..., 3:7] # [num_envs, num_arms, 4]
for arm_idx in range(self.num_arms):
self.palm_center_pos[:, arm_idx] = palm_pos[:, arm_idx] + quat_rotate(
self._palm_rot[:, arm_idx], self.palm_center_offset
)
self.fingertip_state = self.rigid_body_states[:, self.allegro_fingertip_handles][:, :, 0:13]
self.fingertip_pos = self.fingertip_state[:, :, 0:3]
self.fingertip_rot = self.fingertip_state[:, :, 3:7]
if hasattr(self, "fingertip_pos_rel_object"):
self.fingertip_pos_rel_object_prev[:, :, :] = self.fingertip_pos_rel_object
else:
self.fingertip_pos_rel_object_prev = None
self.fingertip_pos_offset = torch.zeros_like(self.fingertip_pos).to(self.device)
for arm_idx in range(self.num_arms):
for i in range(self.num_fingertips):
finger_idx = arm_idx * self.num_fingertips + i
self.fingertip_pos_offset[:, finger_idx] = self.fingertip_pos[:, finger_idx] + quat_rotate(
self.fingertip_rot[:, finger_idx], self.fingertip_offsets[:, i]
)
obj_pos_repeat = self.object_pos.unsqueeze(1).repeat(1, self.num_arms * self.num_fingertips, 1)
self.fingertip_pos_rel_object = self.fingertip_pos_offset - obj_pos_repeat
self.curr_fingertip_distances = torch.norm(
self.fingertip_pos_rel_object.view(self.num_envs, self.num_arms, self.num_fingertips, -1), dim=-1
)
# when episode ends or target changes we reset this to -1, this will initialize it to the actual distance on the 1st frame of the episode
self.closest_fingertip_dist = torch.where(
self.closest_fingertip_dist < 0.0, self.curr_fingertip_distances, self.closest_fingertip_dist
)
palm_center_repeat = self.palm_center_pos.unsqueeze(2).repeat(
1, 1, self.num_fingertips, 1
) # [num_envs, num_arms, num_fingertips, 3] == [num_envs, 2, 4, 3]
self.fingertip_pos_rel_palm = self.fingertip_pos_offset - palm_center_repeat.view(
self.num_envs, self.num_arms * self.num_fingertips, 3
) # [num_envs, num_arms * num_fingertips, 3] == [num_envs, 8, 3]
if self.fingertip_pos_rel_object_prev is None:
self.fingertip_pos_rel_object_prev = self.fingertip_pos_rel_object.clone()
for i in range(self.num_keypoints):
self.obj_keypoint_pos[:, i] = self.object_pos + quat_rotate(
self.object_rot, self.object_keypoint_offsets[:, i]
)
self.goal_keypoint_pos[:, i] = self.goal_pos + quat_rotate(
self.goal_rot, self.object_keypoint_offsets[:, i]
)
self.keypoints_rel_goal = self.obj_keypoint_pos - self.goal_keypoint_pos
palm_center_repeat = self.palm_center_pos.unsqueeze(2).repeat(1, 1, self.num_keypoints, 1)
obj_kp_pos_repeat = self.obj_keypoint_pos.unsqueeze(1).repeat(1, self.num_arms, 1, 1)
self.keypoints_rel_palm = obj_kp_pos_repeat - palm_center_repeat
self.keypoints_rel_palm = self.keypoints_rel_palm.view(self.num_envs, self.num_arms * self.num_keypoints, 3)
# self.keypoints_rel_palm = self.obj_keypoint_pos - palm_center_repeat.view(
# self.num_envs, self.num_arms * self.num_keypoints, 3
# )
self.keypoint_distances_l2 = torch.norm(self.keypoints_rel_goal, dim=-1)
# furthest keypoint from the goal
self.keypoints_max_dist = self.keypoint_distances_l2.max(dim=-1).values
# this is the closest the keypoint had been to the target in the current episode (for the furthest keypoint of all)
# make sure we initialize this value before using it for obs or rewards
self.closest_keypoint_max_dist = torch.where(
self.closest_keypoint_max_dist < 0.0, self.keypoints_max_dist, self.closest_keypoint_max_dist
)
if self.obs_type == "full_state":
full_state_size, reward_obs_ofs = self.compute_full_state(self.obs_buf)
assert (
full_state_size == self.full_state_size
), f"Expected full state size {self.full_state_size}, actual: {full_state_size}"
return self.obs_buf, reward_obs_ofs
else:
raise ValueError("Unkown observations type!")
def compute_full_state(self, buf: Tensor) -> Tuple[int, int]:
num_dofs = self.num_hand_arm_dofs * self.num_arms
ofs: int = 0
# dof positions
buf[:, ofs : ofs + num_dofs] = unscale(
self.arm_hand_dof_pos[:, :num_dofs],
self.arm_hand_dof_lower_limits[:num_dofs],
self.arm_hand_dof_upper_limits[:num_dofs],
)
ofs += num_dofs
# dof velocities
buf[:, ofs : ofs + num_dofs] = self.arm_hand_dof_vel[:, :num_dofs]
ofs += num_dofs
# palm pos
num_palm_coords = 3 * self.num_arms
buf[:, ofs : ofs + num_palm_coords] = self.palm_center_pos.view(self.num_envs, num_palm_coords)
ofs += num_palm_coords
# palm rot, linvel, ang vel
num_palm_rot_vel_angvel = 10 * self.num_arms
buf[:, ofs : ofs + num_palm_rot_vel_angvel] = self._palm_state[..., 3:13].reshape(
self.num_envs, num_palm_rot_vel_angvel
)
ofs += num_palm_rot_vel_angvel
# object rot, linvel, ang vel
buf[:, ofs : ofs + 10] = self.object_state[:, 3:13]
ofs += 10
# fingertip pos relative to the palm of the hand
fingertip_rel_pos_size = 3 * self.num_arms * self.num_fingertips
buf[:, ofs : ofs + fingertip_rel_pos_size] = self.fingertip_pos_rel_palm.reshape(
self.num_envs, fingertip_rel_pos_size
)
ofs += fingertip_rel_pos_size
# keypoint distances relative to the palm of the hand
keypoint_rel_palm_size = 3 * self.num_arms * self.num_keypoints
buf[:, ofs : ofs + keypoint_rel_palm_size] = self.keypoints_rel_palm.reshape(
self.num_envs, keypoint_rel_palm_size
)
ofs += keypoint_rel_palm_size
# keypoint distances relative to the goal
keypoint_rel_pos_size = 3 * self.num_keypoints
buf[:, ofs : ofs + keypoint_rel_pos_size] = self.keypoints_rel_goal.reshape(
self.num_envs, keypoint_rel_pos_size
)
ofs += keypoint_rel_pos_size
# object scales
buf[:, ofs : ofs + 3] = self.object_scales
ofs += 3
# closest distance to the furthest of all keypoints achieved so far in this episode
buf[:, ofs : ofs + 1] = self.closest_keypoint_max_dist.unsqueeze(-1)
# print(f"closest_keypoint_max_dist: {self.closest_keypoint_max_dist[0]}")
ofs += 1
# commented out for 2-hand version to minimize the number of observations
# closest distance between a fingertip and an object achieved since last target reset
# this should help the critic predict the anticipated fingertip reward
# buf[:, ofs : ofs + self.num_fingertips] = self.closest_fingertip_dist
# print(f"closest_fingertip_dist: {self.closest_fingertip_dist[0]}")
# ofs += self.num_fingertips
# indicates whether we already lifted the object from the table or not, should help the critic be more accurate
buf[:, ofs : ofs + 1] = self.lifted_object.unsqueeze(-1)
# print(f"Lifted object: {self.lifted_object[0]}")
ofs += 1
# this should help the critic predict the future rewards better and anticipate the episode termination
buf[:, ofs : ofs + 1] = torch.log(self.progress_buf / 10 + 1).unsqueeze(-1)
ofs += 1
buf[:, ofs : ofs + 1] = torch.log(self.successes + 1).unsqueeze(-1)
ofs += 1
# actions
# buf[:, ofs : ofs + self.num_actions] = self.actions
# ofs += self.num_actions
# state_str = [f"{state.item():.3f}" for state in buf[0, : self.full_state_size]]
# print(' '.join(state_str))
# this is where we will add the reward observation
reward_obs_ofs = ofs
ofs += 1
assert ofs == self.full_state_size
return ofs, reward_obs_ofs
def clamp_obs(self, obs_buf: Tensor) -> None:
if self.clamp_abs_observations > 0:
obs_buf.clamp_(-self.clamp_abs_observations, self.clamp_abs_observations)
def get_random_quat(self, env_ids):
# https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py
# https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L261
uvw = torch_rand_float(0, 1.0, (len(env_ids), 3), device=self.device)
q_w = torch.sqrt(1.0 - uvw[:, 0]) * (torch.sin(2 * np.pi * uvw[:, 1]))
q_x = torch.sqrt(1.0 - uvw[:, 0]) * (torch.cos(2 * np.pi * uvw[:, 1]))
q_y = torch.sqrt(uvw[:, 0]) * (torch.sin(2 * np.pi * uvw[:, 2]))
q_z = torch.sqrt(uvw[:, 0]) * (torch.cos(2 * np.pi * uvw[:, 2]))
new_rot = torch.cat((q_x.unsqueeze(-1), q_y.unsqueeze(-1), q_z.unsqueeze(-1), q_w.unsqueeze(-1)), dim=-1)
return new_rot
def reset_target_pose(self, env_ids: Tensor) -> None:
self._reset_target(env_ids)
self.reset_goal_buf[env_ids] = 0
self.near_goal_steps[env_ids] = 0
self.closest_keypoint_max_dist[env_ids] = -1
def reset_object_pose(self, env_ids):
obj_indices = self.object_indices[env_ids]
# reset object
table_width = 1.1
obj_x_ofs = table_width / 2 - 0.2
left_right_random = torch_rand_float(-1.0, 1.0, (len(env_ids), 1), device=self.device)
x_pos = torch.where(
left_right_random > 0,
obj_x_ofs * torch.ones_like(left_right_random),
-obj_x_ofs * torch.ones_like(left_right_random),
)
rand_pos_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), 3), device=self.device)
self.root_state_tensor[obj_indices] = self.object_init_state[env_ids].clone()
# indices 0..2 correspond to the object position
self.root_state_tensor[obj_indices, 0:1] = x_pos + self.reset_position_noise_x * rand_pos_floats[:, 0:1]
self.root_state_tensor[obj_indices, 1:2] = (
self.object_init_state[env_ids, 1:2] + self.reset_position_noise_y * rand_pos_floats[:, 1:2]
)
self.root_state_tensor[obj_indices, 2:3] = (
self.object_init_state[env_ids, 2:3] + self.reset_position_noise_z * rand_pos_floats[:, 2:3]
)
new_object_rot = self.get_random_quat(env_ids)
# indices 3,4,5,6 correspond to the rotation quaternion
self.root_state_tensor[obj_indices, 3:7] = new_object_rot
self.root_state_tensor[obj_indices, 7:13] = torch.zeros_like(self.root_state_tensor[obj_indices, 7:13])
# since we reset the object, we also should update distances between fingers and the object
self.closest_fingertip_dist[env_ids] = -1
def deferred_set_actor_root_state_tensor_indexed(self, obj_indices: List[Tensor]) -> None:
self.set_actor_root_state_object_indices.extend(obj_indices)
def set_actor_root_state_tensor_indexed(self) -> None:
object_indices: List[Tensor] = self.set_actor_root_state_object_indices
if not object_indices:
# nothing to set
return
unique_object_indices = torch.unique(torch.cat(object_indices).to(torch.int32))
self.gym.set_actor_root_state_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.root_state_tensor),
gymtorch.unwrap_tensor(unique_object_indices),
len(unique_object_indices),
)
self.set_actor_root_state_object_indices = []
def reset_idx(self, env_ids: Tensor) -> None:
# randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
# randomize start object poses
self.reset_target_pose(env_ids)
# reset rigid body forces
self.rb_forces[env_ids, :, :] = 0.0
# reset object
self.reset_object_pose(env_ids)
# flattened list of arm actors that we need to reset
arm_indices = self.arm_indices[env_ids].to(torch.int32).flatten()
# reset random force probabilities
self.random_force_prob[env_ids] = torch.exp(
(torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(len(env_ids), device=self.device)
+ torch.log(self.force_prob_range[1])
)
# reset allegro hand
delta_max = self.arm_hand_dof_upper_limits - self.hand_arm_default_dof_pos
delta_min = self.arm_hand_dof_lower_limits - self.hand_arm_default_dof_pos
rand_dof_floats = torch_rand_float(
0.0, 1.0, (len(env_ids), self.num_arms * self.num_hand_arm_dofs), device=self.device
)
rand_delta = delta_min + (delta_max - delta_min) * rand_dof_floats
allegro_pos = self.hand_arm_default_dof_pos + self.pos_noise_coeff * rand_delta
self.arm_hand_dof_pos[env_ids, ...] = allegro_pos
self.prev_targets[env_ids, ...] = allegro_pos
self.cur_targets[env_ids, ...] = allegro_pos
rand_vel_floats = torch_rand_float(
-1.0, 1.0, (len(env_ids), self.num_hand_arm_dofs * self.num_arms), device=self.device
)
self.arm_hand_dof_vel[env_ids, :] = self.reset_dof_vel_noise * rand_vel_floats
arm_indices_gym = gymtorch.unwrap_tensor(arm_indices)
num_arm_indices: int = len(arm_indices)
self.gym.set_dof_position_target_tensor_indexed(
self.sim, gymtorch.unwrap_tensor(self.prev_targets), arm_indices_gym, num_arm_indices
)
self.gym.set_dof_state_tensor_indexed(
self.sim, gymtorch.unwrap_tensor(self.dof_state), arm_indices_gym, num_arm_indices
)
object_indices = [self.object_indices[env_ids]]
object_indices.extend(self._extra_object_indices(env_ids))
self.deferred_set_actor_root_state_tensor_indexed(object_indices)
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
self.prev_episode_successes[env_ids] = self.successes[env_ids]
self.successes[env_ids] = 0
self.prev_episode_true_objective[env_ids] = self.true_objective[env_ids]
self.true_objective[env_ids] = 0
self.lifted_object[env_ids] = False
# -1 here indicates that the value is not initialized
self.closest_keypoint_max_dist[env_ids] = -1
self.closest_fingertip_dist[env_ids] = -1
self.near_goal_steps[env_ids] = 0
for key in self.rewards_episode.keys():
# print(f"{env_ids}: {key}: {self.rewards_episode[key][env_ids]}")
self.rewards_episode[key][env_ids] = 0
self.extras["scalars"] = dict()
self.extras["scalars"]["success_tolerance"] = self.success_tolerance
def pre_physics_step(self, actions):
self.actions = actions.clone().to(self.device)
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
reset_goal_env_ids = self.reset_goal_buf.nonzero(as_tuple=False).squeeze(-1)
self.reset_target_pose(reset_goal_env_ids)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
self.set_actor_root_state_tensor_indexed()
if self.use_relative_control:
raise NotImplementedError("Use relative control False for now")
else:
# TODO: this uses simplified finger control compared to the original code of 1-hand env
num_dofs: int = self.num_hand_arm_dofs * self.num_arms
# target position control for the hand DOFs
self.cur_targets[..., :num_dofs] = scale(
actions[..., :num_dofs],
self.arm_hand_dof_lower_limits[:num_dofs],
self.arm_hand_dof_upper_limits[:num_dofs],
)
self.cur_targets[..., :num_dofs] = (
self.act_moving_average * self.cur_targets[..., :num_dofs]
+ (1.0 - self.act_moving_average) * self.prev_targets[..., :num_dofs]
)
self.cur_targets[..., :num_dofs] = tensor_clamp(
self.cur_targets[..., :num_dofs],
self.arm_hand_dof_lower_limits[:num_dofs],
self.arm_hand_dof_upper_limits[:num_dofs],
)
self.prev_targets[...] = self.cur_targets[...]
