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NVIDIA-Omniverse/IsaacGymEnvs/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 |
NVIDIA-Omniverse/IsaacGymEnvs/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 |
NVIDIA-Omniverse/IsaacGymEnvs/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 |
NVIDIA-Omniverse/IsaacGymEnvs/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 |
NVIDIA-Omniverse/IsaacGymEnvs/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 |
NVIDIA-Omniverse/IsaacGymEnvs/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 |
NVIDIA-Omniverse/IsaacGymEnvs/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 |
NVIDIA-Omniverse/IsaacGymEnvs/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 |
NVIDIA-Omniverse/IsaacGymEnvs/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 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/mjcf_importer.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 poselib.skeleton.skeleton3d import SkeletonTree, SkeletonState
from poselib.visualization.common import plot_skeleton_state
# load in XML mjcf file and save zero rotation pose in npy format
xml_path = "../../../../assets/mjcf/nv_humanoid.xml"
skeleton = SkeletonTree.from_mjcf(xml_path)
zero_pose = SkeletonState.zero_pose(skeleton)
zero_pose.to_file("data/nv_humanoid.npy")
# visualize zero rotation pose
plot_skeleton_state(zero_pose) | 2,003 | Python | 49.099999 | 80 | 0.783325 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/generate_amp_humanoid_tpose.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 poselib.core.rotation3d import *
from poselib.skeleton.skeleton3d import SkeletonTree, SkeletonState
from poselib.visualization.common import plot_skeleton_state
"""
This scripts imports a MJCF XML file and converts the skeleton into a SkeletonTree format.
It then generates a zero rotation pose, and adjusts the pose into a T-Pose.
"""
# import MJCF file
xml_path = "../../../../assets/mjcf/amp_humanoid.xml"
skeleton = SkeletonTree.from_mjcf(xml_path)
# generate zero rotation pose
zero_pose = SkeletonState.zero_pose(skeleton)
# adjust pose into a T Pose
local_rotation = zero_pose.local_rotation
local_rotation[skeleton.index("left_upper_arm")] = quat_mul(
quat_from_angle_axis(angle=torch.tensor([90.0]), axis=torch.tensor([1.0, 0.0, 0.0]), degree=True),
local_rotation[skeleton.index("left_upper_arm")]
)
local_rotation[skeleton.index("right_upper_arm")] = quat_mul(
quat_from_angle_axis(angle=torch.tensor([-90.0]), axis=torch.tensor([1.0, 0.0, 0.0]), degree=True),
local_rotation[skeleton.index("right_upper_arm")]
)
translation = zero_pose.root_translation
translation += torch.tensor([0, 0, 0.9])
# save and visualize T-pose
zero_pose.to_file("data/amp_humanoid_tpose.npy")
plot_skeleton_state(zero_pose) | 2,816 | Python | 43.714285 | 104 | 0.763849 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/fbx_importer.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 json
from poselib.skeleton.skeleton3d import SkeletonTree, SkeletonState, SkeletonMotion
from poselib.visualization.common import plot_skeleton_state, plot_skeleton_motion_interactive
# source fbx file path
fbx_file = "data/01_01_cmu.fbx"
# import fbx file - make sure to provide a valid joint name for root_joint
motion = SkeletonMotion.from_fbx(
fbx_file_path=fbx_file,
root_joint="Hips",
fps=60
)
# save motion in npy format
motion.to_file("data/01_01_cmu.npy")
# visualize motion
plot_skeleton_motion_interactive(motion)
| 2,119 | Python | 40.568627 | 94 | 0.779141 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/README.md | # poselib
`poselib` is a library for loading, manipulating, and retargeting skeleton poses and motions. It is separated into three modules: `poselib.core` for basic data loading and tensor operations, `poselib.skeleton` for higher-level skeleton operations, and `poselib.visualization` for displaying skeleton poses. This library is built on top of the PyTorch framework and requires data to be in PyTorch tensors.
## poselib.core
- `poselib.core.rotation3d`: A set of Torch JIT functions for computing quaternions, transforms, and rotation/transformation matrices.
- `quat_*` manipulate and create quaternions in [x, y, z, w] format (where w is the real component).
- `transform_*` handle 7D transforms in [quat, pos] format.
- `rot_matrix_*` handle 3x3 rotation matrices.
- `euclidean_*` handle 4x4 Euclidean transformation matrices.
- `poselib.core.tensor_utils`: Provides loading and saving functions for PyTorch tensors.
## poselib.skeleton
- `poselib.skeleton.skeleton3d`: Utilities for loading and manipulating skeleton poses, and retargeting poses to different skeletons.
- `SkeletonTree` is a class that stores a skeleton as a tree structure. This describes the skeleton topology and joints.
- `SkeletonState` describes the static state of a skeleton, and provides both global and local joint angles.
- `SkeletonMotion` describes a time-series of skeleton states and provides utilities for computing joint velocities.
## poselib.visualization
- `poselib.visualization.common`: Functions used for visualizing skeletons interactively in `matplotlib`.
- In SkeletonState visualization, use key `q` to quit window.
- In interactive SkeletonMotion visualization, you can use the following key commands:
- `w` - loop animation
- `x` - play/pause animation
- `z` - previous frame
- `c` - next frame
- `n` - quit window
## Key Features
Poselib provides several key features for working with animation data. We list some of the frequently used ones here, and provide instructions and examples on their usage.
### Importing from FBX
Poselib supports importing skeletal animation sequences from .fbx format into a SkeletonMotion representation. To use this functionality, you will need to first set up the Python FBX SDK on your machine using the following instructions.
This package is necessary to read data from fbx files, which is a proprietary file format owned by Autodesk. The latest FBX SDK tested was FBX SDK 2020.2.1 for Python 3.7, which can be found on the Autodesk website: https://www.autodesk.com/developer-network/platform-technologies/fbx-sdk-2020-2-1.
Follow the instructions at https://help.autodesk.com/view/FBX/2020/ENU/?guid=FBX_Developer_Help_scripting_with_python_fbx_installing_python_fbx_html for download, install, and copy/paste instructions for the FBX Python SDK.
This repo provides an example script `fbx_importer.py` that shows usage of importing a .fbx file. Note that `SkeletonMotion.from_fbx()` takes in an optional parameter `root_joint`, which can be used to specify a joint in the skeleton tree as the root joint. If `root_joint` is not specified, we will default to using the first node in the FBX scene that contains animation data.
### Importing from MJCF
MJCF is a robotics file format supported by Isaac Gym. For convenience, we provide an API for importing MJCF assets into SkeletonTree definitions to represent the skeleton topology. An example script `mjcf_importer.py` is provided to show usage of this.
This can be helpful if motion sequences need to be retargeted to your simulation skeleton that's been created in MJCF format. Importing the file to SkeletonTree format will allow you to generate T-poses or other retargeting poses that can be used for retargeting. We also show an example of creating a T-Pose for our AMP Humanoid asset in `generate_amp_humanoid_tpose.py`.
### Retargeting Motions
Retargeting motions is important when your source data uses skeletons that have different morphologies than your target skeletons. We provide APIs for performing retarget of motion sequences in our SkeletonState and SkeletonMotion classes.
To use the retargeting API, users must provide the following information:
- source_motion: a SkeletonMotion npy representation of a motion sequence. The motion clip should use the same skeleton as the source T-Pose skeleton.
- target_motion_path: path to save the retargeted motion to
- source_tpose: a SkeletonState npy representation of the source skeleton in it's T-Pose state
- target_tpose: a SkeletonState npy representation of the target skeleton in it's T-Pose state (pose should match source T-Pose)
- joint_mapping: mapping of joint names from source to target
- rotation: root rotation offset from source to target skeleton (for transforming across different orientation axes), represented as a quaternion in XYZW order.
- scale: scale offset from source to target skeleton
We provide an example script `retarget_motion.py` to demonstrate usage of the retargeting API for the CMU Motion Capture Database. Note that the retargeting data for this script is stored in `data/configs/retarget_cmu_to_amp.json`.
Additionally, a SkeletonState T-Pose file and retargeting config file are also provided for the SFU Motion Capture Database. These can be found at `data/sfu_tpose.npy` and `data/configs/retarget_sfu_to_amp.json`.
### Documentation
We provide a description of the functions and classes available in poselib in the comments of the APIs. Please check them out for more details.
| 5,585 | Markdown | 86.281249 | 404 | 0.78299 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/retarget_motion.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 isaacgymenvs.utils.torch_jit_utils import quat_mul, quat_from_angle_axis
import torch
import json
import numpy as np
from poselib.core.rotation3d import *
from poselib.skeleton.skeleton3d import SkeletonTree, SkeletonState, SkeletonMotion
from poselib.visualization.common import plot_skeleton_state, plot_skeleton_motion_interactive
"""
This scripts shows how to retarget a motion clip from the source skeleton to a target skeleton.
Data required for retargeting are stored in a retarget config dictionary as a json file. This file contains:
- source_motion: a SkeletonMotion npy format representation of a motion sequence. The motion clip should use the same skeleton as the source T-Pose skeleton.
- target_motion_path: path to save the retargeted motion to
- source_tpose: a SkeletonState npy format representation of the source skeleton in it's T-Pose state
- target_tpose: a SkeletonState npy format representation of the target skeleton in it's T-Pose state (pose should match source T-Pose)
- joint_mapping: mapping of joint names from source to target
- rotation: root rotation offset from source to target skeleton (for transforming across different orientation axes), represented as a quaternion in XYZW order.