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(self.cur_targets))
if self.force_scale > 0.0:
self.rb_forces *= torch.pow(self.force_decay, self.dt / self.force_decay_interval)
# apply new forces
force_indices = (torch.rand(self.num_envs, device=self.device) < self.random_force_prob).nonzero()
self.rb_forces[force_indices, self.object_rb_handles, :] = (
torch.randn(self.rb_forces[force_indices, self.object_rb_handles, :].shape, device=self.device)
* self.object_rb_masses
* self.force_scale
)
self.gym.apply_rigid_body_force_tensors(
self.sim, gymtorch.unwrap_tensor(self.rb_forces), None, gymapi.LOCAL_SPACE
)
def post_physics_step(self):
self.frame_since_restart += 1
self.progress_buf += 1
self.randomize_buf += 1
self._extra_curriculum()
obs_buf, reward_obs_ofs = self.compute_observations()
rewards, is_success = self.compute_kuka_reward()
# add rewards to observations
reward_obs_scale = 0.01
obs_buf[:, reward_obs_ofs : reward_obs_ofs + 1] = rewards.unsqueeze(-1) * reward_obs_scale
self.clamp_obs(obs_buf)
self._eval_stats(is_success)
if self.viewer and self.debug_viz:
# draw axes on target object
self.gym.clear_lines(self.viewer)
self.gym.refresh_rigid_body_state_tensor(self.sim)
axes_geom = gymutil.AxesGeometry(0.1)
sphere_pose = gymapi.Transform()
sphere_pose.r = gymapi.Quat(0, 0, 0, 1)
sphere_geom = gymutil.WireframeSphereGeometry(0.01, 8, 8, sphere_pose, color=(1, 1, 0))
sphere_geom_white = gymutil.WireframeSphereGeometry(0.02, 8, 8, sphere_pose, color=(1, 1, 1))
palm_center_pos_cpu = self.palm_center_pos.cpu().numpy()
palm_rot_cpu = self._palm_rot.cpu().numpy()
for i in range(self.num_envs):
palm_center_transform = gymapi.Transform()
palm_center_transform.p = gymapi.Vec3(*palm_center_pos_cpu[i])
palm_center_transform.r = gymapi.Quat(*palm_rot_cpu[i])
gymutil.draw_lines(sphere_geom_white, self.gym, self.viewer, self.envs[i], palm_center_transform)
for j in range(self.num_fingertips):
fingertip_pos_cpu = self.fingertip_pos_offset[:, j].cpu().numpy()
fingertip_rot_cpu = self.fingertip_rot[:, j].cpu().numpy()
for i in range(self.num_envs):
fingertip_transform = gymapi.Transform()
fingertip_transform.p = gymapi.Vec3(*fingertip_pos_cpu[i])
fingertip_transform.r = gymapi.Quat(*fingertip_rot_cpu[i])
gymutil.draw_lines(sphere_geom, self.gym, self.viewer, self.envs[i], fingertip_transform)
for j in range(self.num_keypoints):
keypoint_pos_cpu = self.obj_keypoint_pos[:, j].cpu().numpy()
goal_keypoint_pos_cpu = self.goal_keypoint_pos[:, j].cpu().numpy()
for i in range(self.num_envs):
keypoint_transform = gymapi.Transform()
keypoint_transform.p = gymapi.Vec3(*keypoint_pos_cpu[i])
gymutil.draw_lines(sphere_geom, self.gym, self.viewer, self.envs[i], keypoint_transform)
goal_keypoint_transform = gymapi.Transform()
goal_keypoint_transform.p = gymapi.Vec3(*goal_keypoint_pos_cpu[i])
gymutil.draw_lines(sphere_geom, self.gym, self.viewer, self.envs[i], goal_keypoint_transform)
| 65,956 | Python | 45.579802 | 145 | 0.626099 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/allegro_kuka/allegro_kuka_base.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import io
import math
import os
import random
import tempfile
from copy import copy
from os.path import join
from typing import List, Tuple
from isaacgym import gymapi, gymtorch, gymutil
from torch import Tensor
from isaacgymenvs.tasks.allegro_kuka.allegro_kuka_utils import DofParameters, populate_dof_properties
from isaacgymenvs.tasks.base.vec_task import VecTask
from isaacgymenvs.tasks.allegro_kuka.generate_cuboids import (
generate_big_cuboids,
generate_default_cube,
generate_small_cuboids,
generate_sticks,
)
from isaacgymenvs.utils.torch_jit_utils import *
class AllegroKukaBase(VecTask):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.cfg = cfg
self.frame_since_restart: int = 0 # number of control steps since last restart across all actors
self.hand_arm_asset_file: str = self.cfg["env"]["asset"]["kukaAllegro"]
self.clamp_abs_observations: float = self.cfg["env"]["clampAbsObservations"]
self.privileged_actions = self.cfg["env"]["privilegedActions"]
self.privileged_actions_torque = self.cfg["env"]["privilegedActionsTorque"]
# 4 joints for index, middle, ring, and thumb and 7 for kuka arm
self.num_arm_dofs = 7
self.num_finger_dofs = 4
self.num_allegro_fingertips = 4
self.num_hand_dofs = self.num_finger_dofs * self.num_allegro_fingertips
self.num_hand_arm_dofs = self.num_hand_dofs + self.num_arm_dofs
self.num_allegro_kuka_actions = self.num_hand_arm_dofs
if self.privileged_actions:
self.num_allegro_kuka_actions += 3
self.randomize = self.cfg["task"]["randomize"]
self.randomization_params = self.cfg["task"]["randomization_params"]
self.distance_delta_rew_scale = self.cfg["env"]["distanceDeltaRewScale"]
self.lifting_rew_scale = self.cfg["env"]["liftingRewScale"]
self.lifting_bonus = self.cfg["env"]["liftingBonus"]
self.lifting_bonus_threshold = self.cfg["env"]["liftingBonusThreshold"]
self.keypoint_rew_scale = self.cfg["env"]["keypointRewScale"]
self.kuka_actions_penalty_scale = self.cfg["env"]["kukaActionsPenaltyScale"]
self.allegro_actions_penalty_scale = self.cfg["env"]["allegroActionsPenaltyScale"]
self.dof_params: DofParameters = DofParameters.from_cfg(self.cfg)
self.initial_tolerance = self.cfg["env"]["successTolerance"]
self.success_tolerance = self.initial_tolerance
self.target_tolerance = self.cfg["env"]["targetSuccessTolerance"]
self.tolerance_curriculum_increment = self.cfg["env"]["toleranceCurriculumIncrement"]
self.tolerance_curriculum_interval = self.cfg["env"]["toleranceCurriculumInterval"]
self.save_states = self.cfg["env"]["saveStates"]
self.save_states_filename = self.cfg["env"]["saveStatesFile"]
self.should_load_initial_states = self.cfg["env"]["loadInitialStates"]
self.load_states_filename = self.cfg["env"]["loadStatesFile"]
self.initial_root_state_tensors = self.initial_dof_state_tensors = None
self.initial_state_idx = self.num_initial_states = 0
self.reach_goal_bonus = self.cfg["env"]["reachGoalBonus"]
self.fall_dist = self.cfg["env"]["fallDistance"]
self.fall_penalty = self.cfg["env"]["fallPenalty"]
self.reset_position_noise_x = self.cfg["env"]["resetPositionNoiseX"]
self.reset_position_noise_y = self.cfg["env"]["resetPositionNoiseY"]
self.reset_position_noise_z = self.cfg["env"]["resetPositionNoiseZ"]
self.reset_rotation_noise = self.cfg["env"]["resetRotationNoise"]
self.reset_dof_pos_noise_fingers = self.cfg["env"]["resetDofPosRandomIntervalFingers"]
self.reset_dof_pos_noise_arm = self.cfg["env"]["resetDofPosRandomIntervalArm"]
self.reset_dof_vel_noise = self.cfg["env"]["resetDofVelRandomInterval"]
self.force_scale = self.cfg["env"].get("forceScale", 0.0)
self.force_prob_range = self.cfg["env"].get("forceProbRange", [0.001, 0.1])
self.force_decay = self.cfg["env"].get("forceDecay", 0.99)
self.force_decay_interval = self.cfg["env"].get("forceDecayInterval", 0.08)
self.hand_dof_speed_scale = self.cfg["env"]["dofSpeedScale"]
self.use_relative_control = self.cfg["env"]["useRelativeControl"]
self.act_moving_average = self.cfg["env"]["actionsMovingAverage"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.reset_time = self.cfg["env"].get("resetTime", -1.0)
self.max_consecutive_successes = self.cfg["env"]["maxConsecutiveSuccesses"]
self.success_steps: int = self.cfg["env"]["successSteps"]
# 1.0 means keypoints correspond to the corners of the object
# larger values help the agent to prioritize rotation matching
self.keypoint_scale = self.cfg["env"]["keypointScale"]
# size of the object (i.e. cube) before scaling
self.object_base_size = self.cfg["env"]["objectBaseSize"]
# whether to sample random object dimensions
self.randomize_object_dimensions = self.cfg["env"]["randomizeObjectDimensions"]
self.with_small_cuboids = self.cfg["env"]["withSmallCuboids"]
self.with_big_cuboids = self.cfg["env"]["withBigCuboids"]
self.with_sticks = self.cfg["env"]["withSticks"]
self.with_dof_force_sensors = False
# create fingertip force-torque sensors
self.with_fingertip_force_sensors = False
if self.reset_time > 0.0:
self.max_episode_length = int(round(self.reset_time / (self.control_freq_inv * self.sim_params.dt)))
print("Reset time: ", self.reset_time)
print("New episode length: ", self.max_episode_length)
self.object_type = self.cfg["env"]["objectType"]
assert self.object_type in ["block"]
self.asset_files_dict = {
"block": "urdf/objects/cube_multicolor.urdf", # 0.05m box
"table": "urdf/table_narrow.urdf",
"bucket": "urdf/objects/bucket.urdf",
"lightbulb": "lightbulb/A60_E27_SI.urdf",
"socket": "E27SocketSimple.urdf",
"ball": "urdf/objects/ball.urdf",
}
self.keypoints_offsets = self._object_keypoint_offsets()
self.num_keypoints = len(self.keypoints_offsets)
self.allegro_fingertips = ["index_link_3", "middle_link_3", "ring_link_3", "thumb_link_3"]
self.fingertip_offsets = np.array(
[[0.05, 0.005, 0], [0.05, 0.005, 0], [0.05, 0.005, 0], [0.06, 0.005, 0]], dtype=np.float32
)
self.palm_offset = np.array([-0.00, -0.02, 0.16], dtype=np.float32)
assert self.num_allegro_fingertips == len(self.allegro_fingertips)
# can be only "full_state"
self.obs_type = self.cfg["env"]["observationType"]
if not (self.obs_type in ["full_state"]):
raise Exception("Unknown type of observations!")
print("Obs type:", self.obs_type)
num_dof_pos = self.num_hand_arm_dofs
num_dof_vel = self.num_hand_arm_dofs
num_dof_forces = self.num_hand_arm_dofs if self.with_dof_force_sensors else 0
palm_pos_size = 3
palm_rot_vel_angvel_size = 10
obj_rot_vel_angvel_size = 10
fingertip_rel_pos_size = 3 * self.num_allegro_fingertips
keypoint_info_size = self.num_keypoints * 3 + self.num_keypoints * 3
object_scales_size = 3
max_keypoint_dist_size = 1
lifted_object_flag_size = 1
progress_obs_size = 1 + 1
closest_fingertip_distance_size = self.num_allegro_fingertips
reward_obs_size = 1
self.full_state_size = (
num_dof_pos
+ num_dof_vel
+ num_dof_forces
+ palm_pos_size
+ palm_rot_vel_angvel_size
+ obj_rot_vel_angvel_size
+ fingertip_rel_pos_size
+ keypoint_info_size
+ object_scales_size
+ max_keypoint_dist_size
+ lifted_object_flag_size
+ progress_obs_size
+ closest_fingertip_distance_size
+ reward_obs_size
# + self.num_allegro_actions
)
num_states = self.full_state_size
self.num_obs_dict = {
"full_state": self.full_state_size,
}
self.up_axis = "z"
self.fingertip_obs = True
self.cfg["env"]["numObservations"] = self.num_obs_dict[self.obs_type]
self.cfg["env"]["numStates"] = num_states
self.cfg["env"]["numActions"] = self.num_allegro_kuka_actions
self.cfg["device_type"] = sim_device.split(":")[0]
self.cfg["device_id"] = int(sim_device.split(":")[1])
self.cfg["headless"] = headless
super().__init__(
config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id,
headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render,
)
if self.viewer is not None:
cam_pos = gymapi.Vec3(10.0, 5.0, 1.0)
cam_target = gymapi.Vec3(6.0, 5.0, 0.0)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
# volume to sample target position from
target_volume_origin = np.array([0, 0.05, 0.8], dtype=np.float32)
target_volume_extent = np.array([[-0.4, 0.4], [-0.05, 0.3], [-0.12, 0.25]], dtype=np.float32)
self.target_volume_origin = torch.from_numpy(target_volume_origin).to(self.device).float()
self.target_volume_extent = torch.from_numpy(target_volume_extent).to(self.device).float()
# get gym GPU state tensors
actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
rigid_body_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
if self.obs_type == "full_state":
if self.with_fingertip_force_sensors:
sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(
self.num_envs, self.num_allegro_fingertips * 6
)
if self.with_dof_force_sensors:
dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(
self.num_envs, self.num_hand_arm_dofs
)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
# create some wrapper tensors for different slices
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.hand_arm_default_dof_pos = torch.zeros(self.num_hand_arm_dofs, dtype=torch.float, device=self.device)
desired_kuka_pos = torch.tensor([-1.571, 1.571, -0.000, 1.376, -0.000, 1.485, 2.358]) # pose v1
# desired_kuka_pos = torch.tensor([-2.135, 0.843, 1.786, -0.903, -2.262, 1.301, -2.791]) # pose v2
self.hand_arm_default_dof_pos[:7] = desired_kuka_pos
self.arm_hand_dof_state = self.dof_state.view(self.num_envs, -1, 2)[:, : self.num_hand_arm_dofs]
self.arm_hand_dof_pos = self.arm_hand_dof_state[..., 0]
self.arm_hand_dof_vel = self.arm_hand_dof_state[..., 1]
self.rigid_body_states = gymtorch.wrap_tensor(rigid_body_tensor).view(self.num_envs, -1, 13)
self.num_bodies = self.rigid_body_states.shape[1]
self.root_state_tensor = gymtorch.wrap_tensor(actor_root_state_tensor).view(-1, 13)
self.set_actor_root_state_object_indices: List[Tensor] = []
self.num_dofs = self.gym.get_sim_dof_count(self.sim) // self.num_envs
self.prev_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.cur_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.global_indices = torch.arange(self.num_envs * 3, dtype=torch.int32, device=self.device).view(
self.num_envs, -1
)
self.x_unit_tensor = to_torch([1, 0, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.y_unit_tensor = to_torch([0, 1, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.z_unit_tensor = to_torch([0, 0, 1], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.reset_goal_buf = self.reset_buf.clone()
self.successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.prev_episode_successes = torch.zeros_like(self.successes)
# true objective value for the whole episode, plus saving values for the previous episode
self.true_objective = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.prev_episode_true_objective = torch.zeros_like(self.true_objective)
self.total_successes = 0
self.total_resets = 0
# object apply random forces parameters
self.force_decay = to_torch(self.force_decay, dtype=torch.float, device=self.device)
self.force_prob_range = to_torch(self.force_prob_range, dtype=torch.float, device=self.device)
self.random_force_prob = torch.exp(
(torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(self.num_envs, device=self.device)
+ torch.log(self.force_prob_range[1])
)
self.rb_forces = torch.zeros((self.num_envs, self.num_bodies, 3), dtype=torch.float, device=self.device)
self.action_torques = torch.zeros((self.num_envs, self.num_bodies, 3), dtype=torch.float, device=self.device)
self.obj_keypoint_pos = torch.zeros(
(self.num_envs, self.num_keypoints, 3), dtype=torch.float, device=self.device
)
self.goal_keypoint_pos = torch.zeros(
(self.num_envs, self.num_keypoints, 3), dtype=torch.float, device=self.device
)
# how many steps we were within the goal tolerance
self.near_goal_steps = torch.zeros(self.num_envs, dtype=torch.int, device=self.device)
self.lifted_object = torch.zeros(self.num_envs, dtype=torch.bool, device=self.device)
self.closest_keypoint_max_dist = -torch.ones(self.num_envs, dtype=torch.float, device=self.device)
self.closest_fingertip_dist = -torch.ones(
[self.num_envs, self.num_allegro_fingertips], dtype=torch.float, device=self.device
)
self.furthest_hand_dist = -torch.ones([self.num_envs], dtype=torch.float, device=self.device)
self.finger_rew_coeffs = torch.ones(
[self.num_envs, self.num_allegro_fingertips], dtype=torch.float, device=self.device
)
reward_keys = [
"raw_fingertip_delta_rew",
"raw_hand_delta_penalty",
"raw_lifting_rew",
"raw_keypoint_rew",
"fingertip_delta_rew",
"hand_delta_penalty",
"lifting_rew",
"lift_bonus_rew",
"keypoint_rew",
"bonus_rew",
"kuka_actions_penalty",
"allegro_actions_penalty",
]
self.rewards_episode = {
key: torch.zeros(self.num_envs, dtype=torch.float, device=self.device) for key in reward_keys
}
self.last_curriculum_update = 0
self.episode_root_state_tensors = [[] for _ in range(self.num_envs)]
self.episode_dof_states = [[] for _ in range(self.num_envs)]
self.eval_stats: bool = self.cfg["env"]["evalStats"]
if self.eval_stats:
self.last_success_step = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.success_time = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.total_num_resets = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.successes_count = torch.zeros(
self.max_consecutive_successes + 1, dtype=torch.float, device=self.device
)
from tensorboardX import SummaryWriter
self.eval_summary_dir = "./eval_summaries"
# remove the old directory if it exists
if os.path.exists(self.eval_summary_dir):
import shutil
shutil.rmtree(self.eval_summary_dir)
self.eval_summaries = SummaryWriter(self.eval_summary_dir, flush_secs=3)
# AllegroKukaBase abstract interface - to be overriden in derived classes
def _object_keypoint_offsets(self):
raise NotImplementedError()
def _object_start_pose(self, allegro_pose, table_pose_dy, table_pose_dz):
object_start_pose = gymapi.Transform()
object_start_pose.p = gymapi.Vec3()
object_start_pose.p.x = allegro_pose.p.x
pose_dy, pose_dz = table_pose_dy, table_pose_dz + 0.25
object_start_pose.p.y = allegro_pose.p.y + pose_dy
object_start_pose.p.z = allegro_pose.p.z + pose_dz
return object_start_pose
def _main_object_assets_and_scales(self, object_asset_root, tmp_assets_dir):
object_asset_files, object_asset_scales = self._box_asset_files_and_scales(object_asset_root, tmp_assets_dir)
if not self.randomize_object_dimensions:
object_asset_files = object_asset_files[:1]
object_asset_scales = object_asset_scales[:1]
# randomize order
files_and_scales = list(zip(object_asset_files, object_asset_scales))
# use fixed seed here to make sure when we restart from checkpoint the distribution of object types is the same
rng = np.random.default_rng(42)
rng.shuffle(files_and_scales)
object_asset_files, object_asset_scales = zip(*files_and_scales)
return object_asset_files, object_asset_scales
def _load_main_object_asset(self):
"""Load manipulated object and goal assets."""
object_asset_options = gymapi.AssetOptions()
object_assets = []
for object_asset_file in self.object_asset_files:
object_asset_dir = os.path.dirname(object_asset_file)
object_asset_fname = os.path.basename(object_asset_file)
object_asset_ = self.gym.load_asset(self.sim, object_asset_dir, object_asset_fname, object_asset_options)
object_assets.append(object_asset_)
object_rb_count = self.gym.get_asset_rigid_body_count(
object_assets[0]
) # assuming all of them have the same rb count
object_shapes_count = self.gym.get_asset_rigid_shape_count(
object_assets[0]
) # assuming all of them have the same rb count
return object_assets, object_rb_count, object_shapes_count
def _load_additional_assets(self, object_asset_root, arm_pose):
"""
returns: tuple (num_rigid_bodies, num_shapes)
"""
return 0, 0
def _create_additional_objects(self, env_ptr, env_idx, object_asset_idx):
pass
def _after_envs_created(self):
pass
def _extra_reset_rules(self, resets):
return resets
def _reset_target(self, env_ids: Tensor) -> None:
raise NotImplementedError()
def _extra_object_indices(self, env_ids: Tensor) -> List[Tensor]:
return []
def _extra_curriculum(self):
pass
# AllegroKukaBase implementation
def get_env_state(self):
"""
Return serializable environment state to be saved to checkpoint.