- scale: scale offset from source to target skeleton
"""
VISUALIZE = False
def project_joints(motion):
right_upper_arm_id = motion.skeleton_tree._node_indices["right_upper_arm"]
right_lower_arm_id = motion.skeleton_tree._node_indices["right_lower_arm"]
right_hand_id = motion.skeleton_tree._node_indices["right_hand"]
left_upper_arm_id = motion.skeleton_tree._node_indices["left_upper_arm"]
left_lower_arm_id = motion.skeleton_tree._node_indices["left_lower_arm"]
left_hand_id = motion.skeleton_tree._node_indices["left_hand"]
right_thigh_id = motion.skeleton_tree._node_indices["right_thigh"]
right_shin_id = motion.skeleton_tree._node_indices["right_shin"]
right_foot_id = motion.skeleton_tree._node_indices["right_foot"]
left_thigh_id = motion.skeleton_tree._node_indices["left_thigh"]
left_shin_id = motion.skeleton_tree._node_indices["left_shin"]
left_foot_id = motion.skeleton_tree._node_indices["left_foot"]
device = motion.global_translation.device
# right arm
right_upper_arm_pos = motion.global_translation[..., right_upper_arm_id, :]
right_lower_arm_pos = motion.global_translation[..., right_lower_arm_id, :]
right_hand_pos = motion.global_translation[..., right_hand_id, :]
right_shoulder_rot = motion.local_rotation[..., right_upper_arm_id, :]
right_elbow_rot = motion.local_rotation[..., right_lower_arm_id, :]
right_arm_delta0 = right_upper_arm_pos - right_lower_arm_pos
right_arm_delta1 = right_hand_pos - right_lower_arm_pos
right_arm_delta0 = right_arm_delta0 / torch.norm(right_arm_delta0, dim=-1, keepdim=True)
right_arm_delta1 = right_arm_delta1 / torch.norm(right_arm_delta1, dim=-1, keepdim=True)
right_elbow_dot = torch.sum(-right_arm_delta0 * right_arm_delta1, dim=-1)
right_elbow_dot = torch.clamp(right_elbow_dot, -1.0, 1.0)
right_elbow_theta = torch.acos(right_elbow_dot)
right_elbow_q = quat_from_angle_axis(-torch.abs(right_elbow_theta), torch.tensor(np.array([[0.0, 1.0, 0.0]]),
device=device, dtype=torch.float32))
right_elbow_local_dir = motion.skeleton_tree.local_translation[right_hand_id]
right_elbow_local_dir = right_elbow_local_dir / torch.norm(right_elbow_local_dir)
right_elbow_local_dir_tile = torch.tile(right_elbow_local_dir.unsqueeze(0), [right_elbow_rot.shape[0], 1])
right_elbow_local_dir0 = quat_rotate(right_elbow_rot, right_elbow_local_dir_tile)
right_elbow_local_dir1 = quat_rotate(right_elbow_q, right_elbow_local_dir_tile)
right_arm_dot = torch.sum(right_elbow_local_dir0 * right_elbow_local_dir1, dim=-1)
right_arm_dot = torch.clamp(right_arm_dot, -1.0, 1.0)
right_arm_theta = torch.acos(right_arm_dot)
right_arm_theta = torch.where(right_elbow_local_dir0[..., 1] <= 0, right_arm_theta, -right_arm_theta)
right_arm_q = quat_from_angle_axis(right_arm_theta, right_elbow_local_dir.unsqueeze(0))
right_shoulder_rot = quat_mul(right_shoulder_rot, right_arm_q)
# left arm
left_upper_arm_pos = motion.global_translation[..., left_upper_arm_id, :]
left_lower_arm_pos = motion.global_translation[..., left_lower_arm_id, :]
left_hand_pos = motion.global_translation[..., left_hand_id, :]
left_shoulder_rot = motion.local_rotation[..., left_upper_arm_id, :]
left_elbow_rot = motion.local_rotation[..., left_lower_arm_id, :]
left_arm_delta0 = left_upper_arm_pos - left_lower_arm_pos
left_arm_delta1 = left_hand_pos - left_lower_arm_pos
left_arm_delta0 = left_arm_delta0 / torch.norm(left_arm_delta0, dim=-1, keepdim=True)
left_arm_delta1 = left_arm_delta1 / torch.norm(left_arm_delta1, dim=-1, keepdim=True)
left_elbow_dot = torch.sum(-left_arm_delta0 * left_arm_delta1, dim=-1)
left_elbow_dot = torch.clamp(left_elbow_dot, -1.0, 1.0)
left_elbow_theta = torch.acos(left_elbow_dot)
left_elbow_q = quat_from_angle_axis(-torch.abs(left_elbow_theta), torch.tensor(np.array([[0.0, 1.0, 0.0]]),
device=device, dtype=torch.float32))
left_elbow_local_dir = motion.skeleton_tree.local_translation[left_hand_id]
left_elbow_local_dir = left_elbow_local_dir / torch.norm(left_elbow_local_dir)
left_elbow_local_dir_tile = torch.tile(left_elbow_local_dir.unsqueeze(0), [left_elbow_rot.shape[0], 1])
left_elbow_local_dir0 = quat_rotate(left_elbow_rot, left_elbow_local_dir_tile)
left_elbow_local_dir1 = quat_rotate(left_elbow_q, left_elbow_local_dir_tile)
left_arm_dot = torch.sum(left_elbow_local_dir0 * left_elbow_local_dir1, dim=-1)
left_arm_dot = torch.clamp(left_arm_dot, -1.0, 1.0)
left_arm_theta = torch.acos(left_arm_dot)
left_arm_theta = torch.where(left_elbow_local_dir0[..., 1] <= 0, left_arm_theta, -left_arm_theta)
left_arm_q = quat_from_angle_axis(left_arm_theta, left_elbow_local_dir.unsqueeze(0))
left_shoulder_rot = quat_mul(left_shoulder_rot, left_arm_q)
# right leg
right_thigh_pos = motion.global_translation[..., right_thigh_id, :]
right_shin_pos = motion.global_translation[..., right_shin_id, :]
right_foot_pos = motion.global_translation[..., right_foot_id, :]
right_hip_rot = motion.local_rotation[..., right_thigh_id, :]
right_knee_rot = motion.local_rotation[..., right_shin_id, :]
right_leg_delta0 = right_thigh_pos - right_shin_pos
right_leg_delta1 = right_foot_pos - right_shin_pos
right_leg_delta0 = right_leg_delta0 / torch.norm(right_leg_delta0, dim=-1, keepdim=True)
right_leg_delta1 = right_leg_delta1 / torch.norm(right_leg_delta1, dim=-1, keepdim=True)
right_knee_dot = torch.sum(-right_leg_delta0 * right_leg_delta1, dim=-1)
right_knee_dot = torch.clamp(right_knee_dot, -1.0, 1.0)
right_knee_theta = torch.acos(right_knee_dot)
right_knee_q = quat_from_angle_axis(torch.abs(right_knee_theta), torch.tensor(np.array([[0.0, 1.0, 0.0]]),
device=device, dtype=torch.float32))
right_knee_local_dir = motion.skeleton_tree.local_translation[right_foot_id]
right_knee_local_dir = right_knee_local_dir / torch.norm(right_knee_local_dir)
right_knee_local_dir_tile = torch.tile(right_knee_local_dir.unsqueeze(0), [right_knee_rot.shape[0], 1])
right_knee_local_dir0 = quat_rotate(right_knee_rot, right_knee_local_dir_tile)
right_knee_local_dir1 = quat_rotate(right_knee_q, right_knee_local_dir_tile)
right_leg_dot = torch.sum(right_knee_local_dir0 * right_knee_local_dir1, dim=-1)
right_leg_dot = torch.clamp(right_leg_dot, -1.0, 1.0)
right_leg_theta = torch.acos(right_leg_dot)
right_leg_theta = torch.where(right_knee_local_dir0[..., 1] >= 0, right_leg_theta, -right_leg_theta)
right_leg_q = quat_from_angle_axis(right_leg_theta, right_knee_local_dir.unsqueeze(0))
right_hip_rot = quat_mul(right_hip_rot, right_leg_q)
# left leg
left_thigh_pos = motion.global_translation[..., left_thigh_id, :]
left_shin_pos = motion.global_translation[..., left_shin_id, :]
left_foot_pos = motion.global_translation[..., left_foot_id, :]
left_hip_rot = motion.local_rotation[..., left_thigh_id, :]
left_knee_rot = motion.local_rotation[..., left_shin_id, :]
left_leg_delta0 = left_thigh_pos - left_shin_pos
left_leg_delta1 = left_foot_pos - left_shin_pos
left_leg_delta0 = left_leg_delta0 / torch.norm(left_leg_delta0, dim=-1, keepdim=True)
left_leg_delta1 = left_leg_delta1 / torch.norm(left_leg_delta1, dim=-1, keepdim=True)
left_knee_dot = torch.sum(-left_leg_delta0 * left_leg_delta1, dim=-1)
left_knee_dot = torch.clamp(left_knee_dot, -1.0, 1.0)
left_knee_theta = torch.acos(left_knee_dot)
left_knee_q = quat_from_angle_axis(torch.abs(left_knee_theta), torch.tensor(np.array([[0.0, 1.0, 0.0]]),
device=device, dtype=torch.float32))
left_knee_local_dir = motion.skeleton_tree.local_translation[left_foot_id]
left_knee_local_dir = left_knee_local_dir / torch.norm(left_knee_local_dir)
left_knee_local_dir_tile = torch.tile(left_knee_local_dir.unsqueeze(0), [left_knee_rot.shape[0], 1])
left_knee_local_dir0 = quat_rotate(left_knee_rot, left_knee_local_dir_tile)
left_knee_local_dir1 = quat_rotate(left_knee_q, left_knee_local_dir_tile)
left_leg_dot = torch.sum(left_knee_local_dir0 * left_knee_local_dir1, dim=-1)
left_leg_dot = torch.clamp(left_leg_dot, -1.0, 1.0)
left_leg_theta = torch.acos(left_leg_dot)
left_leg_theta = torch.where(left_knee_local_dir0[..., 1] >= 0, left_leg_theta, -left_leg_theta)
left_leg_q = quat_from_angle_axis(left_leg_theta, left_knee_local_dir.unsqueeze(0))
left_hip_rot = quat_mul(left_hip_rot, left_leg_q)
new_local_rotation = motion.local_rotation.clone()
new_local_rotation[..., right_upper_arm_id, :] = right_shoulder_rot
new_local_rotation[..., right_lower_arm_id, :] = right_elbow_q
new_local_rotation[..., left_upper_arm_id, :] = left_shoulder_rot
new_local_rotation[..., left_lower_arm_id, :] = left_elbow_q
new_local_rotation[..., right_thigh_id, :] = right_hip_rot
new_local_rotation[..., right_shin_id, :] = right_knee_q
new_local_rotation[..., left_thigh_id, :] = left_hip_rot
new_local_rotation[..., left_shin_id, :] = left_knee_q
new_local_rotation[..., left_hand_id, :] = quat_identity([1])
new_local_rotation[..., right_hand_id, :] = quat_identity([1])
new_sk_state = SkeletonState.from_rotation_and_root_translation(motion.skeleton_tree, new_local_rotation, motion.root_translation, is_local=True)
new_motion = SkeletonMotion.from_skeleton_state(new_sk_state, fps=motion.fps)
return new_motion
def main():
# load retarget config
retarget_data_path = "data/configs/retarget_cmu_to_amp.json"
with open(retarget_data_path) as f:
retarget_data = json.load(f)
# load and visualize t-pose files
source_tpose = SkeletonState.from_file(retarget_data["source_tpose"])
if VISUALIZE:
plot_skeleton_state(source_tpose)
target_tpose = SkeletonState.from_file(retarget_data["target_tpose"])
if VISUALIZE:
plot_skeleton_state(target_tpose)
# load and visualize source motion sequence
source_motion = SkeletonMotion.from_file(retarget_data["source_motion"])
if VISUALIZE:
plot_skeleton_motion_interactive(source_motion)
# parse data from retarget config
joint_mapping = retarget_data["joint_mapping"]
rotation_to_target_skeleton = torch.tensor(retarget_data["rotation"])
# run retargeting
target_motion = source_motion.retarget_to_by_tpose(
joint_mapping=retarget_data["joint_mapping"],
source_tpose=source_tpose,
target_tpose=target_tpose,
rotation_to_target_skeleton=rotation_to_target_skeleton,
scale_to_target_skeleton=retarget_data["scale"]
)
# keep frames between [trim_frame_beg, trim_frame_end - 1]
frame_beg = retarget_data["trim_frame_beg"]
frame_end = retarget_data["trim_frame_end"]
if (frame_beg == -1):
frame_beg = 0
if (frame_end == -1):
frame_end = target_motion.local_rotation.shape[0]
local_rotation = target_motion.local_rotation
root_translation = target_motion.root_translation
local_rotation = local_rotation[frame_beg:frame_end, ...]
root_translation = root_translation[frame_beg:frame_end, ...]
new_sk_state = SkeletonState.from_rotation_and_root_translation(target_motion.skeleton_tree, local_rotation, root_translation, is_local=True)
target_motion = SkeletonMotion.from_skeleton_state(new_sk_state, fps=target_motion.fps)
# need to convert some joints from 3D to 1D (e.g. elbows and knees)
target_motion = project_joints(target_motion)
# move the root so that the feet are on the ground
local_rotation = target_motion.local_rotation
root_translation = target_motion.root_translation
tar_global_pos = target_motion.global_translation
min_h = torch.min(tar_global_pos[..., 2])
root_translation[:, 2] += -min_h
# adjust the height of the root to avoid ground penetration
root_height_offset = retarget_data["root_height_offset"]
root_translation[:, 2] += root_height_offset
new_sk_state = SkeletonState.from_rotation_and_root_translation(target_motion.skeleton_tree, local_rotation, root_translation, is_local=True)
target_motion = SkeletonMotion.from_skeleton_state(new_sk_state, fps=target_motion.fps)
# save retargeted motion
target_motion.to_file(retarget_data["target_motion_path"])
# visualize retargeted motion
plot_skeleton_motion_interactive(target_motion)
return
if __name__ == '__main__':
main() | 15,589 | Python | 54.283688 | 162 | 0.696196 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/__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.
__version__ = "0.0.1"
from .core import *
| 1,603 | Python | 47.606059 | 80 | 0.777293 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/visualization/common.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 ..core import logger
from .plt_plotter import Matplotlib3DPlotter
from .skeleton_plotter_tasks import Draw3DSkeletonMotion, Draw3DSkeletonState
def plot_skeleton_state(skeleton_state, task_name=""):
"""
Visualize a skeleton state
:param skeleton_state:
:param task_name:
:type skeleton_state: SkeletonState
:type task_name: string, optional
"""
logger.info("plotting {}".format(task_name))
task = Draw3DSkeletonState(task_name=task_name, skeleton_state=skeleton_state)
plotter = Matplotlib3DPlotter(task)
plotter.show()
def plot_skeleton_states(skeleton_state, skip_n=1, task_name=""):
"""
Visualize a sequence of skeleton state. The dimension of the skeleton state must be 1
:param skeleton_state:
:param task_name:
:type skeleton_state: SkeletonState
:type task_name: string, optional
"""
logger.info("plotting {} motion".format(task_name))
assert len(skeleton_state.shape) == 1, "the state must have only one dimension"
task = Draw3DSkeletonState(task_name=task_name, skeleton_state=skeleton_state[0])
plotter = Matplotlib3DPlotter(task)
for frame_id in range(skeleton_state.shape[0]):
if frame_id % skip_n != 0:
continue
task.update(skeleton_state[frame_id])
plotter.update()
plotter.show()
def plot_skeleton_motion(skeleton_motion, skip_n=1, task_name=""):
"""
Visualize a skeleton motion along its first dimension.