Can be used for stateful training sessions, i.e. with adaptive curriculums.
"""
return dict(
success_tolerance=self.success_tolerance,
)
def set_env_state(self, env_state):
if env_state is None:
return
for key in self.get_env_state().keys():
value = env_state.get(key, None)
if value is None:
continue
self.__dict__[key] = value
print(f"Loaded env state value {key}:{value}")
print(f"Success tolerance value after loading from checkpoint: {self.success_tolerance}")
def create_sim(self):
self.dt = self.sim_params.dt
self.up_axis_idx = 2 # index of up axis: Y=1, Z=2 (same as in allegro_hand.py)
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]["envSpacing"], int(np.sqrt(self.num_envs)))
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
self.gym.add_ground(self.sim, plane_params)
def _box_asset_files_and_scales(self, object_assets_root, generated_assets_dir):
files = []
scales = []
try:
filenames = os.listdir(generated_assets_dir)
for fname in filenames:
if fname.endswith(".urdf"):
os.remove(join(generated_assets_dir, fname))
except Exception as exc:
print(f"Exception {exc} while removing older procedurally-generated urdf assets")
objects_rel_path = os.path.dirname(self.asset_files_dict[self.object_type])
objects_dir = join(object_assets_root, objects_rel_path)
base_mesh = join(objects_dir, "meshes", "cube_multicolor.obj")
generate_default_cube(generated_assets_dir, base_mesh, self.object_base_size)
if self.with_small_cuboids:
generate_small_cuboids(generated_assets_dir, base_mesh, self.object_base_size)
if self.with_big_cuboids:
generate_big_cuboids(generated_assets_dir, base_mesh, self.object_base_size)
if self.with_sticks:
generate_sticks(generated_assets_dir, base_mesh, self.object_base_size)
filenames = os.listdir(generated_assets_dir)
filenames = sorted(filenames)
for fname in filenames:
if fname.endswith(".urdf"):
scale_tokens = os.path.splitext(fname)[0].split("_")[2:]
files.append(join(generated_assets_dir, fname))
scales.append([float(scale_token) / 100 for scale_token in scale_tokens])
return files, scales
def _create_envs(self, num_envs, spacing, num_per_row):
if self.should_load_initial_states:
self.load_initial_states()
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../../assets")
object_asset_root = asset_root
tmp_assets_dir = tempfile.TemporaryDirectory()
self.object_asset_files, self.object_asset_scales = self._main_object_assets_and_scales(
object_asset_root, tmp_assets_dir.name
)
asset_options = gymapi.AssetOptions()
asset_options.fix_base_link = True
asset_options.flip_visual_attachments = False
asset_options.collapse_fixed_joints = True
asset_options.disable_gravity = True
asset_options.thickness = 0.001
asset_options.angular_damping = 0.01
asset_options.linear_damping = 0.01
if self.physics_engine == gymapi.SIM_PHYSX:
asset_options.use_physx_armature = True
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS
print(f"Loading asset {self.hand_arm_asset_file} from {asset_root}")
allegro_kuka_asset = self.gym.load_asset(self.sim, asset_root, self.hand_arm_asset_file, asset_options)
print(f"Loaded asset {allegro_kuka_asset}")
self.num_hand_arm_bodies = self.gym.get_asset_rigid_body_count(allegro_kuka_asset)
self.num_hand_arm_shapes = self.gym.get_asset_rigid_shape_count(allegro_kuka_asset)
num_hand_arm_dofs = self.gym.get_asset_dof_count(allegro_kuka_asset)
assert (
self.num_hand_arm_dofs == num_hand_arm_dofs
), f"Number of DOFs in asset {allegro_kuka_asset} is {num_hand_arm_dofs}, but {self.num_hand_arm_dofs} was expected"
max_agg_bodies = self.num_hand_arm_bodies
max_agg_shapes = self.num_hand_arm_shapes
allegro_rigid_body_names = [
self.gym.get_asset_rigid_body_name(allegro_kuka_asset, i) for i in range(self.num_hand_arm_bodies)
]
print(f"Allegro num rigid bodies: {self.num_hand_arm_bodies}")
print(f"Allegro rigid bodies: {allegro_rigid_body_names}")
allegro_hand_dof_props = self.gym.get_asset_dof_properties(allegro_kuka_asset)
self.arm_hand_dof_lower_limits = []
self.arm_hand_dof_upper_limits = []
self.allegro_sensors = []
allegro_sensor_pose = gymapi.Transform()
for i in range(self.num_hand_arm_dofs):
self.arm_hand_dof_lower_limits.append(allegro_hand_dof_props["lower"][i])
self.arm_hand_dof_upper_limits.append(allegro_hand_dof_props["upper"][i])
self.arm_hand_dof_lower_limits = to_torch(self.arm_hand_dof_lower_limits, device=self.device)
self.arm_hand_dof_upper_limits = to_torch(self.arm_hand_dof_upper_limits, device=self.device)
allegro_pose = gymapi.Transform()
allegro_pose.p = gymapi.Vec3(*get_axis_params(0.0, self.up_axis_idx)) + gymapi.Vec3(0.0, 0.8, 0)
allegro_pose.r = gymapi.Quat(0, 0, 0, 1)
object_assets, object_rb_count, object_shapes_count = self._load_main_object_asset()
max_agg_bodies += object_rb_count
max_agg_shapes += object_shapes_count
# load auxiliary objects
table_asset_options = gymapi.AssetOptions()
table_asset_options.disable_gravity = False
table_asset_options.fix_base_link = True
table_asset = self.gym.load_asset(self.sim, asset_root, self.asset_files_dict["table"], table_asset_options)
table_pose = gymapi.Transform()
table_pose.p = gymapi.Vec3()
table_pose.p.x = allegro_pose.p.x
table_pose_dy, table_pose_dz = -0.8, 0.38
table_pose.p.y = allegro_pose.p.y + table_pose_dy
table_pose.p.z = allegro_pose.p.z + table_pose_dz
table_rb_count = self.gym.get_asset_rigid_body_count(table_asset)
table_shapes_count = self.gym.get_asset_rigid_shape_count(table_asset)
max_agg_bodies += table_rb_count
max_agg_shapes += table_shapes_count
additional_rb, additional_shapes = self._load_additional_assets(object_asset_root, allegro_pose)
max_agg_bodies += additional_rb
max_agg_shapes += additional_shapes
# set up object and goal positions
self.object_start_pose = self._object_start_pose(allegro_pose, table_pose_dy, table_pose_dz)
self.allegro_hands = []
self.envs = []
object_init_state = []
self.allegro_hand_indices = []
object_indices = []
object_scales = []
object_keypoint_offsets = []
self.allegro_fingertip_handles = [
self.gym.find_asset_rigid_body_index(allegro_kuka_asset, name) for name in self.allegro_fingertips
]
self.allegro_palm_handle = self.gym.find_asset_rigid_body_index(allegro_kuka_asset, "iiwa7_link_7")
# this rely on the fact that objects are added right after the arms in terms of create_actor()
self.object_rb_handles = list(range(self.num_hand_arm_bodies, self.num_hand_arm_bodies + object_rb_count))
for i in range(self.num_envs):
# create env instance
env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row)
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
allegro_actor = self.gym.create_actor(env_ptr, allegro_kuka_asset, allegro_pose, "allegro", i, -1, 0)
populate_dof_properties(allegro_hand_dof_props, self.dof_params, self.num_arm_dofs, self.num_hand_dofs)
self.gym.set_actor_dof_properties(env_ptr, allegro_actor, allegro_hand_dof_props)
allegro_hand_idx = self.gym.get_actor_index(env_ptr, allegro_actor, gymapi.DOMAIN_SIM)
self.allegro_hand_indices.append(allegro_hand_idx)
if self.obs_type == "full_state":
if self.with_fingertip_force_sensors:
for ft_handle in self.allegro_fingertip_handles:
env_sensors = [self.gym.create_force_sensor(env_ptr, ft_handle, allegro_sensor_pose)]
self.allegro_sensors.append(env_sensors)
if self.with_dof_force_sensors:
self.gym.enable_actor_dof_force_sensors(env_ptr, allegro_actor)
# add object
object_asset_idx = i % len(object_assets)
object_asset = object_assets[object_asset_idx]
object_handle = self.gym.create_actor(env_ptr, object_asset, self.object_start_pose, "object", i, 0, 0)
object_init_state.append(
[
self.object_start_pose.p.x,
self.object_start_pose.p.y,
self.object_start_pose.p.z,
self.object_start_pose.r.x,
self.object_start_pose.r.y,
self.object_start_pose.r.z,
self.object_start_pose.r.w,
0,
0,
0,
0,
0,
0,
]
)
object_idx = self.gym.get_actor_index(env_ptr, object_handle, gymapi.DOMAIN_SIM)
object_indices.append(object_idx)
object_scale = self.object_asset_scales[object_asset_idx]
object_scales.append(object_scale)
object_offsets = []
for keypoint in self.keypoints_offsets:
keypoint = copy(keypoint)
for coord_idx in range(3):
keypoint[coord_idx] *= object_scale[coord_idx] * self.object_base_size * self.keypoint_scale / 2
object_offsets.append(keypoint)
object_keypoint_offsets.append(object_offsets)
# table object
table_handle = self.gym.create_actor(env_ptr, table_asset, table_pose, "table_object", i, 0, 0)
table_object_idx = self.gym.get_actor_index(env_ptr, table_handle, gymapi.DOMAIN_SIM)
# task-specific objects (i.e. goal object for reorientation task)
self._create_additional_objects(env_ptr, env_idx=i, object_asset_idx=object_asset_idx)
self.gym.end_aggregate(env_ptr)
self.envs.append(env_ptr)
self.allegro_hands.append(allegro_actor)
# we are not using new mass values after DR when calculating random forces applied to an object,
# which should be ok as long as the randomization range is not too big
object_rb_props = self.gym.get_actor_rigid_body_properties(self.envs[0], object_handle)
self.object_rb_masses = [prop.mass for prop in object_rb_props]
self.object_init_state = to_torch(object_init_state, device=self.device, dtype=torch.float).view(
self.num_envs, 13
)
self.goal_states = self.object_init_state.clone()
self.goal_states[:, self.up_axis_idx] -= 0.04
self.goal_init_state = self.goal_states.clone()
self.allegro_fingertip_handles = to_torch(self.allegro_fingertip_handles, dtype=torch.long, device=self.device)
self.object_rb_handles = to_torch(self.object_rb_handles, dtype=torch.long, device=self.device)
self.object_rb_masses = to_torch(self.object_rb_masses, dtype=torch.float, device=self.device)
self.allegro_hand_indices = to_torch(self.allegro_hand_indices, dtype=torch.long, device=self.device)
self.object_indices = to_torch(object_indices, dtype=torch.long, device=self.device)
self.object_scales = to_torch(object_scales, dtype=torch.float, device=self.device)
self.object_keypoint_offsets = to_torch(object_keypoint_offsets, dtype=torch.float, device=self.device)
self._after_envs_created()
try:
# by this point we don't need the temporary folder for procedurally generated assets
tmp_assets_dir.cleanup()
except Exception:
pass
def _distance_delta_rewards(self, lifted_object: Tensor) -> Tuple[Tensor, Tensor]:
"""Rewards for fingertips approaching the object or penalty for hand getting further away from the object."""