:param skeleton_motion:
:param task_name:
:type skeleton_motion: SkeletonMotion
:type task_name: string, optional
"""
logger.info("plotting {} motion".format(task_name))
task = Draw3DSkeletonMotion(
task_name=task_name, skeleton_motion=skeleton_motion, frame_index=0
)
plotter = Matplotlib3DPlotter(task)
for frame_id in range(len(skeleton_motion)):
if frame_id % skip_n != 0:
continue
task.update(frame_id)
plotter.update()
plotter.show()
def plot_skeleton_motion_interactive_base(skeleton_motion, task_name=""):
class PlotParams:
def __init__(self, total_num_frames):
self.current_frame = 0
self.playing = False
self.looping = False
self.confirmed = False
self.playback_speed = 4
self.total_num_frames = total_num_frames
def sync(self, other):
self.current_frame = other.current_frame
self.playing = other.playing
self.looping = other.current_frame
self.confirmed = other.confirmed
self.playback_speed = other.playback_speed
self.total_num_frames = other.total_num_frames
task = Draw3DSkeletonMotion(
task_name=task_name, skeleton_motion=skeleton_motion, frame_index=0
)
plotter = Matplotlib3DPlotter(task)
plot_params = PlotParams(total_num_frames=len(skeleton_motion))
print("Entered interactive plot - press 'n' to quit, 'h' for a list of commands")
def press(event):
if event.key == "x":
plot_params.playing = not plot_params.playing
elif event.key == "z":
plot_params.current_frame = plot_params.current_frame - 1
elif event.key == "c":
plot_params.current_frame = plot_params.current_frame + 1
elif event.key == "a":
plot_params.current_frame = plot_params.current_frame - 20
elif event.key == "d":
plot_params.current_frame = plot_params.current_frame + 20
elif event.key == "w":
plot_params.looping = not plot_params.looping
print("Looping: {}".format(plot_params.looping))
elif event.key == "v":
plot_params.playback_speed *= 2
print("playback speed: {}".format(plot_params.playback_speed))
elif event.key == "b":
if plot_params.playback_speed != 1:
plot_params.playback_speed //= 2
print("playback speed: {}".format(plot_params.playback_speed))
elif event.key == "n":
plot_params.confirmed = True
elif event.key == "h":
rows, columns = os.popen("stty size", "r").read().split()
columns = int(columns)
print("=" * columns)
print("x: play/pause")
print("z: previous frame")
print("c: next frame")
print("a: jump 10 frames back")
print("d: jump 10 frames forward")
print("w: looping/non-looping")
print("v: double speed (this can be applied multiple times)")
print("b: half speed (this can be applied multiple times)")
print("n: quit")
print("h: help")
print("=" * columns)
print(
'current frame index: {}/{} (press "n" to quit)'.format(
plot_params.current_frame, plot_params.total_num_frames - 1
)
)
plotter.fig.canvas.mpl_connect("key_press_event", press)
while True:
reset_trail = False
if plot_params.confirmed:
break
if plot_params.playing:
plot_params.current_frame += plot_params.playback_speed
if plot_params.current_frame >= plot_params.total_num_frames:
if plot_params.looping:
plot_params.current_frame %= plot_params.total_num_frames
reset_trail = True
else:
plot_params.current_frame = plot_params.total_num_frames - 1
if plot_params.current_frame < 0:
if plot_params.looping:
plot_params.current_frame %= plot_params.total_num_frames
reset_trail = True
else:
plot_params.current_frame = 0
yield plot_params
task.update(plot_params.current_frame, reset_trail)
plotter.update()
def plot_skeleton_motion_interactive(skeleton_motion, task_name=""):
"""
Visualize a skeleton motion along its first dimension interactively.
:param skeleton_motion:
:param task_name:
:type skeleton_motion: SkeletonMotion
:type task_name: string, optional
"""
for _ in plot_skeleton_motion_interactive_base(skeleton_motion, task_name):
pass
def plot_skeleton_motion_interactive_multiple(*callables, sync=True):
for _ in zip(*callables):
if sync:
for p1, p2 in zip(_[:-1], _[1:]):
p2.sync(p1)
# def plot_skeleton_motion_interactive_multiple_same(skeleton_motions, task_name=""):
| 8,107 | Python | 37.42654 | 89 | 0.642654 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/visualization/simple_plotter_tasks.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.
"""
This is where all the task primitives are defined
"""
import numpy as np
from .core import BasePlotterTask
class DrawXDLines(BasePlotterTask):
_lines: np.ndarray
_color: str
_line_width: int
_alpha: float
_influence_lim: bool
def __init__(
self,
task_name: str,
lines: np.ndarray,
color: str = "blue",
line_width: int = 2,
alpha: float = 1.0,
influence_lim: bool = True,
) -> None:
super().__init__(task_name=task_name, task_type=self.__class__.__name__)
self._color = color
self._line_width = line_width
self._alpha = alpha
self._influence_lim = influence_lim
self.update(lines)
@property
def influence_lim(self) -> bool:
return self._influence_lim
@property
def raw_data(self):
return self._lines
@property
def color(self):
return self._color
@property
def line_width(self):
return self._line_width
@property
def alpha(self):
return self._alpha
@property
def dim(self):
raise NotImplementedError
@property
def name(self):
return "{}DLines".format(self.dim)
def update(self, lines):
self._lines = np.array(lines)
shape = self._lines.shape
assert shape[-1] == self.dim and shape[-2] == 2 and len(shape) == 3
def __getitem__(self, index):
return self._lines[index]
def __len__(self):
return self._lines.shape[0]
def __iter__(self):
yield self
class DrawXDDots(BasePlotterTask):
_dots: np.ndarray
_color: str
_marker_size: int
_alpha: float
_influence_lim: bool
def __init__(
self,
task_name: str,
dots: np.ndarray,
color: str = "blue",
marker_size: int = 10,
alpha: float = 1.0,
influence_lim: bool = True,
) -> None:
super().__init__(task_name=task_name, task_type=self.__class__.__name__)
self._color = color
self._marker_size = marker_size
self._alpha = alpha
self._influence_lim = influence_lim
self.update(dots)
def update(self, dots):
self._dots = np.array(dots)
shape = self._dots.shape
assert shape[-1] == self.dim and len(shape) == 2
def __getitem__(self, index):
return self._dots[index]
def __len__(self):
return self._dots.shape[0]
def __iter__(self):
yield self
@property
def influence_lim(self) -> bool:
return self._influence_lim
@property
def raw_data(self):
return self._dots
@property
def color(self):
return self._color
@property
def marker_size(self):
return self._marker_size
@property
def alpha(self):
return self._alpha
@property
def dim(self):
raise NotImplementedError
@property
def name(self):
return "{}DDots".format(self.dim)
class DrawXDTrail(DrawXDDots):
@property
def line_width(self):
return self.marker_size
@property
def name(self):
return "{}DTrail".format(self.dim)
class Draw2DLines(DrawXDLines):
@property
def dim(self):
return 2
class Draw3DLines(DrawXDLines):
@property
def dim(self):
return 3
class Draw2DDots(DrawXDDots):
@property
def dim(self):
return 2
class Draw3DDots(DrawXDDots):
@property
def dim(self):
return 3
class Draw2DTrail(DrawXDTrail):
@property
def dim(self):
return 2
class Draw3DTrail(DrawXDTrail):
@property
def dim(self):
return 3
| 5,246 | Python | 23.404651 | 80 | 0.633626 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/visualization/__init__.py | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited. | 423 | Python | 69.666655 | 76 | 0.817967 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/visualization/core.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.
"""
The base abstract classes for plotter and the plotting tasks. It describes how the plotter
deals with the tasks in the general cases
"""
from typing import List
class BasePlotterTask(object):
_task_name: str # unique name of the task
_task_type: str # type of the task is used to identify which callable
def __init__(self, task_name: str, task_type: str) -> None:
self._task_name = task_name
self._task_type = task_type
@property
def task_name(self):
return self._task_name
@property
def task_type(self):
return self._task_type
def get_scoped_name(self, name):
return self._task_name + "/" + name
def __iter__(self):
"""Should override this function to return a list of task primitives
"""
raise NotImplementedError
class BasePlotterTasks(object):
def __init__(self, tasks) -> None:
self._tasks = tasks
def __iter__(self):
for task in self._tasks:
yield from task
class BasePlotter(object):
"""An abstract plotter which deals with a plotting task. The children class needs to implement
the functions to create/update the objects according to the task given
"""
_task_primitives: List[BasePlotterTask]
def __init__(self, task: BasePlotterTask) -> None:
self._task_primitives = []
self.create(task)
@property
def task_primitives(self):
return self._task_primitives
def create(self, task: BasePlotterTask) -> None:
"""Create more task primitives from a task for the plotter"""
new_task_primitives = list(task) # get all task primitives
self._task_primitives += new_task_primitives # append them
self._create_impl(new_task_primitives)
def update(self) -> None:
"""Update the plotter for any updates in the task primitives"""
self._update_impl(self._task_primitives)
def _update_impl(self, task_list: List[BasePlotterTask]) -> None:
raise NotImplementedError
def _create_impl(self, task_list: List[BasePlotterTask]) -> None:
raise NotImplementedError
| 3,700 | Python | 36.01 | 98 | 0.705676 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/visualization/plt_plotter.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.