# this is positive if we got closer, negative if we're further away than the closest we've gotten
fingertip_deltas_closest = self.closest_fingertip_dist - self.curr_fingertip_distances
# update the values if finger tips got closer to the object
self.closest_fingertip_dist = torch.minimum(self.closest_fingertip_dist, self.curr_fingertip_distances)
# again, positive is closer, negative is further away
# here we use index of the 1st finger, when the distance is large it doesn't matter which one we use
hand_deltas_furthest = self.furthest_hand_dist - self.curr_fingertip_distances[:, 0]
# update the values if finger tips got further away from the object
self.furthest_hand_dist = torch.maximum(self.furthest_hand_dist, self.curr_fingertip_distances[:, 0])
# clip between zero and +inf to turn deltas into rewards
fingertip_deltas = torch.clip(fingertip_deltas_closest, 0, 10)
fingertip_deltas *= self.finger_rew_coeffs
fingertip_delta_rew = torch.sum(fingertip_deltas, dim=-1)
# add this reward only before the object is lifted off the table
# after this, we should be guided only by keypoint and bonus rewards
fingertip_delta_rew *= ~lifted_object
# clip between zero and -inf to turn deltas into penalties
hand_delta_penalty = torch.clip(hand_deltas_furthest, -10, 0)
hand_delta_penalty *= ~lifted_object
# multiply by the number of fingers so two rewards are on the same scale
hand_delta_penalty *= self.num_allegro_fingertips
return fingertip_delta_rew, hand_delta_penalty
def _lifting_reward(self) -> Tuple[Tensor, Tensor, Tensor]:
"""Reward for lifting the object off the table."""
z_lift = 0.05 + self.object_pos[:, 2] - self.object_init_state[:, 2]
lifting_rew = torch.clip(z_lift, 0, 0.5)
# this flag tells us if we lifted an object above a certain height compared to the initial position
lifted_object = (z_lift > self.lifting_bonus_threshold) | self.lifted_object
# Since we stop rewarding the agent for height after the object is lifted, we should give it large positive reward
# to compensate for "lost" opportunity to get more lifting reward for sitting just below the threshold.
# This bonus depends on the max lifting reward (lifting reward coeff * threshold) and the discount factor
# (i.e. the effective future horizon for the agent)
# For threshold 0.15, lifting reward coeff = 3 and gamma 0.995 (effective horizon ~500 steps)
# a value of 300 for the bonus reward seems reasonable
just_lifted_above_threshold = lifted_object & ~self.lifted_object
lift_bonus_rew = self.lifting_bonus * just_lifted_above_threshold
# stop giving lifting reward once we crossed the threshold - now the agent can focus entirely on the
# keypoint reward
lifting_rew *= ~lifted_object
# update the flag that describes whether we lifted an object above the table or not
self.lifted_object = lifted_object
return lifting_rew, lift_bonus_rew, lifted_object
def _keypoint_reward(self, lifted_object: Tensor) -> Tensor:
# this is positive if we got closer, negative if we're further away
max_keypoint_deltas = self.closest_keypoint_max_dist - self.keypoints_max_dist
# update the values if we got closer to the target
self.closest_keypoint_max_dist = torch.minimum(self.closest_keypoint_max_dist, self.keypoints_max_dist)
# clip between zero and +inf to turn deltas into rewards
max_keypoint_deltas = torch.clip(max_keypoint_deltas, 0, 100)
# administer reward only when we already lifted an object from the table
# to prevent the situation where the agent just rolls it around the table
keypoint_rew = max_keypoint_deltas * lifted_object
return keypoint_rew
def _action_penalties(self) -> Tuple[Tensor, Tensor]:
kuka_actions_penalty = (
torch.sum(torch.abs(self.arm_hand_dof_vel[..., 0:7]), dim=-1) * self.kuka_actions_penalty_scale
)
allegro_actions_penalty = (
torch.sum(torch.abs(self.arm_hand_dof_vel[..., 7 : self.num_hand_arm_dofs]), dim=-1)
* self.allegro_actions_penalty_scale
)
return -1 * kuka_actions_penalty, -1 * allegro_actions_penalty
def _compute_resets(self, is_success):
resets = torch.where(self.object_pos[:, 2] < 0.1, torch.ones_like(self.reset_buf), self.reset_buf) # fall
if self.max_consecutive_successes > 0:
# Reset progress buffer if max_consecutive_successes > 0
self.progress_buf = torch.where(is_success > 0, torch.zeros_like(self.progress_buf), self.progress_buf)
resets = torch.where(self.successes >= self.max_consecutive_successes, torch.ones_like(resets), resets)
resets = torch.where(self.progress_buf >= self.max_episode_length - 1, torch.ones_like(resets), resets)
resets = self._extra_reset_rules(resets)
return resets
def _true_objective(self):
raise NotImplementedError()
def compute_kuka_reward(self) -> Tuple[Tensor, Tensor]:
lifting_rew, lift_bonus_rew, lifted_object = self._lifting_reward()
fingertip_delta_rew, hand_delta_penalty = self._distance_delta_rewards(lifted_object)
keypoint_rew = self._keypoint_reward(lifted_object)
keypoint_success_tolerance = self.success_tolerance * self.keypoint_scale
# noinspection PyTypeChecker
near_goal: Tensor = self.keypoints_max_dist <= keypoint_success_tolerance
self.near_goal_steps += near_goal
is_success = self.near_goal_steps >= self.success_steps
goal_resets = is_success
self.successes += is_success
self.reset_goal_buf[:] = goal_resets
self.rewards_episode["raw_fingertip_delta_rew"] += fingertip_delta_rew
self.rewards_episode["raw_hand_delta_penalty"] += hand_delta_penalty
self.rewards_episode["raw_lifting_rew"] += lifting_rew
self.rewards_episode["raw_keypoint_rew"] += keypoint_rew
fingertip_delta_rew *= self.distance_delta_rew_scale
hand_delta_penalty *= self.distance_delta_rew_scale * 0 # currently disabled
lifting_rew *= self.lifting_rew_scale
keypoint_rew *= self.keypoint_rew_scale
kuka_actions_penalty, allegro_actions_penalty = self._action_penalties()
# Success bonus: orientation is within `success_tolerance` of goal orientation
# We spread out the reward over "success_steps"
bonus_rew = near_goal * (self.reach_goal_bonus / self.success_steps)
reward = (
fingertip_delta_rew
+ hand_delta_penalty # + sign here because hand_delta_penalty is negative
+ lifting_rew
+ lift_bonus_rew
+ keypoint_rew
+ kuka_actions_penalty
+ allegro_actions_penalty
+ bonus_rew
)
self.rew_buf[:] = reward
resets = self._compute_resets(is_success)
self.reset_buf[:] = resets
self.extras["successes"] = self.prev_episode_successes.mean()
self.true_objective = self._true_objective()
self.extras["true_objective"] = self.true_objective
# scalars for logging
self.extras["true_objective_mean"] = self.true_objective.mean()
self.extras["true_objective_min"] = self.true_objective.min()
self.extras["true_objective_max"] = self.true_objective.max()
rewards = [
(fingertip_delta_rew, "fingertip_delta_rew"),
(hand_delta_penalty, "hand_delta_penalty"),
(lifting_rew, "lifting_rew"),
(lift_bonus_rew, "lift_bonus_rew"),
(keypoint_rew, "keypoint_rew"),
(kuka_actions_penalty, "kuka_actions_penalty"),
(allegro_actions_penalty, "allegro_actions_penalty"),
(bonus_rew, "bonus_rew"),
]
episode_cumulative = dict()
for rew_value, rew_name in rewards:
self.rewards_episode[rew_name] += rew_value
episode_cumulative[rew_name] = rew_value
self.extras["rewards_episode"] = self.rewards_episode
self.extras["episode_cumulative"] = episode_cumulative
return self.rew_buf, is_success
def _eval_stats(self, is_success: Tensor) -> None:
if self.eval_stats:
frame: int = self.frame_since_restart
n_frames = torch.empty_like(self.last_success_step).fill_(frame)
self.success_time = torch.where(is_success, n_frames - self.last_success_step, self.success_time)
self.last_success_step = torch.where(is_success, n_frames, self.last_success_step)
mask_ = self.success_time > 0
if any(mask_):
avg_time_mean = ((self.success_time * mask_).sum(dim=0) / mask_.sum(dim=0)).item()
else:
avg_time_mean = math.nan
self.total_resets = self.total_resets + self.reset_buf.sum()
self.total_successes = self.total_successes + (self.successes * self.reset_buf).sum()
self.total_num_resets += self.reset_buf
reset_ids = self.reset_buf.nonzero().squeeze()
last_successes = self.successes[reset_ids].long()
self.successes_count[last_successes] += 1
if frame % 100 == 0:
# The direct average shows the overall result more quickly, but slightly undershoots long term
# policy performance.
print(f"Max num successes: {self.successes.max().item()}")
print(f"Average consecutive successes: {self.prev_episode_successes.mean().item():.2f}")
print(f"Total num resets: {self.total_num_resets.sum().item()} --> {self.total_num_resets}")
print(f"Reset percentage: {(self.total_num_resets > 0).sum() / self.num_envs:.2%}")
print(f"Last ep successes: {self.prev_episode_successes.mean().item():.2f}")
print(f"Last ep true objective: {self.prev_episode_true_objective.mean().item():.2f}")
self.eval_summaries.add_scalar("last_ep_successes", self.prev_episode_successes.mean().item(), frame)
self.eval_summaries.add_scalar(
"last_ep_true_objective", self.prev_episode_true_objective.mean().item(), frame
)
self.eval_summaries.add_scalar(
"reset_stats/reset_percentage", (self.total_num_resets > 0).sum() / self.num_envs, frame
)
self.eval_summaries.add_scalar("reset_stats/min_num_resets", self.total_num_resets.min().item(), frame)
self.eval_summaries.add_scalar("policy_speed/avg_success_time_frames", avg_time_mean, frame)
frame_time = self.control_freq_inv * self.dt
self.eval_summaries.add_scalar(
"policy_speed/avg_success_time_seconds", avg_time_mean * frame_time, frame
)
self.eval_summaries.add_scalar(
"policy_speed/avg_success_per_minute", 60.0 / (avg_time_mean * frame_time), frame
)
print(f"Policy speed (successes per minute): {60.0 / (avg_time_mean * frame_time):.2f}")
# create a matplotlib bar chart of the self.successes_count
import matplotlib.pyplot as plt
plt.bar(list(range(self.max_consecutive_successes + 1)), self.successes_count.cpu().numpy())
plt.title("Successes histogram")
plt.xlabel("Successes")
plt.ylabel("Frequency")
plt.savefig(f"{self.eval_summary_dir}/successes_histogram.png")
plt.clf()
def compute_observations(self) -> Tuple[Tensor, int]:
self.gym.refresh_dof_state_tensor(self.sim)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
if self.obs_type == "full_state":
if self.with_fingertip_force_sensors:
self.gym.refresh_force_sensor_tensor(self.sim)
if self.with_dof_force_sensors:
self.gym.refresh_dof_force_tensor(self.sim)
self.object_state = self.root_state_tensor[self.object_indices, 0:13]
self.object_pose = self.root_state_tensor[self.object_indices, 0:7]
self.object_pos = self.root_state_tensor[self.object_indices, 0:3]
self.object_rot = self.root_state_tensor[self.object_indices, 3:7]
self.object_linvel = self.root_state_tensor[self.object_indices, 7:10]
self.object_angvel = self.root_state_tensor[self.object_indices, 10:13]
self.goal_pose = self.goal_states[:, 0:7]
self.goal_pos = self.goal_states[:, 0:3]
self.goal_rot = self.goal_states[:, 3:7]
self.palm_center_offset = torch.from_numpy(self.palm_offset).to(self.device).repeat((self.num_envs, 1))
self._palm_state = self.rigid_body_states[:, self.allegro_palm_handle][:, 0:13]
self._palm_pos = self.rigid_body_states[:, self.allegro_palm_handle][:, 0:3]
self._palm_rot = self.rigid_body_states[:, self.allegro_palm_handle][:, 3:7]
self.palm_center_pos = self._palm_pos + quat_rotate(self._palm_rot, self.palm_center_offset)
self.fingertip_state = self.rigid_body_states[:, self.allegro_fingertip_handles][:, :, 0:13]
self.fingertip_pos = self.rigid_body_states[:, self.allegro_fingertip_handles][:, :, 0:3]
self.fingertip_rot = self.rigid_body_states[:, self.allegro_fingertip_handles][:, :, 3:7]
if not isinstance(self.fingertip_offsets, torch.Tensor):
self.fingertip_offsets = (
torch.from_numpy(self.fingertip_offsets).to(self.device).repeat((self.num_envs, 1, 1))
)
if hasattr(self, "fingertip_pos_rel_object"):
self.fingertip_pos_rel_object_prev[:, :, :] = self.fingertip_pos_rel_object
else:
self.fingertip_pos_rel_object_prev = None
self.fingertip_pos_offset = torch.zeros_like(self.fingertip_pos).to(self.device)
for i in range(self.num_allegro_fingertips):
self.fingertip_pos_offset[:, i] = self.fingertip_pos[:, i] + quat_rotate(
self.fingertip_rot[:, i], self.fingertip_offsets[:, i]
)
obj_pos_repeat = self.object_pos.unsqueeze(1).repeat(1, self.num_allegro_fingertips, 1)
self.fingertip_pos_rel_object = self.fingertip_pos_offset - obj_pos_repeat
self.curr_fingertip_distances = torch.norm(self.fingertip_pos_rel_object, dim=-1)
# when episode ends or target changes we reset this to -1, this will initialize it to the actual distance on the 1st frame of the episode
self.closest_fingertip_dist = torch.where(
self.closest_fingertip_dist < 0.0, self.curr_fingertip_distances, self.closest_fingertip_dist
)
self.furthest_hand_dist = torch.where(
self.furthest_hand_dist < 0.0, self.curr_fingertip_distances[:, 0], self.furthest_hand_dist
)
palm_center_repeat = self.palm_center_pos.unsqueeze(1).repeat(1, self.num_allegro_fingertips, 1)
self.fingertip_pos_rel_palm = self.fingertip_pos_offset - palm_center_repeat
if self.fingertip_pos_rel_object_prev is None:
self.fingertip_pos_rel_object_prev = self.fingertip_pos_rel_object.clone()
for i in range(self.num_keypoints):
self.obj_keypoint_pos[:, i] = self.object_pos + quat_rotate(
self.object_rot, self.object_keypoint_offsets[:, i]
)
self.goal_keypoint_pos[:, i] = self.goal_pos + quat_rotate(
self.goal_rot, self.object_keypoint_offsets[:, i]
)
self.keypoints_rel_goal = self.obj_keypoint_pos - self.goal_keypoint_pos
palm_center_repeat = self.palm_center_pos.unsqueeze(1).repeat(1, self.num_keypoints, 1)
self.keypoints_rel_palm = self.obj_keypoint_pos - palm_center_repeat
self.keypoint_distances_l2 = torch.norm(self.keypoints_rel_goal, dim=-1)
# furthest keypoint from the goal
self.keypoints_max_dist = self.keypoint_distances_l2.max(dim=-1).values
# this is the closest the keypoint had been to the target in the current episode (for the furthest keypoint of all)
# make sure we initialize this value before using it for obs or rewards
self.closest_keypoint_max_dist = torch.where(
self.closest_keypoint_max_dist < 0.0, self.keypoints_max_dist, self.closest_keypoint_max_dist
)
if self.obs_type == "full_state":
full_state_size, reward_obs_ofs = self.compute_full_state(self.obs_buf)
assert (
full_state_size == self.full_state_size
), f"Expected full state size {self.full_state_size}, actual: {full_state_size}"
return self.obs_buf, reward_obs_ofs
else:
raise ValueError("Unkown observations type!")