"""
The matplotlib plotter implementation for all the primitive tasks (in our case: lines and
dots)
"""
from typing import Any, Callable, Dict, List
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
import numpy as np
from .core import BasePlotter, BasePlotterTask
class Matplotlib2DPlotter(BasePlotter):
_fig: plt.figure # plt figure
_ax: plt.axis # plt axis
# stores artist objects for each task (task name as the key)
_artist_cache: Dict[str, Any]
# callables for each task primitives
_create_impl_callables: Dict[str, Callable]
_update_impl_callables: Dict[str, Callable]
def __init__(self, task: "BasePlotterTask") -> None:
fig, ax = plt.subplots()
self._fig = fig
self._ax = ax
self._artist_cache = {}
self._create_impl_callables = {
"Draw2DLines": self._lines_create_impl,
"Draw2DDots": self._dots_create_impl,
"Draw2DTrail": self._trail_create_impl,
}
self._update_impl_callables = {
"Draw2DLines": self._lines_update_impl,
"Draw2DDots": self._dots_update_impl,
"Draw2DTrail": self._trail_update_impl,
}
self._init_lim()
super().__init__(task)
@property
def ax(self):
return self._ax
@property
def fig(self):
return self._fig
def show(self):
plt.show()
def _min(self, x, y):
if x is None:
return y
if y is None:
return x
return min(x, y)
def _max(self, x, y):
if x is None:
return y
if y is None:
return x
return max(x, y)
def _init_lim(self):
self._curr_x_min = None
self._curr_y_min = None
self._curr_x_max = None
self._curr_y_max = None
def _update_lim(self, xs, ys):
self._curr_x_min = self._min(np.min(xs), self._curr_x_min)
self._curr_y_min = self._min(np.min(ys), self._curr_y_min)
self._curr_x_max = self._max(np.max(xs), self._curr_x_max)
self._curr_y_max = self._max(np.max(ys), self._curr_y_max)
def _set_lim(self):
if not (
self._curr_x_min is None
or self._curr_x_max is None
or self._curr_y_min is None
or self._curr_y_max is None
):
self._ax.set_xlim(self._curr_x_min, self._curr_x_max)
self._ax.set_ylim(self._curr_y_min, self._curr_y_max)
self._init_lim()
@staticmethod
def _lines_extract_xy_impl(index, lines_task):
return lines_task[index, :, 0], lines_task[index, :, 1]
@staticmethod
def _trail_extract_xy_impl(index, trail_task):
return (trail_task[index : index + 2, 0], trail_task[index : index + 2, 1])
def _lines_create_impl(self, lines_task):
color = lines_task.color
self._artist_cache[lines_task.task_name] = [
self._ax.plot(
*Matplotlib2DPlotter._lines_extract_xy_impl(i, lines_task),
color=color,
linewidth=lines_task.line_width,
alpha=lines_task.alpha
)[0]
for i in range(len(lines_task))
]
def _lines_update_impl(self, lines_task):
lines_artists = self._artist_cache[lines_task.task_name]
for i in range(len(lines_task)):
artist = lines_artists[i]
xs, ys = Matplotlib2DPlotter._lines_extract_xy_impl(i, lines_task)
artist.set_data(xs, ys)
if lines_task.influence_lim:
self._update_lim(xs, ys)
def _dots_create_impl(self, dots_task):
color = dots_task.color
self._artist_cache[dots_task.task_name] = self._ax.plot(
dots_task[:, 0],
dots_task[:, 1],
c=color,
linestyle="",
marker=".",
markersize=dots_task.marker_size,
alpha=dots_task.alpha,
)[0]
def _dots_update_impl(self, dots_task):
dots_artist = self._artist_cache[dots_task.task_name]
dots_artist.set_data(dots_task[:, 0], dots_task[:, 1])
if dots_task.influence_lim:
self._update_lim(dots_task[:, 0], dots_task[:, 1])
def _trail_create_impl(self, trail_task):
color = trail_task.color
trail_length = len(trail_task) - 1
self._artist_cache[trail_task.task_name] = [
self._ax.plot(
*Matplotlib2DPlotter._trail_extract_xy_impl(i, trail_task),
color=trail_task.color,
linewidth=trail_task.line_width,
alpha=trail_task.alpha * (1.0 - i / (trail_length - 1))
)[0]
for i in range(trail_length)
]
def _trail_update_impl(self, trail_task):
trails_artists = self._artist_cache[trail_task.task_name]
for i in range(len(trail_task) - 1):
artist = trails_artists[i]
xs, ys = Matplotlib2DPlotter._trail_extract_xy_impl(i, trail_task)
artist.set_data(xs, ys)
if trail_task.influence_lim:
self._update_lim(xs, ys)
def _create_impl(self, task_list):
for task in task_list:
self._create_impl_callables[task.task_type](task)
self._draw()
def _update_impl(self, task_list):
for task in task_list:
self._update_impl_callables[task.task_type](task)
self._draw()
def _set_aspect_equal_2d(self, zero_centered=True):
xlim = self._ax.get_xlim()
ylim = self._ax.get_ylim()
if not zero_centered:
xmean = np.mean(xlim)
ymean = np.mean(ylim)
else:
xmean = 0
ymean = 0
plot_radius = max(
[
abs(lim - mean_)
for lims, mean_ in ((xlim, xmean), (ylim, ymean))
for lim in lims
]
)
self._ax.set_xlim([xmean - plot_radius, xmean + plot_radius])
self._ax.set_ylim([ymean - plot_radius, ymean + plot_radius])
def _draw(self):
self._set_lim()
self._set_aspect_equal_2d()
self._fig.canvas.draw()
self._fig.canvas.flush_events()
plt.pause(0.00001)
class Matplotlib3DPlotter(BasePlotter):
_fig: plt.figure # plt figure
_ax: p3.Axes3D # plt 3d axis
# stores artist objects for each task (task name as the key)
_artist_cache: Dict[str, Any]
# callables for each task primitives
_create_impl_callables: Dict[str, Callable]
_update_impl_callables: Dict[str, Callable]
def __init__(self, task: "BasePlotterTask") -> None:
self._fig = plt.figure()
self._ax = p3.Axes3D(self._fig)
self._artist_cache = {}
self._create_impl_callables = {
"Draw3DLines": self._lines_create_impl,
"Draw3DDots": self._dots_create_impl,
"Draw3DTrail": self._trail_create_impl,
}
self._update_impl_callables = {
"Draw3DLines": self._lines_update_impl,
"Draw3DDots": self._dots_update_impl,
"Draw3DTrail": self._trail_update_impl,
}
self._init_lim()
super().__init__(task)
@property
def ax(self):
return self._ax
@property
def fig(self):
return self._fig
def show(self):
plt.show()
def _min(self, x, y):
if x is None:
return y
if y is None:
return x
return min(x, y)
def _max(self, x, y):
if x is None:
return y
if y is None:
return x
return max(x, y)
def _init_lim(self):
self._curr_x_min = None
self._curr_y_min = None
self._curr_z_min = None
self._curr_x_max = None
self._curr_y_max = None
self._curr_z_max = None
def _update_lim(self, xs, ys, zs):
self._curr_x_min = self._min(np.min(xs), self._curr_x_min)
self._curr_y_min = self._min(np.min(ys), self._curr_y_min)
self._curr_z_min = self._min(np.min(zs), self._curr_z_min)
self._curr_x_max = self._max(np.max(xs), self._curr_x_max)
self._curr_y_max = self._max(np.max(ys), self._curr_y_max)
self._curr_z_max = self._max(np.max(zs), self._curr_z_max)
def _set_lim(self):
if not (
self._curr_x_min is None
or self._curr_x_max is None
or self._curr_y_min is None
or self._curr_y_max is None
or self._curr_z_min is None
or self._curr_z_max is None
):
self._ax.set_xlim3d(self._curr_x_min, self._curr_x_max)
self._ax.set_ylim3d(self._curr_y_min, self._curr_y_max)
self._ax.set_zlim3d(self._curr_z_min, self._curr_z_max)
self._init_lim()
@staticmethod
def _lines_extract_xyz_impl(index, lines_task):
return lines_task[index, :, 0], lines_task[index, :, 1], lines_task[index, :, 2]
@staticmethod
def _trail_extract_xyz_impl(index, trail_task):
return (
trail_task[index : index + 2, 0],
trail_task[index : index + 2, 1],
trail_task[index : index + 2, 2],
)
def _lines_create_impl(self, lines_task):
color = lines_task.color
self._artist_cache[lines_task.task_name] = [
self._ax.plot(
*Matplotlib3DPlotter._lines_extract_xyz_impl(i, lines_task),
color=color,
linewidth=lines_task.line_width,
alpha=lines_task.alpha
)[0]
for i in range(len(lines_task))
]
def _lines_update_impl(self, lines_task):
lines_artists = self._artist_cache[lines_task.task_name]
for i in range(len(lines_task)):
artist = lines_artists[i]
xs, ys, zs = Matplotlib3DPlotter._lines_extract_xyz_impl(i, lines_task)
artist.set_data(xs, ys)
artist.set_3d_properties(zs)
if lines_task.influence_lim:
self._update_lim(xs, ys, zs)
def _dots_create_impl(self, dots_task):
color = dots_task.color
self._artist_cache[dots_task.task_name] = self._ax.plot(
dots_task[:, 0],
dots_task[:, 1],
dots_task[:, 2],
c=color,
linestyle="",
marker=".",
markersize=dots_task.marker_size,
alpha=dots_task.alpha,
)[0]
def _dots_update_impl(self, dots_task):
dots_artist = self._artist_cache[dots_task.task_name]
dots_artist.set_data(dots_task[:, 0], dots_task[:, 1])
dots_artist.set_3d_properties(dots_task[:, 2])
if dots_task.influence_lim:
self._update_lim(dots_task[:, 0], dots_task[:, 1], dots_task[:, 2])
def _trail_create_impl(self, trail_task):
color = trail_task.color
trail_length = len(trail_task) - 1
self._artist_cache[trail_task.task_name] = [
self._ax.plot(
*Matplotlib3DPlotter._trail_extract_xyz_impl(i, trail_task),
color=trail_task.color,
linewidth=trail_task.line_width,
alpha=trail_task.alpha * (1.0 - i / (trail_length - 1))
)[0]
for i in range(trail_length)
]
def _trail_update_impl(self, trail_task):
trails_artists = self._artist_cache[trail_task.task_name]
for i in range(len(trail_task) - 1):
artist = trails_artists[i]
xs, ys, zs = Matplotlib3DPlotter._trail_extract_xyz_impl(i, trail_task)
artist.set_data(xs, ys)
artist.set_3d_properties(zs)
if trail_task.influence_lim:
self._update_lim(xs, ys, zs)
def _create_impl(self, task_list):
for task in task_list:
self._create_impl_callables[task.task_type](task)
self._draw()
def _update_impl(self, task_list):
for task in task_list:
self._update_impl_callables[task.task_type](task)
self._draw()
def _set_aspect_equal_3d(self):
xlim = self._ax.get_xlim3d()
ylim = self._ax.get_ylim3d()
zlim = self._ax.get_zlim3d()
xmean = np.mean(xlim)
ymean = np.mean(ylim)
zmean = np.mean(zlim)
plot_radius = max(
[
abs(lim - mean_)
for lims, mean_ in ((xlim, xmean), (ylim, ymean), (zlim, zmean))
for lim in lims
]
)
self._ax.set_xlim3d([xmean - plot_radius, xmean + plot_radius])
self._ax.set_ylim3d([ymean - plot_radius, ymean + plot_radius])
self._ax.set_zlim3d([zmean - plot_radius, zmean + plot_radius])
def _draw(self):
self._set_lim()
self._set_aspect_equal_3d()
self._fig.canvas.draw()
self._fig.canvas.flush_events()
plt.pause(0.00001)
| 14,522 | Python | 33.171765 | 89 | 0.567071 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/visualization/skeleton_plotter_tasks.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.
"""
This is where all skeleton related complex tasks are defined (skeleton state and skeleton
motion)
"""
import numpy as np
from .core import BasePlotterTask
from .simple_plotter_tasks import Draw3DDots, Draw3DLines, Draw3DTrail
class Draw3DSkeletonState(BasePlotterTask):
_lines_task: Draw3DLines # sub-task for drawing lines
_dots_task: Draw3DDots # sub-task for drawing dots
def __init__(
self,
task_name: str,
skeleton_state,
joints_color: str = "red",
lines_color: str = "blue",
alpha=1.0,
) -> None:
super().__init__(task_name=task_name, task_type="3DSkeletonState")
lines, dots = Draw3DSkeletonState._get_lines_and_dots(skeleton_state)
self._lines_task = Draw3DLines(
self.get_scoped_name("bodies"), lines, joints_color, alpha=alpha
)
self._dots_task = Draw3DDots(
self.get_scoped_name("joints"), dots, lines_color, alpha=alpha
)
@property
def name(self):
return "3DSkeleton"
def update(self, skeleton_state) -> None:
self._update(*Draw3DSkeletonState._get_lines_and_dots(skeleton_state))
@staticmethod
def _get_lines_and_dots(skeleton_state):
"""Get all the lines and dots needed to draw the skeleton state
"""
assert (
len(skeleton_state.tensor.shape) == 1
), "the state has to be zero dimensional"
dots = skeleton_state.global_translation.numpy()
skeleton_tree = skeleton_state.skeleton_tree
parent_indices = skeleton_tree.parent_indices.numpy()
lines = []
for node_index in range(len(skeleton_tree)):
parent_index = parent_indices[node_index]
if parent_index != -1:
lines.append([dots[node_index], dots[parent_index]])
lines = np.array(lines)
return lines, dots
def _update(self, lines, dots) -> None:
self._lines_task.update(lines)
self._dots_task.update(dots)
def __iter__(self):
yield from self._lines_task
yield from self._dots_task
class Draw3DSkeletonMotion(BasePlotterTask):
def __init__(
self,
task_name: str,
skeleton_motion,
frame_index=None,
joints_color="red",
lines_color="blue",
velocity_color="green",
angular_velocity_color="purple",
trail_color="black",
trail_length=10,
alpha=1.0,
) -> None:
super().__init__(task_name=task_name, task_type="3DSkeletonMotion")
self._trail_length = trail_length
self._skeleton_motion = skeleton_motion
# if frame_index is None:
curr_skeleton_motion = self._skeleton_motion.clone()
if frame_index is not None:
curr_skeleton_motion.tensor = self._skeleton_motion.tensor[frame_index, :]
# else:
# curr_skeleton_motion = self._skeleton_motion[frame_index, :]
self._skeleton_state_task = Draw3DSkeletonState(
self.get_scoped_name("skeleton_state"),
curr_skeleton_motion,
joints_color=joints_color,
lines_color=lines_color,
alpha=alpha,
)
vel_lines, avel_lines = Draw3DSkeletonMotion._get_vel_and_avel(
curr_skeleton_motion
)
self._com_pos = curr_skeleton_motion.root_translation.numpy()[
np.newaxis, ...
].repeat(trail_length, axis=0)
self._vel_task = Draw3DLines(
self.get_scoped_name("velocity"),
vel_lines,
velocity_color,
influence_lim=False,
alpha=alpha,
)
self._avel_task = Draw3DLines(
self.get_scoped_name("angular_velocity"),
avel_lines,
angular_velocity_color,
influence_lim=False,
alpha=alpha,
)
self._com_trail_task = Draw3DTrail(
self.get_scoped_name("com_trail"),
self._com_pos,
trail_color,
marker_size=2,
influence_lim=True,
alpha=alpha,
)
@property
def name(self):
return "3DSkeletonMotion"
def update(self, frame_index=None, reset_trail=False, skeleton_motion=None) -> None:
if skeleton_motion is not None:
self._skeleton_motion = skeleton_motion
curr_skeleton_motion = self._skeleton_motion.clone()
if frame_index is not None:
curr_skeleton_motion.tensor = curr_skeleton_motion.tensor[frame_index, :]
if reset_trail:
self._com_pos = curr_skeleton_motion.root_translation.numpy()[
np.newaxis, ...
].repeat(self._trail_length, axis=0)
else:
self._com_pos = np.concatenate(
(
curr_skeleton_motion.root_translation.numpy()[np.newaxis, ...],
self._com_pos[:-1],
),
axis=0,
)
self._skeleton_state_task.update(curr_skeleton_motion)
self._com_trail_task.update(self._com_pos)
self._update(*Draw3DSkeletonMotion._get_vel_and_avel(curr_skeleton_motion))
@staticmethod
def _get_vel_and_avel(skeleton_motion):
"""Get all the velocity and angular velocity lines
"""
pos = skeleton_motion.global_translation.numpy()
vel = skeleton_motion.global_velocity.numpy()
avel = skeleton_motion.global_angular_velocity.numpy()
vel_lines = np.stack((pos, pos + vel * 0.02), axis=1)
avel_lines = np.stack((pos, pos + avel * 0.01), axis=1)
return vel_lines, avel_lines
def _update(self, vel_lines, avel_lines) -> None:
self._vel_task.update(vel_lines)
self._avel_task.update(avel_lines)
def __iter__(self):
yield from self._skeleton_state_task
yield from self._vel_task
yield from self._avel_task
yield from self._com_trail_task
class Draw3DSkeletonMotions(BasePlotterTask):
def __init__(self, skeleton_motion_tasks) -> None:
self._skeleton_motion_tasks = skeleton_motion_tasks
@property
def name(self):
return "3DSkeletonMotions"
def update(self, frame_index) -> None:
list(map(lambda x: x.update(frame_index), self._skeleton_motion_tasks))
def __iter__(self):
yield from self._skeleton_state_tasks
| 7,974 | Python | 35.751152 | 89 | 0.627163 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/visualization/tests/test_plotter.py | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from typing import cast
import matplotlib.pyplot as plt
import numpy as np
from ..core import BasePlotterTask, BasePlotterTasks
from ..plt_plotter import Matplotlib3DPlotter
from ..simple_plotter_tasks import Draw3DDots, Draw3DLines
task = Draw3DLines(task_name="test",
lines=np.array([[[0, 0, 0], [0, 0, 1]], [[0, 1, 1], [0, 1, 0]]]), color="blue")
task2 = Draw3DDots(task_name="test2",
dots=np.array([[0, 0, 0], [0, 0, 1], [0, 1, 1], [0, 1, 0]]), color="red")
task3 = BasePlotterTasks([task, task2])
plotter = Matplotlib3DPlotter(cast(BasePlotterTask, task3))
plt.show()
| 1,011 | Python | 41.166665 | 83 | 0.738872 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/visualization/tests/__init__.py | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited. | 423 | Python | 69.666655 | 76 | 0.817967 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/core/__init__.py | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from .tensor_utils import *
from .rotation3d import *
from .backend import Serializable, logger
| 521 | Python | 46.454541 | 76 | 0.809981 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/core/rotation3d.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, Optional
import math
import torch
@torch.jit.script
def quat_mul(a, b):
"""
quaternion multiplication
"""
x1, y1, z1, w1 = a[..., 0], a[..., 1], a[..., 2], a[..., 3]
x2, y2, z2, w2 = b[..., 0], b[..., 1], b[..., 2], b[..., 3]
w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2
x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2
y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2
z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2
return torch.stack([x, y, z, w], dim=-1)
@torch.jit.script
def quat_pos(x):
"""
make all the real part of the quaternion positive
"""
q = x
z = (q[..., 3:] < 0).float()
q = (1 - 2 * z) * q
return q
@torch.jit.script
def quat_abs(x):
"""
quaternion norm (unit quaternion represents a 3D rotation, which has norm of 1)
"""
x = x.norm(p=2, dim=-1)
return x
@torch.jit.script
def quat_unit(x):
"""
normalized quaternion with norm of 1
"""
norm = quat_abs(x).unsqueeze(-1)
return x / (norm.clamp(min=1e-9))
@torch.jit.script
def quat_conjugate(x):
"""
quaternion with its imaginary part negated
"""
return torch.cat([-x[..., :3], x[..., 3:]], dim=-1)
@torch.jit.script
def quat_real(x):
"""
real component of the quaternion
"""
return x[..., 3]
@torch.jit.script
def quat_imaginary(x):
"""
imaginary components of the quaternion
"""
return x[..., :3]
@torch.jit.script
def quat_norm_check(x):
"""
verify that a quaternion has norm 1
"""
assert bool(
(abs(x.norm(p=2, dim=-1) - 1) < 1e-3).all()
), "the quaternion is has non-1 norm: {}".format(abs(x.norm(p=2, dim=-1) - 1))
assert bool((x[..., 3] >= 0).all()), "the quaternion has negative real part"
@torch.jit.script
def quat_normalize(q):
"""
Construct 3D rotation from quaternion (the quaternion needs not to be normalized).
"""
q = quat_unit(quat_pos(q)) # normalized to positive and unit quaternion
return q
@torch.jit.script
def quat_from_xyz(xyz):
"""
Construct 3D rotation from the imaginary component
"""
w = (1.0 - xyz.norm()).unsqueeze(-1)
assert bool((w >= 0).all()), "xyz has its norm greater than 1"
return torch.cat([xyz, w], dim=-1)
@torch.jit.script
def quat_identity(shape: List[int]):
"""
Construct 3D identity rotation given shape
"""
w = torch.ones(shape + [1])
xyz = torch.zeros(shape + [3])
q = torch.cat([xyz, w], dim=-1)
return quat_normalize(q)
@torch.jit.script
def quat_from_angle_axis(angle, axis, degree: bool = False):
""" Create a 3D rotation from angle and axis of rotation. The rotation is counter-clockwise
along the axis.
The rotation can be interpreted as a_R_b where frame "b" is the new frame that
gets rotated counter-clockwise along the axis from frame "a"
:param angle: angle of rotation
:type angle: Tensor
:param axis: axis of rotation
:type axis: Tensor
:param degree: put True here if the angle is given by degree
:type degree: bool, optional, default=False
"""
if degree:
angle = angle / 180.0 * math.pi
theta = (angle / 2).unsqueeze(-1)
axis = axis / (axis.norm(p=2, dim=-1, keepdim=True).clamp(min=1e-9))
xyz = axis * theta.sin()
w = theta.cos()
return quat_normalize(torch.cat([xyz, w], dim=-1))
@torch.jit.script
def quat_from_rotation_matrix(m):
"""
Construct a 3D rotation from a valid 3x3 rotation matrices.
Reference can be found here:
http://www.cg.info.hiroshima-cu.ac.jp/~miyazaki/knowledge/teche52.html
:param m: 3x3 orthogonal rotation matrices.
:type m: Tensor
:rtype: Tensor
"""
m = m.unsqueeze(0)
diag0 = m[..., 0, 0]
diag1 = m[..., 1, 1]
diag2 = m[..., 2, 2]
# Math stuff.
w = (((diag0 + diag1 + diag2 + 1.0) / 4.0).clamp(0.0, None)) ** 0.5
x = (((diag0 - diag1 - diag2 + 1.0) / 4.0).clamp(0.0, None)) ** 0.5
y = (((-diag0 + diag1 - diag2 + 1.0) / 4.0).clamp(0.0, None)) ** 0.5
z = (((-diag0 - diag1 + diag2 + 1.0) / 4.0).clamp(0.0, None)) ** 0.5
# Only modify quaternions where w > x, y, z.
c0 = (w >= x) & (w >= y) & (w >= z)
x[c0] *= (m[..., 2, 1][c0] - m[..., 1, 2][c0]).sign()
y[c0] *= (m[..., 0, 2][c0] - m[..., 2, 0][c0]).sign()
z[c0] *= (m[..., 1, 0][c0] - m[..., 0, 1][c0]).sign()
# Only modify quaternions where x > w, y, z
c1 = (x >= w) & (x >= y) & (x >= z)
w[c1] *= (m[..., 2, 1][c1] - m[..., 1, 2][c1]).sign()
y[c1] *= (m[..., 1, 0][c1] + m[..., 0, 1][c1]).sign()
z[c1] *= (m[..., 0, 2][c1] + m[..., 2, 0][c1]).sign()
# Only modify quaternions where y > w, x, z.
c2 = (y >= w) & (y >= x) & (y >= z)
w[c2] *= (m[..., 0, 2][c2] - m[..., 2, 0][c2]).sign()
x[c2] *= (m[..., 1, 0][c2] + m[..., 0, 1][c2]).sign()
z[c2] *= (m[..., 2, 1][c2] + m[..., 1, 2][c2]).sign()
# Only modify quaternions where z > w, x, y.
c3 = (z >= w) & (z >= x) & (z >= y)
w[c3] *= (m[..., 1, 0][c3] - m[..., 0, 1][c3]).sign()
x[c3] *= (m[..., 2, 0][c3] + m[..., 0, 2][c3]).sign()
y[c3] *= (m[..., 2, 1][c3] + m[..., 1, 2][c3]).sign()
return quat_normalize(torch.stack([x, y, z, w], dim=-1)).squeeze(0)
@torch.jit.script
def quat_mul_norm(x, y):
"""
Combine two set of 3D rotations together using \**\* operator. The shape needs to be
broadcastable
"""
return quat_normalize(quat_mul(x, y))
@torch.jit.script
def quat_rotate(rot, vec):
"""
Rotate a 3D vector with the 3D rotation
"""
other_q = torch.cat([vec, torch.zeros_like(vec[..., :1])], dim=-1)
return quat_imaginary(quat_mul(quat_mul(rot, other_q), quat_conjugate(rot)))
@torch.jit.script
def quat_inverse(x):
"""
The inverse of the rotation
"""
return quat_conjugate(x)
@torch.jit.script
def quat_identity_like(x):
"""
Construct identity 3D rotation with the same shape
"""
return quat_identity(x.shape[:-1])
@torch.jit.script
def quat_angle_axis(x):
"""
The (angle, axis) representation of the rotation. The axis is normalized to unit length.
The angle is guaranteed to be between [0, pi].
"""
s = 2 * (x[..., 3] ** 2) - 1
angle = s.clamp(-1, 1).arccos() # just to be safe
axis = x[..., :3]
axis /= axis.norm(p=2, dim=-1, keepdim=True).clamp(min=1e-9)
return angle, axis
@torch.jit.script
def quat_yaw_rotation(x, z_up: bool = True):
"""
Yaw rotation (rotation along z-axis)
"""
q = x
if z_up:
q = torch.cat([torch.zeros_like(q[..., 0:2]), q[..., 2:3], q[..., 3:]], dim=-1)
else:
q = torch.cat(
[
torch.zeros_like(q[..., 0:1]),
q[..., 1:2],
torch.zeros_like(q[..., 2:3]),
q[..., 3:4],
],
dim=-1,
)
return quat_normalize(q)
@torch.jit.script
def transform_from_rotation_translation(
r: Optional[torch.Tensor] = None, t: Optional[torch.Tensor] = None
):
"""
Construct a transform from a quaternion and 3D translation. Only one of them can be None.