def compute_full_state(self, buf: Tensor) -> Tuple[int, int]:
num_dofs = self.num_hand_arm_dofs
ofs = 0
# dof positions
buf[:, ofs : ofs + num_dofs] = unscale(
self.arm_hand_dof_pos[:, :num_dofs],
self.arm_hand_dof_lower_limits[:num_dofs],
self.arm_hand_dof_upper_limits[:num_dofs],
)
ofs += num_dofs
# dof velocities
buf[:, ofs : ofs + num_dofs] = self.arm_hand_dof_vel[:, :num_dofs]
ofs += num_dofs
if self.with_dof_force_sensors:
# dof forces
buf[:, ofs : ofs + num_dofs] = self.dof_force_tensor[:, :num_dofs]
ofs += num_dofs
# palm pos
buf[:, ofs : ofs + 3] = self.palm_center_pos
ofs += 3
# palm rot, linvel, ang vel
buf[:, ofs : ofs + 10] = self._palm_state[:, 3:13]
ofs += 10
# object rot, linvel, ang vel
buf[:, ofs : ofs + 10] = self.object_state[:, 3:13]
ofs += 10
# fingertip pos relative to the palm of the hand
fingertip_rel_pos_size = 3 * self.num_allegro_fingertips
buf[:, ofs : ofs + fingertip_rel_pos_size] = self.fingertip_pos_rel_palm.reshape(
self.num_envs, fingertip_rel_pos_size
)
ofs += fingertip_rel_pos_size
# keypoint distances relative to the palm of the hand
keypoint_rel_pos_size = 3 * self.num_keypoints
buf[:, ofs : ofs + keypoint_rel_pos_size] = self.keypoints_rel_palm.reshape(
self.num_envs, keypoint_rel_pos_size
)
ofs += keypoint_rel_pos_size
# keypoint distances relative to the goal
buf[:, ofs : ofs + keypoint_rel_pos_size] = self.keypoints_rel_goal.reshape(
self.num_envs, keypoint_rel_pos_size
)
ofs += keypoint_rel_pos_size
# object scales
buf[:, ofs : ofs + 3] = self.object_scales
ofs += 3
# closest distance to the furthest keypoint, achieved so far in this episode
buf[:, ofs : ofs + 1] = self.closest_keypoint_max_dist.unsqueeze(-1)
ofs += 1
# closest distance between a fingertip and an object achieved since last target reset
# this should help the critic predict the anticipated fingertip reward
buf[:, ofs : ofs + self.num_allegro_fingertips] = self.closest_fingertip_dist
ofs += self.num_allegro_fingertips
# indicates whether we already lifted the object from the table or not, should help the critic be more accurate
buf[:, ofs : ofs + 1] = self.lifted_object.unsqueeze(-1)
ofs += 1
# this should help the critic predict the future rewards better and anticipate the episode termination
buf[:, ofs : ofs + 1] = torch.log(self.progress_buf / 10 + 1).unsqueeze(-1)
ofs += 1
buf[:, ofs : ofs + 1] = torch.log(self.successes + 1).unsqueeze(-1)
ofs += 1
# this is where we will add the reward observation
reward_obs_ofs = ofs
ofs += 1
assert ofs == self.full_state_size
return ofs, reward_obs_ofs
def clamp_obs(self, obs_buf: Tensor) -> None:
if self.clamp_abs_observations > 0:
obs_buf.clamp_(-self.clamp_abs_observations, self.clamp_abs_observations)
def get_random_quat(self, env_ids):
# https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py
# https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L261
uvw = torch_rand_float(0, 1.0, (len(env_ids), 3), device=self.device)
q_w = torch.sqrt(1.0 - uvw[:, 0]) * (torch.sin(2 * np.pi * uvw[:, 1]))
q_x = torch.sqrt(1.0 - uvw[:, 0]) * (torch.cos(2 * np.pi * uvw[:, 1]))
q_y = torch.sqrt(uvw[:, 0]) * (torch.sin(2 * np.pi * uvw[:, 2]))
q_z = torch.sqrt(uvw[:, 0]) * (torch.cos(2 * np.pi * uvw[:, 2]))
new_rot = torch.cat((q_x.unsqueeze(-1), q_y.unsqueeze(-1), q_z.unsqueeze(-1), q_w.unsqueeze(-1)), dim=-1)
return new_rot
def reset_target_pose(self, env_ids: Tensor) -> None:
self._reset_target(env_ids)
self.reset_goal_buf[env_ids] = 0
self.near_goal_steps[env_ids] = 0
self.closest_keypoint_max_dist[env_ids] = -1
def reset_object_pose(self, env_ids):
obj_indices = self.object_indices[env_ids]
# reset object
rand_pos_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), 3), device=self.device)
self.root_state_tensor[obj_indices] = self.object_init_state[env_ids].clone()
# indices 0..2 correspond to the object position
self.root_state_tensor[obj_indices, 0:1] = (
self.object_init_state[env_ids, 0:1] + self.reset_position_noise_x * rand_pos_floats[:, 0:1]
)
self.root_state_tensor[obj_indices, 1:2] = (
self.object_init_state[env_ids, 1:2] + self.reset_position_noise_y * rand_pos_floats[:, 1:2]
)
self.root_state_tensor[obj_indices, 2:3] = (
self.object_init_state[env_ids, 2:3] + self.reset_position_noise_z * rand_pos_floats[:, 2:3]
)
new_object_rot = self.get_random_quat(env_ids)
# indices 3,4,5,6 correspond to the rotation quaternion
self.root_state_tensor[obj_indices, 3:7] = new_object_rot
self.root_state_tensor[obj_indices, 7:13] = torch.zeros_like(self.root_state_tensor[obj_indices, 7:13])
# since we reset the object, we also should update distances between fingers and the object
self.closest_fingertip_dist[env_ids] = -1
self.furthest_hand_dist[env_ids] = -1
def deferred_set_actor_root_state_tensor_indexed(self, obj_indices: List[Tensor]) -> None:
self.set_actor_root_state_object_indices.extend(obj_indices)
def set_actor_root_state_tensor_indexed(self) -> None:
object_indices: List[Tensor] = self.set_actor_root_state_object_indices
if not object_indices:
# nothing to set
return
unique_object_indices = torch.unique(torch.cat(object_indices).to(torch.int32))
self.gym.set_actor_root_state_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.root_state_tensor),
gymtorch.unwrap_tensor(unique_object_indices),
len(unique_object_indices),
)
self.set_actor_root_state_object_indices = []
def reset_idx(self, env_ids: Tensor) -> None:
# randomization can happen only at reset time, since it can reset actor positions on GPU
if self.randomize:
self.apply_randomizations(self.randomization_params)
# randomize start object poses
self.reset_target_pose(env_ids)
# reset rigid body forces
self.rb_forces[env_ids, :, :] = 0.0
# reset object
self.reset_object_pose(env_ids)
hand_indices = self.allegro_hand_indices[env_ids].to(torch.int32)
# reset random force probabilities
self.random_force_prob[env_ids] = torch.exp(
(torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(len(env_ids), device=self.device)
+ torch.log(self.force_prob_range[1])
)
# reset allegro hand
delta_max = self.arm_hand_dof_upper_limits - self.hand_arm_default_dof_pos
delta_min = self.arm_hand_dof_lower_limits - self.hand_arm_default_dof_pos
rand_dof_floats = torch_rand_float(0.0, 1.0, (len(env_ids), self.num_hand_arm_dofs), device=self.device)
rand_delta = delta_min + (delta_max - delta_min) * rand_dof_floats
noise_coeff = torch.zeros_like(self.hand_arm_default_dof_pos, device=self.device)
noise_coeff[0:7] = self.reset_dof_pos_noise_arm
noise_coeff[7 : self.num_hand_arm_dofs] = self.reset_dof_pos_noise_fingers
allegro_pos = self.hand_arm_default_dof_pos + noise_coeff * rand_delta
self.arm_hand_dof_pos[env_ids, :] = allegro_pos
rand_vel_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), self.num_hand_arm_dofs), device=self.device)
self.arm_hand_dof_vel[env_ids, :] = self.reset_dof_vel_noise * rand_vel_floats
self.prev_targets[env_ids, : self.num_hand_arm_dofs] = allegro_pos
self.cur_targets[env_ids, : self.num_hand_arm_dofs] = allegro_pos
if self.should_load_initial_states:
if len(env_ids) > self.num_initial_states:
print(f"Not enough initial states to load {len(env_ids)}/{self.num_initial_states}...")