"""
assert r is not None or t is not None, "rotation and translation can't be all None"
if r is None:
assert t is not None
r = quat_identity(list(t.shape))
if t is None:
t = torch.zeros(list(r.shape) + [3])
return torch.cat([r, t], dim=-1)
@torch.jit.script
def transform_identity(shape: List[int]):
"""
Identity transformation with given shape
"""
r = quat_identity(shape)
t = torch.zeros(shape + [3])
return transform_from_rotation_translation(r, t)
@torch.jit.script
def transform_rotation(x):
"""Get rotation from transform"""
return x[..., :4]
@torch.jit.script
def transform_translation(x):
"""Get translation from transform"""
return x[..., 4:]
@torch.jit.script
def transform_inverse(x):
"""
Inverse transformation
"""
inv_so3 = quat_inverse(transform_rotation(x))
return transform_from_rotation_translation(
r=inv_so3, t=quat_rotate(inv_so3, -transform_translation(x))
)
@torch.jit.script
def transform_identity_like(x):
"""
identity transformation with the same shape
"""
return transform_identity(x.shape)
@torch.jit.script
def transform_mul(x, y):
"""
Combine two transformation together
"""
z = transform_from_rotation_translation(
r=quat_mul_norm(transform_rotation(x), transform_rotation(y)),
t=quat_rotate(transform_rotation(x), transform_translation(y))
+ transform_translation(x),
)
return z
@torch.jit.script
def transform_apply(rot, vec):
"""
Transform a 3D vector
"""
assert isinstance(vec, torch.Tensor)
return quat_rotate(transform_rotation(rot), vec) + transform_translation(rot)
@torch.jit.script
def rot_matrix_det(x):
"""
Return the determinant of the 3x3 matrix. The shape of the tensor will be as same as the
shape of the matrix
"""
a, b, c = x[..., 0, 0], x[..., 0, 1], x[..., 0, 2]
d, e, f = x[..., 1, 0], x[..., 1, 1], x[..., 1, 2]
g, h, i = x[..., 2, 0], x[..., 2, 1], x[..., 2, 2]
t1 = a * (e * i - f * h)
t2 = b * (d * i - f * g)
t3 = c * (d * h - e * g)
return t1 - t2 + t3
@torch.jit.script
def rot_matrix_integrity_check(x):
"""
Verify that a rotation matrix has a determinant of one and is orthogonal
"""
det = rot_matrix_det(x)
assert bool((abs(det - 1) < 1e-3).all()), "the matrix has non-one determinant"
rtr = x @ x.permute(torch.arange(x.dim() - 2), -1, -2)
rtr_gt = rtr.zeros_like()
rtr_gt[..., 0, 0] = 1
rtr_gt[..., 1, 1] = 1
rtr_gt[..., 2, 2] = 1
assert bool(((rtr - rtr_gt) < 1e-3).all()), "the matrix is not orthogonal"
@torch.jit.script
def rot_matrix_from_quaternion(q):
"""
Construct rotation matrix from quaternion
"""
# Shortcuts for individual elements (using wikipedia's convention)
qi, qj, qk, qr = q[..., 0], q[..., 1], q[..., 2], q[..., 3]
# Set individual elements
R00 = 1.0 - 2.0 * (qj ** 2 + qk ** 2)
R01 = 2 * (qi * qj - qk * qr)
R02 = 2 * (qi * qk + qj * qr)
R10 = 2 * (qi * qj + qk * qr)
R11 = 1.0 - 2.0 * (qi ** 2 + qk ** 2)
R12 = 2 * (qj * qk - qi * qr)
R20 = 2 * (qi * qk - qj * qr)
R21 = 2 * (qj * qk + qi * qr)
R22 = 1.0 - 2.0 * (qi ** 2 + qj ** 2)
R0 = torch.stack([R00, R01, R02], dim=-1)
R1 = torch.stack([R10, R11, R12], dim=-1)
R2 = torch.stack([R10, R21, R22], dim=-1)
R = torch.stack([R0, R1, R2], dim=-2)
return R
@torch.jit.script
def euclidean_to_rotation_matrix(x):
"""
Get the rotation matrix on the top-left corner of a Euclidean transformation matrix
"""
return x[..., :3, :3]
@torch.jit.script
def euclidean_integrity_check(x):
euclidean_to_rotation_matrix(x) # check 3d-rotation matrix
assert bool((x[..., 3, :3] == 0).all()), "the last row is illegal"
assert bool((x[..., 3, 3] == 1).all()), "the last row is illegal"
@torch.jit.script
def euclidean_translation(x):
"""
Get the translation vector located at the last column of the matrix
"""
return x[..., :3, 3]
@torch.jit.script
def euclidean_inverse(x):
"""
Compute the matrix that represents the inverse rotation
"""
s = x.zeros_like()
irot = quat_inverse(quat_from_rotation_matrix(x))
s[..., :3, :3] = irot
s[..., :3, 4] = quat_rotate(irot, -euclidean_translation(x))
return s
@torch.jit.script
def euclidean_to_transform(transformation_matrix):
"""
Construct a transform from a Euclidean transformation matrix
"""
return transform_from_rotation_translation(
r=quat_from_rotation_matrix(
m=euclidean_to_rotation_matrix(transformation_matrix)
),
t=euclidean_translation(transformation_matrix),
)
| 13,466 | Python | 27.471459 | 96 | 0.582207 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/core/tensor_utils.py | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from collections import OrderedDict
from .backend import Serializable
import torch
class TensorUtils(Serializable):
@classmethod
def from_dict(cls, dict_repr, *args, **kwargs):
""" Read the object from an ordered dictionary
:param dict_repr: the ordered dictionary that is used to construct the object
:type dict_repr: OrderedDict
:param kwargs: the arguments that need to be passed into from_dict()
:type kwargs: additional arguments
"""
return torch.from_numpy(dict_repr["arr"].astype(dict_repr["context"]["dtype"]))
def to_dict(self):
""" Construct an ordered dictionary from the object
:rtype: OrderedDict
"""
return NotImplemented
def tensor_to_dict(x):
""" Construct an ordered dictionary from the object
:rtype: OrderedDict
"""
x_np = x.numpy()
return {
"arr": x_np,
"context": {
"dtype": x_np.dtype.name
}
}
| 1,420 | Python | 31.295454 | 87 | 0.674648 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/core/backend/abstract.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 abc import ABCMeta, abstractmethod, abstractclassmethod
from collections import OrderedDict
import json
import numpy as np
import os
TENSOR_CLASS = {}
def register(name):
global TENSOR_CLASS
def core(tensor_cls):
TENSOR_CLASS[name] = tensor_cls
return tensor_cls
return core
def _get_cls(name):
global TENSOR_CLASS
return TENSOR_CLASS[name]
class NumpyEncoder(json.JSONEncoder):
""" Special json encoder for numpy types """
def default(self, obj):
if isinstance(
obj,
(
np.int_,
np.intc,
np.intp,
np.int8,
np.int16,
np.int32,
np.int64,
np.uint8,
np.uint16,
np.uint32,
np.uint64,
),
):
return int(obj)
elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):
return float(obj)
elif isinstance(obj, (np.ndarray,)):
return dict(__ndarray__=obj.tolist(), dtype=str(obj.dtype), shape=obj.shape)
return json.JSONEncoder.default(self, obj)
def json_numpy_obj_hook(dct):
if isinstance(dct, dict) and "__ndarray__" in dct:
data = np.asarray(dct["__ndarray__"], dtype=dct["dtype"])
return data.reshape(dct["shape"])
return dct
class Serializable:
""" Implementation to read/write to file.
All class the is inherited from this class needs to implement to_dict() and
from_dict()
"""
@abstractclassmethod
def from_dict(cls, dict_repr, *args, **kwargs):
""" Read the object from an ordered dictionary
:param dict_repr: the ordered dictionary that is used to construct the object
:type dict_repr: OrderedDict
:param args, kwargs: the arguments that need to be passed into from_dict()
:type args, kwargs: additional arguments
"""
pass
@abstractmethod
def to_dict(self):
""" Construct an ordered dictionary from the object
:rtype: OrderedDict
"""
pass
@classmethod
def from_file(cls, path, *args, **kwargs):
""" Read the object from a file (either .npy or .json)
:param path: path of the file
:type path: string
:param args, kwargs: the arguments that need to be passed into from_dict()
:type args, kwargs: additional arguments
"""
if path.endswith(".json"):
with open(path, "r") as f:
d = json.load(f, object_hook=json_numpy_obj_hook)
elif path.endswith(".npy"):
d = np.load(path, allow_pickle=True).item()
else:
assert False, "failed to load {} from {}".format(cls.__name__, path)
assert d["__name__"] == cls.__name__, "the file belongs to {}, not {}".format(
d["__name__"], cls.__name__
)
return cls.from_dict(d, *args, **kwargs)
def to_file(self, path: str) -> None:
""" Write the object to a file (either .npy or .json)
:param path: path of the file
:type path: string
"""
if os.path.dirname(path) != "" and not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
d = self.to_dict()
d["__name__"] = self.__class__.__name__
if path.endswith(".json"):
with open(path, "w") as f:
json.dump(d, f, cls=NumpyEncoder, indent=4)
elif path.endswith(".npy"):
np.save(path, d)
| 5,159 | Python | 33.172185 | 88 | 0.622795 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/core/backend/__init__.py | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from .abstract import Serializable
from .logger import logger
| 488 | Python | 43.454542 | 76 | 0.815574 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/core/backend/logger.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 logging
logger = logging.getLogger("poselib")
logger.setLevel(logging.INFO)
if not len(logger.handlers):
formatter = logging.Formatter(
fmt="%(asctime)-15s - %(levelname)s - %(module)s - %(message)s"
)
handler = logging.StreamHandler()
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.info("logger initialized")
| 1,930 | Python | 43.906976 | 80 | 0.766321 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/core/tests/test_rotation.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 ..rotation3d import *
import numpy as np
import torch
q = torch.from_numpy(np.array([[0, 1, 2, 3], [-2, 3, -1, 5]], dtype=np.float32))
print("q", q)
r = quat_normalize(q)
x = torch.from_numpy(np.array([[1, 0, 0], [0, -1, 0]], dtype=np.float32))
print(r)
print(quat_rotate(r, x))
angle = torch.from_numpy(np.array(np.random.rand() * 10.0, dtype=np.float32))
axis = torch.from_numpy(
np.array([1, np.random.rand() * 10.0, np.random.rand() * 10.0], dtype=np.float32),
)
print(repr(angle))
print(repr(axis))
rot = quat_from_angle_axis(angle, axis)
x = torch.from_numpy(np.random.rand(5, 6, 3))
y = quat_rotate(quat_inverse(rot), quat_rotate(rot, x))
print(x.numpy())
print(y.numpy())
assert np.allclose(x.numpy(), y.numpy())
m = torch.from_numpy(np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]], dtype=np.float32))
r = quat_from_rotation_matrix(m)
t = torch.from_numpy(np.array([0, 1, 0], dtype=np.float32))
se3 = transform_from_rotation_translation(r=r, t=t)
print(se3)
print(transform_apply(se3, t))
rot = quat_from_angle_axis(
torch.from_numpy(np.array([45, -54], dtype=np.float32)),
torch.from_numpy(np.array([[1, 0, 0], [0, 1, 0]], dtype=np.float32)),
degree=True,
)
trans = torch.from_numpy(np.array([[1, 1, 0], [1, 1, 0]], dtype=np.float32))
transform = transform_from_rotation_translation(r=rot, t=trans)
t = transform_mul(transform, transform_inverse(transform))
gt = np.zeros((2, 7))
gt[:, 0] = 1.0
print(t.numpy())
print(gt)
# assert np.allclose(t.numpy(), gt)
transform2 = torch.from_numpy(
np.array(
[[1, 0, 0, 1], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]], dtype=np.float32
),
)
transform2 = euclidean_to_transform(transform2)
print(transform2)
| 3,256 | Python | 37.317647 | 86 | 0.707924 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/core/tests/__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. | 1,557 | Python | 56.703702 | 80 | 0.784843 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/skeleton/__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. | 1,557 | Python | 56.703702 | 80 | 0.784843 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/skeleton/skeleton3d.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 xml.etree.ElementTree as ET
from collections import OrderedDict
from typing import List, Optional, Type, Dict
import numpy as np
import torch
from ..core import *
from .backend.fbx.fbx_read_wrapper import fbx_to_array
import scipy.ndimage.filters as filters
class SkeletonTree(Serializable):
"""
A skeleton tree gives a complete description of a rigid skeleton. It describes a tree structure
over a list of nodes with their names indicated by strings. Each edge in the tree has a local
translation associated with it which describes the distance between the two nodes that it
connects.
Basic Usage:
>>> t = SkeletonTree.from_mjcf(SkeletonTree.__example_mjcf_path__)
>>> t
SkeletonTree(
node_names=['torso', 'front_left_leg', 'aux_1', 'front_left_foot', 'front_right_leg', 'aux_2', 'front_right_foot', 'left_back_leg', 'aux_3', 'left_back_foot', 'right_back_leg', 'aux_4', 'right_back_foot'],
parent_indices=tensor([-1, 0, 1, 2, 0, 4, 5, 0, 7, 8, 0, 10, 11]),
local_translation=tensor([[ 0.0000, 0.0000, 0.7500],
[ 0.0000, 0.0000, 0.0000],
[ 0.2000, 0.2000, 0.0000],
[ 0.2000, 0.2000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[-0.2000, 0.2000, 0.0000],
[-0.2000, 0.2000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[-0.2000, -0.2000, 0.0000],
[-0.2000, -0.2000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.2000, -0.2000, 0.0000],
[ 0.2000, -0.2000, 0.0000]])
)
>>> t.node_names
['torso', 'front_left_leg', 'aux_1', 'front_left_foot', 'front_right_leg', 'aux_2', 'front_right_foot', 'left_back_leg', 'aux_3', 'left_back_foot', 'right_back_leg', 'aux_4', 'right_back_foot']
>>> t.parent_indices
tensor([-1, 0, 1, 2, 0, 4, 5, 0, 7, 8, 0, 10, 11])
>>> t.local_translation
tensor([[ 0.0000, 0.0000, 0.7500],
[ 0.0000, 0.0000, 0.0000],
[ 0.2000, 0.2000, 0.0000],
[ 0.2000, 0.2000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[-0.2000, 0.2000, 0.0000],
[-0.2000, 0.2000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[-0.2000, -0.2000, 0.0000],
[-0.2000, -0.2000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.2000, -0.2000, 0.0000],
[ 0.2000, -0.2000, 0.0000]])
>>> t.parent_of('front_left_leg')
'torso'
>>> t.index('front_right_foot')
6
>>> t[2]
'aux_1'
"""
__example_mjcf_path__ = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "tests/ant.xml"
)
def __init__(self, node_names, parent_indices, local_translation):
"""
:param node_names: a list of names for each tree node
:type node_names: List[str]
:param parent_indices: an int32-typed tensor that represents the edge to its parent.\
-1 represents the root node
:type parent_indices: Tensor
:param local_translation: a 3d vector that gives local translation information
:type local_translation: Tensor
"""
ln, lp, ll = len(node_names), len(parent_indices), len(local_translation)
assert len(set((ln, lp, ll))) == 1
self._node_names = node_names
self._parent_indices = parent_indices.long()
self._local_translation = local_translation
self._node_indices = {self.node_names[i]: i for i in range(len(self))}
def __len__(self):
""" number of nodes in the skeleton tree """
return len(self.node_names)
def __iter__(self):
""" iterator that iterate through the name of each node """
yield from self.node_names
def __getitem__(self, item):
""" get the name of the node given the index """
return self.node_names[item]
def __repr__(self):
return (
"SkeletonTree(\n node_names={},\n parent_indices={},"
"\n local_translation={}\n)".format(
self._indent(repr(self.node_names)),
self._indent(repr(self.parent_indices)),
self._indent(repr(self.local_translation)),
)
)
def _indent(self, s):
return "\n ".join(s.split("\n"))
@property
def node_names(self):
return self._node_names
@property
def parent_indices(self):
return self._parent_indices
@property
def local_translation(self):
return self._local_translation
@property
def num_joints(self):
""" number of nodes in the skeleton tree """
return len(self)
@classmethod
def from_dict(cls, dict_repr, *args, **kwargs):
return cls(
list(map(str, dict_repr["node_names"])),
TensorUtils.from_dict(dict_repr["parent_indices"], *args, **kwargs),
TensorUtils.from_dict(dict_repr["local_translation"], *args, **kwargs),
)
def to_dict(self):
return OrderedDict(
[
("node_names", self.node_names),
("parent_indices", tensor_to_dict(self.parent_indices)),
("local_translation", tensor_to_dict(self.local_translation)),
]
)
@classmethod
def from_mjcf(cls, path: str) -> "SkeletonTree":
"""
Parses a mujoco xml scene description file and returns a Skeleton Tree.
We use the model attribute at the root as the name of the tree.
:param path:
:type path: string
:return: The skeleton tree constructed from the mjcf file
:rtype: SkeletonTree
"""
tree = ET.parse(path)
xml_doc_root = tree.getroot()
xml_world_body = xml_doc_root.find("worldbody")
if xml_world_body is None:
raise ValueError("MJCF parsed incorrectly please verify it.")
# assume this is the root
xml_body_root = xml_world_body.find("body")
if xml_body_root is None:
raise ValueError("MJCF parsed incorrectly please verify it.")
node_names = []
parent_indices = []
local_translation = []
# recursively adding all nodes into the skel_tree
def _add_xml_node(xml_node, parent_index, node_index):
node_name = xml_node.attrib.get("name")
# parse the local translation into float list
pos = np.fromstring(xml_node.attrib.get("pos"), dtype=float, sep=" ")
node_names.append(node_name)
parent_indices.append(parent_index)
local_translation.append(pos)
curr_index = node_index
node_index += 1
for next_node in xml_node.findall("body"):
node_index = _add_xml_node(next_node, curr_index, node_index)
return node_index
_add_xml_node(xml_body_root, -1, 0)
return cls(
node_names,
torch.from_numpy(np.array(parent_indices, dtype=np.int32)),
torch.from_numpy(np.array(local_translation, dtype=np.float32)),
)
def parent_of(self, node_name):
""" get the name of the parent of the given node
:param node_name: the name of the node
:type node_name: string
:rtype: string
"""
return self[int(self.parent_indices[self.index(node_name)].item())]
def index(self, node_name):
""" get the index of the node
:param node_name: the name of the node
:type node_name: string
:rtype: int
"""
return self._node_indices[node_name]
def drop_nodes_by_names(
self, node_names: List[str], pairwise_translation=None
) -> "SkeletonTree":
new_length = len(self) - len(node_names)
new_node_names = []
new_local_translation = torch.zeros(
new_length, 3, dtype=self.local_translation.dtype
)
new_parent_indices = torch.zeros(new_length, dtype=self.parent_indices.dtype)
parent_indices = self.parent_indices.numpy()
new_node_indices: dict = {}
new_node_index = 0
for node_index in range(len(self)):
if self[node_index] in node_names:
continue
tb_node_index = parent_indices[node_index]
if tb_node_index != -1:
local_translation = self.local_translation[node_index, :]
while tb_node_index != -1 and self[tb_node_index] in node_names:
local_translation += self.local_translation[tb_node_index, :]
tb_node_index = parent_indices[tb_node_index]
assert tb_node_index != -1, "the root node cannot be dropped"
if pairwise_translation is not None:
local_translation = pairwise_translation[
tb_node_index, node_index, :
]
else:
local_translation = self.local_translation[node_index, :]
new_node_names.append(self[node_index])
new_local_translation[new_node_index, :] = local_translation
if tb_node_index == -1:
new_parent_indices[new_node_index] = -1
else:
new_parent_indices[new_node_index] = new_node_indices[
self[tb_node_index]
]
new_node_indices[self[node_index]] = new_node_index
new_node_index += 1
return SkeletonTree(new_node_names, new_parent_indices, new_local_translation)
def keep_nodes_by_names(
self, node_names: List[str], pairwise_translation=None
) -> "SkeletonTree":
nodes_to_drop = list(filter(lambda x: x not in node_names, self))
return self.drop_nodes_by_names(nodes_to_drop, pairwise_translation)
class SkeletonState(Serializable):
"""
A skeleton state contains all the information needed to describe a static state of a skeleton.
It requires a skeleton tree, local/global rotation at each joint and the root translation.
Example:
>>> t = SkeletonTree.from_mjcf(SkeletonTree.__example_mjcf_path__)
>>> zero_pose = SkeletonState.zero_pose(t)
>>> plot_skeleton_state(zero_pose) # can be imported from `.visualization.common`
[plot of the ant at zero pose
>>> local_rotation = zero_pose.local_rotation.clone()
>>> local_rotation[2] = torch.tensor([0, 0, 1, 0])
>>> new_pose = SkeletonState.from_rotation_and_root_translation(
... skeleton_tree=t,
... r=local_rotation,
... t=zero_pose.root_translation,
... is_local=True
... )
>>> new_pose.local_rotation
tensor([[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 1., 0., 0.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.]])
>>> plot_skeleton_state(new_pose) # you should be able to see one of ant's leg is bent
[plot of the ant with the new pose
>>> new_pose.global_rotation # the local rotation is propagated to the global rotation at joint #3
tensor([[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 1., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.]])
Global/Local Representation (cont. from the previous example)
>>> new_pose.is_local
True
>>> new_pose.tensor # this will return the local rotation followed by the root translation
tensor([0., 0., 0., 1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0.,
0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1.,
0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0.,
0.])
>>> new_pose.tensor.shape # 4 * 13 (joint rotation) + 3 (root translatio
torch.Size([55])
>>> new_pose.global_repr().is_local
False
>>> new_pose.global_repr().tensor # this will return the global rotation followed by the root translation instead
tensor([0., 0., 0., 1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0.,
0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1.,
0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0.,
0.])
>>> new_pose.global_repr().tensor.shape # 4 * 13 (joint rotation) + 3 (root translation
torch.Size([55])
"""
def __init__(self, tensor_backend, skeleton_tree, is_local):
self._skeleton_tree = skeleton_tree
self._is_local = is_local
self.tensor = tensor_backend.clone()
def __len__(self):
return self.tensor.shape[0]
@property
def rotation(self):
if not hasattr(self, "_rotation"):
self._rotation = self.tensor[..., : self.num_joints * 4].reshape(
*(self.tensor.shape[:-1] + (self.num_joints, 4))
)
return self._rotation
@property
def _local_rotation(self):
if self._is_local:
return self.rotation
else:
return None
@property
def _global_rotation(self):
if not self._is_local:
return self.rotation
else:
return None
@property
def is_local(self):
""" is the rotation represented in local frame?
:rtype: bool
"""
return self._is_local
@property
def invariant_property(self):
return {"skeleton_tree": self.skeleton_tree, "is_local": self.is_local}
@property
def num_joints(self):
""" number of joints in the skeleton tree
:rtype: int
"""
return self.skeleton_tree.num_joints
@property
def skeleton_tree(self):
""" skeleton tree
:rtype: SkeletonTree
"""
return self._skeleton_tree
@property
def root_translation(self):
""" root translation
:rtype: Tensor
"""
if not hasattr(self, "_root_translation"):
self._root_translation = self.tensor[
..., self.num_joints * 4 : self.num_joints * 4 + 3
]
return self._root_translation
@property
def global_transformation(self):
""" global transformation of each joint (transform from joint frame to global frame) """
if not hasattr(self, "_global_transformation"):
local_transformation = self.local_transformation
global_transformation = []
parent_indices = self.skeleton_tree.parent_indices.numpy()
# global_transformation = local_transformation.identity_like()
for node_index in range(len(self.skeleton_tree)):
parent_index = parent_indices[node_index]
if parent_index == -1:
global_transformation.append(
local_transformation[..., node_index, :]
)
else:
global_transformation.append(
transform_mul(
global_transformation[parent_index],
local_transformation[..., node_index, :],
)
)
self._global_transformation = torch.stack(global_transformation, axis=-2)
return self._global_transformation
@property
def global_rotation(self):
""" global rotation of each joint (rotation matrix to rotate from joint's F.O.R to global
F.O.R) """
if self._global_rotation is None:
if not hasattr(self, "_comp_global_rotation"):
self._comp_global_rotation = transform_rotation(
self.global_transformation
)
return self._comp_global_rotation
else:
return self._global_rotation
@property
def global_translation(self):
""" global translation of each joint """
if not hasattr(self, "_global_translation"):
self._global_translation = transform_translation(self.global_transformation)
return self._global_translation
@property
def global_translation_xy(self):
""" global translation in xy """
trans_xy_data = self.global_translation.zeros_like()
trans_xy_data[..., 0:2] = self.global_translation[..., 0:2]
return trans_xy_data
@property
def global_translation_xz(self):
""" global translation in xz """
trans_xz_data = self.global_translation.zeros_like()
trans_xz_data[..., 0:1] = self.global_translation[..., 0:1]
trans_xz_data[..., 2:3] = self.global_translation[..., 2:3]
return trans_xz_data
@property
def local_rotation(self):
""" the rotation from child frame to parent frame given in the order of child nodes appeared
in `.skeleton_tree.node_names` """
if self._local_rotation is None:
if not hasattr(self, "_comp_local_rotation"):
local_rotation = quat_identity_like(self.global_rotation)
for node_index in range(len(self.skeleton_tree)):
parent_index = self.skeleton_tree.parent_indices[node_index]
if parent_index == -1:
local_rotation[..., node_index, :] = self.global_rotation[
..., node_index, :
]
else:
local_rotation[..., node_index, :] = quat_mul_norm(
quat_inverse(self.global_rotation[..., parent_index, :]),
self.global_rotation[..., node_index, :],
)
self._comp_local_rotation = local_rotation
return self._comp_local_rotation
else:
return self._local_rotation
@property
def local_transformation(self):
""" local translation + local rotation. It describes the transformation from child frame to
parent frame given in the order of child nodes appeared in `.skeleton_tree.node_names` """
if not hasattr(self, "_local_transformation"):
self._local_transformation = transform_from_rotation_translation(
r=self.local_rotation, t=self.local_translation
)
return self._local_transformation
@property
def local_translation(self):
""" local translation of the skeleton state. It is identical to the local translation in
`.skeleton_tree.local_translation` except the root translation. The root translation is
identical to `.root_translation` """
if not hasattr(self, "_local_translation"):
broadcast_shape = (
tuple(self.tensor.shape[:-1])
+ (len(self.skeleton_tree),)
+ tuple(self.skeleton_tree.local_translation.shape[-1:])
)
local_translation = self.skeleton_tree.local_translation.broadcast_to(
*broadcast_shape
).clone()
local_translation[..., 0, :] = self.root_translation
self._local_translation = local_translation
return self._local_translation
# Root Properties
@property
def root_translation_xy(self):
""" root translation on xy """
if not hasattr(self, "_root_translation_xy"):
self._root_translation_xy = self.global_translation_xy[..., 0, :]
return self._root_translation_xy
@property
def global_root_rotation(self):
""" root rotation """
if not hasattr(self, "_global_root_rotation"):
self._global_root_rotation = self.global_rotation[..., 0, :]
return self._global_root_rotation
@property
def global_root_yaw_rotation(self):
""" root yaw rotation """
if not hasattr(self, "_global_root_yaw_rotation"):
self._global_root_yaw_rotation = self.global_root_rotation.yaw_rotation()
return self._global_root_yaw_rotation
# Properties relative to root
@property
def local_translation_to_root(self):
""" The 3D translation from joint frame to the root frame. """
if not hasattr(self, "_local_translation_to_root"):
self._local_translation_to_root = (
self.global_translation - self.root_translation.unsqueeze(-1)
)
return self._local_translation_to_root
@property
def local_rotation_to_root(self):
""" The 3D rotation from joint frame to the root frame. It is equivalent to
The root_R_world * world_R_node """
return (
quat_inverse(self.global_root_rotation).unsqueeze(-1) * self.global_rotation
)
def compute_forward_vector(
self,
left_shoulder_index,
right_shoulder_index,
left_hip_index,
right_hip_index,
gaussian_filter_width=20,
):
""" Computes forward vector based on cross product of the up vector with
average of the right->left shoulder and hip vectors """