else:
if self.initial_state_idx + len(env_ids) > self.num_initial_states:
self.initial_state_idx = 0
dof_states_to_load = self.initial_dof_state_tensors[
self.initial_state_idx : self.initial_state_idx + len(env_ids)
]
self.dof_state.reshape([self.num_envs, -1, *self.dof_state.shape[1:]])[
env_ids
] = dof_states_to_load.clone()
root_state_tensors_to_load = self.initial_root_state_tensors[
self.initial_state_idx : self.initial_state_idx + len(env_ids)
]
cube_object_idx = self.object_indices[0]
self.root_state_tensor.reshape([self.num_envs, -1, *self.root_state_tensor.shape[1:]])[
env_ids, cube_object_idx
] = root_state_tensors_to_load[:, cube_object_idx].clone()
self.initial_state_idx += len(env_ids)
self.gym.set_dof_position_target_tensor_indexed(
self.sim, gymtorch.unwrap_tensor(self.prev_targets), gymtorch.unwrap_tensor(hand_indices), len(env_ids)
)
self.gym.set_dof_state_tensor_indexed(
self.sim, gymtorch.unwrap_tensor(self.dof_state), gymtorch.unwrap_tensor(hand_indices), len(env_ids)
)
object_indices = [self.object_indices[env_ids]]
object_indices.extend(self._extra_object_indices(env_ids))
self.deferred_set_actor_root_state_tensor_indexed(object_indices)
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
self.prev_episode_successes[env_ids] = self.successes[env_ids]
self.successes[env_ids] = 0
self.prev_episode_true_objective[env_ids] = self.true_objective[env_ids]
self.true_objective[env_ids] = 0
self.lifted_object[env_ids] = False
# -1 here indicates that the value is not initialized
self.closest_keypoint_max_dist[env_ids] = -1
self.closest_fingertip_dist[env_ids] = -1
self.furthest_hand_dist[env_ids] = -1
self.near_goal_steps[env_ids] = 0
for key in self.rewards_episode.keys():
self.rewards_episode[key][env_ids] = 0
if self.save_states:
self.dump_env_states(env_ids)
self.extras["scalars"] = dict()
self.extras["scalars"]["success_tolerance"] = self.success_tolerance
def pre_physics_step(self, actions):
self.actions = actions.clone().to(self.device)
if self.privileged_actions:
torque_actions = actions[:, :3]
actions = actions[:, 3:]
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
reset_goal_env_ids = self.reset_goal_buf.nonzero(as_tuple=False).squeeze(-1)
self.reset_target_pose(reset_goal_env_ids)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
self.set_actor_root_state_tensor_indexed()
if self.use_relative_control:
raise NotImplementedError("Use relative control False for now")
else:
# target position control for the hand DOFs
self.cur_targets[:, 7 : self.num_hand_arm_dofs] = scale(
actions[:, 7 : self.num_hand_arm_dofs],
self.arm_hand_dof_lower_limits[7 : self.num_hand_arm_dofs],
self.arm_hand_dof_upper_limits[7 : self.num_hand_arm_dofs],
)
self.cur_targets[:, 7 : self.num_hand_arm_dofs] = (
self.act_moving_average * self.cur_targets[:, 7 : self.num_hand_arm_dofs]
+ (1.0 - self.act_moving_average) * self.prev_targets[:, 7 : self.num_hand_arm_dofs]
)
self.cur_targets[:, 7 : self.num_hand_arm_dofs] = tensor_clamp(
self.cur_targets[:, 7 : self.num_hand_arm_dofs],
self.arm_hand_dof_lower_limits[7 : self.num_hand_arm_dofs],
self.arm_hand_dof_upper_limits[7 : self.num_hand_arm_dofs],
)
targets = self.prev_targets[:, :7] + self.hand_dof_speed_scale * self.dt * self.actions[:, :7]
self.cur_targets[:, :7] = tensor_clamp(
targets, self.arm_hand_dof_lower_limits[:7], self.arm_hand_dof_upper_limits[:7]
)
self.prev_targets[:, :] = self.cur_targets[:, :]
self.gym.set_dof_position_target_tensor(self.sim, gymtorch.unwrap_tensor(self.cur_targets))
if self.force_scale > 0.0:
self.rb_forces *= torch.pow(self.force_decay, self.dt / self.force_decay_interval)
# apply new forces
force_indices = (torch.rand(self.num_envs, device=self.device) < self.random_force_prob).nonzero()
self.rb_forces[force_indices, self.object_rb_handles, :] = (
torch.randn(self.rb_forces[force_indices, self.object_rb_handles, :].shape, device=self.device)
* self.object_rb_masses
* self.force_scale
)
self.gym.apply_rigid_body_force_tensors(
self.sim, gymtorch.unwrap_tensor(self.rb_forces), None, gymapi.LOCAL_SPACE
)
# apply torques
if self.privileged_actions:
torque_actions = torque_actions.unsqueeze(1)
torque_amount = self.privileged_actions_torque
torque_actions *= torque_amount
self.action_torques[:, self.object_rb_handles, :] = torque_actions
self.gym.apply_rigid_body_force_tensors(
self.sim, None, gymtorch.unwrap_tensor(self.action_torques), gymapi.ENV_SPACE
)
def post_physics_step(self):
self.frame_since_restart += 1
self.progress_buf += 1
self.randomize_buf += 1
self._extra_curriculum()
obs_buf, reward_obs_ofs = self.compute_observations()
rewards, is_success = self.compute_kuka_reward()
# add rewards to observations
reward_obs_scale = 0.01
obs_buf[:, reward_obs_ofs : reward_obs_ofs + 1] = rewards.unsqueeze(-1) * reward_obs_scale
self.clamp_obs(obs_buf)
self._eval_stats(is_success)
if self.save_states:
self.accumulate_env_states()
if self.viewer and self.debug_viz:
# draw axes on target object
self.gym.clear_lines(self.viewer)
self.gym.refresh_rigid_body_state_tensor(self.sim)
axes_geom = gymutil.AxesGeometry(0.1)
sphere_pose = gymapi.Transform()
sphere_pose.r = gymapi.Quat(0, 0, 0, 1)
sphere_geom = gymutil.WireframeSphereGeometry(0.01, 8, 8, sphere_pose, color=(1, 1, 0))
sphere_geom_white = gymutil.WireframeSphereGeometry(0.02, 8, 8, sphere_pose, color=(1, 1, 1))
palm_center_pos_cpu = self.palm_center_pos.cpu().numpy()
palm_rot_cpu = self._palm_rot.cpu().numpy()
for i in range(self.num_envs):
palm_center_transform = gymapi.Transform()
palm_center_transform.p = gymapi.Vec3(*palm_center_pos_cpu[i])
palm_center_transform.r = gymapi.Quat(*palm_rot_cpu[i])
gymutil.draw_lines(sphere_geom_white, self.gym, self.viewer, self.envs[i], palm_center_transform)
for j in range(self.num_allegro_fingertips):
fingertip_pos_cpu = self.fingertip_pos_offset[:, j].cpu().numpy()
fingertip_rot_cpu = self.fingertip_rot[:, j].cpu().numpy()
for i in range(self.num_envs):
fingertip_transform = gymapi.Transform()
fingertip_transform.p = gymapi.Vec3(*fingertip_pos_cpu[i])
fingertip_transform.r = gymapi.Quat(*fingertip_rot_cpu[i])
gymutil.draw_lines(sphere_geom, self.gym, self.viewer, self.envs[i], fingertip_transform)
for j in range(self.num_keypoints):
keypoint_pos_cpu = self.obj_keypoint_pos[:, j].cpu().numpy()
goal_keypoint_pos_cpu = self.goal_keypoint_pos[:, j].cpu().numpy()
for i in range(self.num_envs):
keypoint_transform = gymapi.Transform()
keypoint_transform.p = gymapi.Vec3(*keypoint_pos_cpu[i])
gymutil.draw_lines(sphere_geom, self.gym, self.viewer, self.envs[i], keypoint_transform)
goal_keypoint_transform = gymapi.Transform()
goal_keypoint_transform.p = gymapi.Vec3(*goal_keypoint_pos_cpu[i])
gymutil.draw_lines(sphere_geom, self.gym, self.viewer, self.envs[i], goal_keypoint_transform)
def accumulate_env_states(self):
root_state_tensor = self.root_state_tensor.reshape(
[self.num_envs, -1, *self.root_state_tensor.shape[1:]]
).clone()
dof_state = self.dof_state.reshape([self.num_envs, -1, *self.dof_state.shape[1:]]).clone()
for env_idx in range(self.num_envs):
env_root_state_tensor = root_state_tensor[env_idx]
self.episode_root_state_tensors[env_idx].append(env_root_state_tensor)
env_dof_state = dof_state[env_idx]
self.episode_dof_states[env_idx].append(env_dof_state)
def dump_env_states(self, env_ids):
def write_tensor_to_bin_stream(tensor, stream):
bin_buff = io.BytesIO()
torch.save(tensor, bin_buff)
bin_buff = bin_buff.getbuffer()
stream.write(int(len(bin_buff)).to_bytes(4, "big"))
stream.write(bin_buff)
with open(self.save_states_filename, "ab") as save_states_file:
bin_stream = io.BytesIO()
for env_idx in env_ids:
ep_len = len(self.episode_root_state_tensors[env_idx])
if ep_len <= 20:
continue
states_to_save = min(ep_len // 10, 50)
state_indices = random.sample(range(ep_len), states_to_save)
print(f"Adding {states_to_save} states {state_indices}")
bin_stream.write(int(states_to_save).to_bytes(4, "big"))
root_states = [self.episode_root_state_tensors[env_idx][si] for si in state_indices]
dof_states = [self.episode_dof_states[env_idx][si] for si in state_indices]
root_states = torch.stack(root_states)
dof_states = torch.stack(dof_states)
write_tensor_to_bin_stream(root_states, bin_stream)
write_tensor_to_bin_stream(dof_states, bin_stream)
self.episode_root_state_tensors[env_idx] = []
self.episode_dof_states[env_idx] = []
bin_data = bin_stream.getbuffer()
if bin_data.nbytes > 0:
print(f"Writing {len(bin_data)} to file {self.save_states_filename}")
save_states_file.write(bin_data)
def load_initial_states(self):
loaded_root_states = []
loaded_dof_states = []
with open(self.load_states_filename, "rb") as states_file:
def read_nbytes(n_):
res = states_file.read(n_)
if len(res) < n_:
raise RuntimeError(
f"Could not read {n_} bytes from the binary file. Perhaps reached the end of file"
)
return res
while True:
try:
num_states = int.from_bytes(read_nbytes(4), byteorder="big")
print(f"num_states_chunk {num_states}")
root_states_len = int.from_bytes(read_nbytes(4), byteorder="big")
print(f"root tensors len {root_states_len}")
root_states_bytes = read_nbytes(root_states_len)
dof_states_len = int.from_bytes(read_nbytes(4), byteorder="big")
print(f"dof_states_len {dof_states_len}")
dof_states_bytes = read_nbytes(dof_states_len)
except Exception as exc:
print(exc)
break
finally:
# parse binary buffers
def parse_tensors(bin_data):
with io.BytesIO(bin_data) as buffer:
tensors = torch.load(buffer)
return tensors
root_state_tensors = parse_tensors(root_states_bytes)
dof_state_tensors = parse_tensors(dof_states_bytes)
loaded_root_states.append(root_state_tensors)
loaded_dof_states.append(dof_state_tensors)
self.initial_root_state_tensors = torch.cat(loaded_root_states)
self.initial_dof_state_tensors = torch.cat(loaded_dof_states)
assert self.initial_dof_state_tensors.shape[0] == self.initial_root_state_tensors.shape[0]
self.num_initial_states = len(self.initial_root_state_tensors)
print(f"{self.num_initial_states} states loaded from file {self.load_states_filename}!")
| 73,269 | Python | 44.994978 | 145 | 0.619785 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/allegro_kuka/allegro_kuka_two_arms_reorientation.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
from typing import List
import torch
from isaacgym import gymapi
from torch import Tensor
from isaacgymenvs.utils.torch_jit_utils import to_torch, torch_rand_float
from isaacgymenvs.tasks.allegro_kuka.allegro_kuka_two_arms import AllegroKukaTwoArmsBase
from isaacgymenvs.tasks.allegro_kuka.allegro_kuka_utils import tolerance_curriculum, tolerance_successes_objective
class AllegroKukaTwoArmsReorientation(AllegroKukaTwoArmsBase):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.goal_object_indices = []
self.goal_assets = []
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
def _object_keypoint_offsets(self):
return [
[1, 1, 1],
[1, 1, -1],
[-1, -1, 1],
[-1, -1, -1],
]
def _load_additional_assets(self, object_asset_root, arm_pose):
object_asset_options = gymapi.AssetOptions()
object_asset_options.disable_gravity = True
self.goal_assets = []
for object_asset_file in self.object_asset_files:
object_asset_dir = os.path.dirname(object_asset_file)
object_asset_fname = os.path.basename(object_asset_file)
goal_asset_ = self.gym.load_asset(self.sim, object_asset_dir, object_asset_fname, object_asset_options)
self.goal_assets.append(goal_asset_)
goal_rb_count = self.gym.get_asset_rigid_body_count(
self.goal_assets[0]
) # assuming all of them have the same rb count
goal_shapes_count = self.gym.get_asset_rigid_shape_count(
self.goal_assets[0]
) # assuming all of them have the same rb count
return goal_rb_count, goal_shapes_count
def _create_additional_objects(self, env_ptr, env_idx, object_asset_idx):
self.goal_displacement = gymapi.Vec3(-0.35, -0.06, 0.12)
self.goal_displacement_tensor = to_torch(
[self.goal_displacement.x, self.goal_displacement.y, self.goal_displacement.z], device=self.device
)
goal_start_pose = gymapi.Transform()
goal_start_pose.p = self.object_start_pose.p + self.goal_displacement
goal_start_pose.p.z -= 0.04
goal_asset = self.goal_assets[object_asset_idx]
goal_handle = self.gym.create_actor(
env_ptr, goal_asset, goal_start_pose, "goal_object", env_idx + self.num_envs, 0, 0
)
goal_object_idx = self.gym.get_actor_index(env_ptr, goal_handle, gymapi.DOMAIN_SIM)
self.goal_object_indices.append(goal_object_idx)
if self.object_type != "block":
self.gym.set_rigid_body_color(env_ptr, goal_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
def _after_envs_created(self):
self.goal_object_indices = to_torch(self.goal_object_indices, dtype=torch.long, device=self.device)
def _reset_target(self, env_ids: Tensor) -> None:
# sample random target location in some volume
target_volume_origin = self.target_volume_origin
target_volume_extent = self.target_volume_extent
target_volume_min_coord = target_volume_origin + target_volume_extent[:, 0]
target_volume_max_coord = target_volume_origin + target_volume_extent[:, 1]
target_volume_size = target_volume_max_coord - target_volume_min_coord
rand_pos_floats = torch_rand_float(0.0, 1.0, (len(env_ids), 3), device=self.device)
target_coords = target_volume_min_coord + rand_pos_floats * target_volume_size
# let the target be close to 1st or 2nd arm, randomly
left_right_random = torch_rand_float(-1.0, 1.0, (len(env_ids), 1), device=self.device)
x_ofs = 0.75
x_pos = torch.where(
left_right_random > 0,
x_ofs * torch.ones_like(left_right_random),
-x_ofs * torch.ones_like(left_right_random),
)
target_coords[:, 0] += x_pos.squeeze(dim=1)
self.goal_states[env_ids, 0:3] = target_coords
self.root_state_tensor[self.goal_object_indices[env_ids], 0:3] = self.goal_states[env_ids, 0:3]
# new_rot = randomize_rotation(
# rand_floats[:, 0], rand_floats[:, 1], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids]
# )
# new implementation by Ankur:
new_rot = self.get_random_quat(env_ids)
self.goal_states[env_ids, 3:7] = new_rot
self.root_state_tensor[self.goal_object_indices[env_ids], 3:7] = self.goal_states[env_ids, 3:7]
self.root_state_tensor[self.goal_object_indices[env_ids], 7:13] = torch.zeros_like(
self.root_state_tensor[self.goal_object_indices[env_ids], 7:13]
)
object_indices_to_reset = [self.goal_object_indices[env_ids]]
self.deferred_set_actor_root_state_tensor_indexed(object_indices_to_reset)
def _extra_object_indices(self, env_ids: Tensor) -> List[Tensor]:
return [self.goal_object_indices[env_ids]]
def _extra_curriculum(self):
self.success_tolerance, self.last_curriculum_update = tolerance_curriculum(
self.last_curriculum_update,
self.frame_since_restart,
self.tolerance_curriculum_interval,
self.prev_episode_successes,
self.success_tolerance,
self.initial_tolerance,
self.target_tolerance,
self.tolerance_curriculum_increment,
)
def _true_objective(self) -> Tensor:
true_objective = tolerance_successes_objective(
self.success_tolerance, self.initial_tolerance, self.target_tolerance, self.successes
)
return true_objective
| 7,306 | Python | 44.955975 | 120 | 0.673008 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/allegro_kuka/allegro_kuka_utils.py |
# Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import annotations
from dataclasses import dataclass
from typing import Tuple, Dict, List
from torch import Tensor
@dataclass
class DofParameters:
"""Joint/dof parameters."""
allegro_stiffness: float
kuka_stiffness: float
allegro_effort: float
kuka_effort: List[float] # separate per DOF
allegro_damping: float
kuka_damping: float
dof_friction: float
allegro_armature: float
kuka_armature: float
@staticmethod
def from_cfg(cfg: Dict) -> DofParameters:
return DofParameters(
allegro_stiffness=cfg["env"]["allegroStiffness"],
kuka_stiffness=cfg["env"]["kukaStiffness"],
allegro_effort=cfg["env"]["allegroEffort"],
kuka_effort=cfg["env"]["kukaEffort"],
allegro_damping=cfg["env"]["allegroDamping"],
kuka_damping=cfg["env"]["kukaDamping"],
dof_friction=cfg["env"]["dofFriction"],
allegro_armature=cfg["env"]["allegroArmature"],
kuka_armature=cfg["env"]["kukaArmature"],
)
def populate_dof_properties(hand_arm_dof_props, params: DofParameters, arm_dofs: int, hand_dofs: int) -> None:
assert len(hand_arm_dof_props["stiffness"]) == arm_dofs + hand_dofs
hand_arm_dof_props["stiffness"][0:arm_dofs].fill(params.kuka_stiffness)
hand_arm_dof_props["stiffness"][arm_dofs:].fill(params.allegro_stiffness)
assert len(params.kuka_effort) == arm_dofs
hand_arm_dof_props["effort"][0:arm_dofs] = params.kuka_effort
hand_arm_dof_props["effort"][arm_dofs:].fill(params.allegro_effort)
hand_arm_dof_props["damping"][0:arm_dofs].fill(params.kuka_damping)
hand_arm_dof_props["damping"][arm_dofs:].fill(params.allegro_damping)
if params.dof_friction >= 0:
hand_arm_dof_props["friction"].fill(params.dof_friction)
hand_arm_dof_props["armature"][0:arm_dofs].fill(params.kuka_armature)
hand_arm_dof_props["armature"][arm_dofs:].fill(params.allegro_armature)
def tolerance_curriculum(
last_curriculum_update: int,
frames_since_restart: int,
curriculum_interval: int,
prev_episode_successes: Tensor,
success_tolerance: float,
initial_tolerance: float,
target_tolerance: float,
tolerance_curriculum_increment: float,
) -> Tuple[float, int]:
"""
Returns: new tolerance, new last_curriculum_update
"""
if frames_since_restart - last_curriculum_update < curriculum_interval:
return success_tolerance, last_curriculum_update
mean_successes_per_episode = prev_episode_successes.mean()
if mean_successes_per_episode < 3.0:
# this policy is not good enough with the previous tolerance value, keep training for now...
return success_tolerance, last_curriculum_update
# decrease the tolerance now
success_tolerance *= tolerance_curriculum_increment
success_tolerance = min(success_tolerance, initial_tolerance)
success_tolerance = max(success_tolerance, target_tolerance)
print(f"Prev episode successes: {mean_successes_per_episode}, success tolerance: {success_tolerance}")
last_curriculum_update = frames_since_restart
return success_tolerance, last_curriculum_update
def interp_0_1(x_curr: float, x_initial: float, x_target: float) -> float:
"""
Outputs 1 when x_curr == x_target (curriculum completed)
Outputs 0 when x_curr == x_initial (just started training)
Interpolates value in between.
"""
span = x_initial - x_target
return (x_initial - x_curr) / span
def tolerance_successes_objective(
success_tolerance: float, initial_tolerance: float, target_tolerance: float, successes: Tensor
) -> Tensor:
"""
Objective for the PBT. This basically prioritizes tolerance over everything else when we
execute the curriculum, after that it's just #successes.
"""
# this grows from 0 to 1 as we reach the target tolerance
if initial_tolerance > target_tolerance:
# makeshift unit tests:
eps = 1e-5
assert abs(interp_0_1(initial_tolerance, initial_tolerance, target_tolerance)) < eps
assert abs(interp_0_1(target_tolerance, initial_tolerance, target_tolerance) - 1.0) < eps
mid_tolerance = (initial_tolerance + target_tolerance) / 2
assert abs(interp_0_1(mid_tolerance, initial_tolerance, target_tolerance) - 0.5) < eps
tolerance_objective = interp_0_1(success_tolerance, initial_tolerance, target_tolerance)
else:
tolerance_objective = 1.0
if success_tolerance > target_tolerance:
# add succeses with a small coefficient to differentiate between policies at the beginning of training
# increment in tolerance improvement should always give higher value than higher successes with the
# previous tolerance, that's why this coefficient is very small
true_objective = (successes * 0.01) + tolerance_objective
else:
# basically just the successes + tolerance objective so that true_objective never decreases when we cross
# the threshold
true_objective = successes + tolerance_objective
return true_objective
| 6,689 | Python | 41.075471 | 113 | 0.712214 |
Tbarkin121/GuardDog/isaac/IsaacGymEnvs/isaacgymenvs/tasks/allegro_kuka/allegro_kuka_regrasping.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from typing import List, Tuple
import torch
from isaacgym import gymapi
from torch import Tensor
from isaacgymenvs.utils.torch_jit_utils import to_torch, torch_rand_float
from isaacgymenvs.tasks.allegro_kuka.allegro_kuka_base import AllegroKukaBase
from isaacgymenvs.tasks.allegro_kuka.allegro_kuka_utils import tolerance_curriculum, tolerance_successes_objective
class AllegroKukaRegrasping(AllegroKukaBase):
def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):
self.goal_object_indices = []
self.goal_asset = None
super().__init__(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)
def _object_keypoint_offsets(self):
"""Regrasping task uses only a single object keypoint since we do not care about object orientation."""
return [[0, 0, 0]]
def _load_additional_assets(self, object_asset_root, arm_pose):
goal_asset_options = gymapi.AssetOptions()
goal_asset_options.disable_gravity = True
self.goal_asset = self.gym.load_asset(
self.sim, object_asset_root, self.asset_files_dict["ball"], goal_asset_options
)
goal_rb_count = self.gym.get_asset_rigid_body_count(self.goal_asset)
goal_shapes_count = self.gym.get_asset_rigid_shape_count(self.goal_asset)
return goal_rb_count, goal_shapes_count
def _create_additional_objects(self, env_ptr, env_idx, object_asset_idx):
goal_start_pose = gymapi.Transform()
goal_asset = self.goal_asset
goal_handle = self.gym.create_actor(
env_ptr, goal_asset, goal_start_pose, "goal_object", env_idx + self.num_envs, 0, 0
)
self.gym.set_actor_scale(env_ptr, goal_handle, 0.5)
self.gym.set_rigid_body_color(env_ptr, goal_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
goal_object_idx = self.gym.get_actor_index(env_ptr, goal_handle, gymapi.DOMAIN_SIM)
self.goal_object_indices.append(goal_object_idx)
def _after_envs_created(self):
self.goal_object_indices = to_torch(self.goal_object_indices, dtype=torch.long, device=self.device)
def _reset_target(self, env_ids: Tensor) -> None:
target_volume_origin = self.target_volume_origin
target_volume_extent = self.target_volume_extent
target_volume_min_coord = target_volume_origin + target_volume_extent[:, 0]
target_volume_max_coord = target_volume_origin + target_volume_extent[:, 1]
target_volume_size = target_volume_max_coord - target_volume_min_coord
rand_pos_floats = torch_rand_float(0.0, 1.0, (len(env_ids), 3), device=self.device)
target_coords = target_volume_min_coord + rand_pos_floats * target_volume_size
self.goal_states[env_ids, 0:3] = target_coords
self.root_state_tensor[self.goal_object_indices[env_ids], 0:3] = self.goal_states[env_ids, 0:3]
# we also reset the object to its initial position
self.reset_object_pose(env_ids)
# since we put the object back on the table, also reset the lifting reward
self.lifted_object[env_ids] = False
self.deferred_set_actor_root_state_tensor_indexed(
[self.object_indices[env_ids], self.goal_object_indices[env_ids]]
)
def _extra_object_indices(self, env_ids: Tensor) -> List[Tensor]:
return [self.goal_object_indices[env_ids]]
def compute_kuka_reward(self) -> Tuple[Tensor, Tensor]:
rew_buf, is_success = super().compute_kuka_reward() # TODO: customize reward?
return rew_buf, is_success
def _true_objective(self) -> Tensor:
true_objective = tolerance_successes_objective(
self.success_tolerance, self.initial_tolerance, self.target_tolerance, self.successes
)
return true_objective
def _extra_curriculum(self):
self.success_tolerance, self.last_curriculum_update = tolerance_curriculum(
self.last_curriculum_update,
self.frame_since_restart,
self.tolerance_curriculum_interval,
self.prev_episode_successes,
self.success_tolerance,
self.initial_tolerance,
self.target_tolerance,
self.tolerance_curriculum_increment,
)
| 5,893 | Python | 46.532258 | 120 | 0.702019 |
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