global_positions = self.global_translation
# Perpendicular to the forward direction.
# Uses the shoulders and hips to find this.
side_direction = (
global_positions[:, left_shoulder_index].numpy()
- global_positions[:, right_shoulder_index].numpy()
+ global_positions[:, left_hip_index].numpy()
- global_positions[:, right_hip_index].numpy()
)
side_direction = (
side_direction
/ np.sqrt((side_direction ** 2).sum(axis=-1))[..., np.newaxis]
)
# Forward direction obtained by crossing with the up direction.
forward_direction = np.cross(side_direction, np.array([[0, 1, 0]]))
# Smooth the forward direction with a Gaussian.
# Axis 0 is the time/frame axis.
forward_direction = filters.gaussian_filter1d(
forward_direction, gaussian_filter_width, axis=0, mode="nearest"
)
forward_direction = (
forward_direction
/ np.sqrt((forward_direction ** 2).sum(axis=-1))[..., np.newaxis]
)
return torch.from_numpy(forward_direction)
@staticmethod
def _to_state_vector(rot, rt):
state_shape = rot.shape[:-2]
vr = rot.reshape(*(state_shape + (-1,)))
vt = rt.broadcast_to(*state_shape + rt.shape[-1:]).reshape(
*(state_shape + (-1,))
)
v = torch.cat([vr, vt], axis=-1)
return v
@classmethod
def from_dict(
cls: Type["SkeletonState"], dict_repr: OrderedDict, *args, **kwargs
) -> "SkeletonState":
rot = TensorUtils.from_dict(dict_repr["rotation"], *args, **kwargs)
rt = TensorUtils.from_dict(dict_repr["root_translation"], *args, **kwargs)
return cls(
SkeletonState._to_state_vector(rot, rt),
SkeletonTree.from_dict(dict_repr["skeleton_tree"], *args, **kwargs),
dict_repr["is_local"],
)
def to_dict(self) -> OrderedDict:
return OrderedDict(
[
("rotation", tensor_to_dict(self.rotation)),
("root_translation", tensor_to_dict(self.root_translation)),
("skeleton_tree", self.skeleton_tree.to_dict()),
("is_local", self.is_local),
]
)
@classmethod
def from_rotation_and_root_translation(cls, skeleton_tree, r, t, is_local=True):
"""
Construct a skeleton state from rotation and root translation
:param skeleton_tree: the skeleton tree
:type skeleton_tree: SkeletonTree
:param r: rotation (either global or local)
:type r: Tensor
:param t: root translation
:type t: Tensor
:param is_local: to indicate that whether the rotation is local or global
:type is_local: bool, optional, default=True
"""
assert (
r.dim() > 0
), "the rotation needs to have at least 1 dimension (dim = {})".format(r.dim)
return cls(
SkeletonState._to_state_vector(r, t),
skeleton_tree=skeleton_tree,
is_local=is_local,
)
@classmethod
def zero_pose(cls, skeleton_tree):
"""
Construct a zero-pose skeleton state from the skeleton tree by assuming that all the local
rotation is 0 and root translation is also 0.
:param skeleton_tree: the skeleton tree as the rigid body
:type skeleton_tree: SkeletonTree
"""
return cls.from_rotation_and_root_translation(
skeleton_tree=skeleton_tree,
r=quat_identity([skeleton_tree.num_joints]),
t=torch.zeros(3, dtype=skeleton_tree.local_translation.dtype),
is_local=True,
)
def local_repr(self):
"""
Convert the skeleton state into local representation. This will only affects the values of
.tensor. If the skeleton state already has `is_local=True`. This method will do nothing.
:rtype: SkeletonState
"""
if self.is_local:
return self
return SkeletonState.from_rotation_and_root_translation(
self.skeleton_tree,
r=self.local_rotation,
t=self.root_translation,
is_local=True,
)
def global_repr(self):
"""
Convert the skeleton state into global representation. This will only affects the values of
.tensor. If the skeleton state already has `is_local=False`. This method will do nothing.
:rtype: SkeletonState
"""
if not self.is_local:
return self
return SkeletonState.from_rotation_and_root_translation(
self.skeleton_tree,
r=self.global_rotation,
t=self.root_translation,
is_local=False,
)
def _get_pairwise_average_translation(self):
global_transform_inv = transform_inverse(self.global_transformation)
p1 = global_transform_inv.unsqueeze(-2)
p2 = self.global_transformation.unsqueeze(-3)
pairwise_translation = (
transform_translation(transform_mul(p1, p2))
.reshape(-1, len(self.skeleton_tree), len(self.skeleton_tree), 3)
.mean(axis=0)
)
return pairwise_translation
def _transfer_to(self, new_skeleton_tree: SkeletonTree):
old_indices = list(map(self.skeleton_tree.index, new_skeleton_tree))
return SkeletonState.from_rotation_and_root_translation(
new_skeleton_tree,
r=self.global_rotation[..., old_indices, :],
t=self.root_translation,
is_local=False,
)
def drop_nodes_by_names(
self, node_names: List[str], estimate_local_translation_from_states: bool = True
) -> "SkeletonState":
"""
Drop a list of nodes from the skeleton and re-compute the local rotation to match the
original joint position as much as possible.
:param node_names: a list node names that specifies the nodes need to be dropped
:type node_names: List of strings
:param estimate_local_translation_from_states: the boolean indicator that specifies whether\
or not to re-estimate the local translation from the states (avg.)
:type estimate_local_translation_from_states: boolean
:rtype: SkeletonState
"""
if estimate_local_translation_from_states:
pairwise_translation = self._get_pairwise_average_translation()
else:
pairwise_translation = None
new_skeleton_tree = self.skeleton_tree.drop_nodes_by_names(
node_names, pairwise_translation
)
return self._transfer_to(new_skeleton_tree)
def keep_nodes_by_names(
self, node_names: List[str], estimate_local_translation_from_states: bool = True
) -> "SkeletonState":
"""
Keep a list of nodes and drop all other nodes from the skeleton and re-compute the local
rotation to match the original joint position as much as possible.
:param node_names: a list node names that specifies the nodes need to be dropped
:type node_names: List of strings
:param estimate_local_translation_from_states: the boolean indicator that specifies whether\
or not to re-estimate the local translation from the states (avg.)
:type estimate_local_translation_from_states: boolean
:rtype: SkeletonState
"""
return self.drop_nodes_by_names(
list(filter(lambda x: (x not in node_names), self)),
estimate_local_translation_from_states,
)
def _remapped_to(
self, joint_mapping: Dict[str, str], target_skeleton_tree: SkeletonTree
):
joint_mapping_inv = {target: source for source, target in joint_mapping.items()}
reduced_target_skeleton_tree = target_skeleton_tree.keep_nodes_by_names(
list(joint_mapping_inv)
)
n_joints = (
len(joint_mapping),
len(self.skeleton_tree),
len(reduced_target_skeleton_tree),
)
assert (
len(set(n_joints)) == 1
), "the joint mapping is not consistent with the skeleton trees"
source_indices = list(
map(
lambda x: self.skeleton_tree.index(joint_mapping_inv[x]),
reduced_target_skeleton_tree,
)
)
target_local_rotation = self.local_rotation[..., source_indices, :]
return SkeletonState.from_rotation_and_root_translation(
skeleton_tree=reduced_target_skeleton_tree,
r=target_local_rotation,
t=self.root_translation,
is_local=True,
)
def retarget_to(
self,
joint_mapping: Dict[str, str],
source_tpose_local_rotation,
source_tpose_root_translation: np.ndarray,
target_skeleton_tree: SkeletonTree,
target_tpose_local_rotation,
target_tpose_root_translation: np.ndarray,
rotation_to_target_skeleton,
scale_to_target_skeleton: float,
z_up: bool = True,
) -> "SkeletonState":
"""
Retarget the skeleton state to a target skeleton tree. This is a naive retarget
implementation with rough approximations. The function follows the procedures below.
Steps:
1. Drop the joints from the source (self) that do not belong to the joint mapping\
with an implementation that is similar to "keep_nodes_by_names()" - take a\
look at the function doc for more details (same for source_tpose)
2. Rotate the source state and the source tpose by "rotation_to_target_skeleton"\
to align the source with the target orientation
3. Extract the root translation and normalize it to match the scale of the target\
skeleton
4. Extract the global rotation from source state relative to source tpose and\
re-apply the relative rotation to the target tpose to construct the global\
rotation after retargetting
5. Combine the computed global rotation and the root translation from 3 and 4 to\
complete the retargeting.
6. Make feet on the ground (global translation z)
:param joint_mapping: a dictionary of that maps the joint node from the source skeleton to \
the target skeleton
:type joint_mapping: Dict[str, str]
:param source_tpose_local_rotation: the local rotation of the source skeleton
:type source_tpose_local_rotation: Tensor
:param source_tpose_root_translation: the root translation of the source tpose
:type source_tpose_root_translation: np.ndarray
:param target_skeleton_tree: the target skeleton tree
:type target_skeleton_tree: SkeletonTree
:param target_tpose_local_rotation: the local rotation of the target skeleton
:type target_tpose_local_rotation: Tensor
:param target_tpose_root_translation: the root translation of the target tpose
:type target_tpose_root_translation: Tensor
:param rotation_to_target_skeleton: the rotation that needs to be applied to the source\
skeleton to align with the target skeleton. Essentially the rotation is t_R_s, where t is\
the frame of reference of the target skeleton and s is the frame of reference of the source\
skeleton
:type rotation_to_target_skeleton: Tensor
:param scale_to_target_skeleton: the factor that needs to be multiplied from source\
skeleton to target skeleton (unit in distance). For example, to go from `cm` to `m`, the \
factor needs to be 0.01.
:type scale_to_target_skeleton: float
:rtype: SkeletonState
"""
# STEP 0: Preprocess
source_tpose = SkeletonState.from_rotation_and_root_translation(
skeleton_tree=self.skeleton_tree,
r=source_tpose_local_rotation,
t=source_tpose_root_translation,
is_local=True,
)
target_tpose = SkeletonState.from_rotation_and_root_translation(
skeleton_tree=target_skeleton_tree,
r=target_tpose_local_rotation,
t=target_tpose_root_translation,
is_local=True,
)
# STEP 1: Drop the irrelevant joints
pairwise_translation = self._get_pairwise_average_translation()
node_names = list(joint_mapping)
new_skeleton_tree = self.skeleton_tree.keep_nodes_by_names(
node_names, pairwise_translation
)
# TODO: combine the following steps before STEP 3
source_tpose = source_tpose._transfer_to(new_skeleton_tree)
source_state = self._transfer_to(new_skeleton_tree)
source_tpose = source_tpose._remapped_to(joint_mapping, target_skeleton_tree)
source_state = source_state._remapped_to(joint_mapping, target_skeleton_tree)
# STEP 2: Rotate the source to align with the target
new_local_rotation = source_tpose.local_rotation.clone()
new_local_rotation[..., 0, :] = quat_mul_norm(
rotation_to_target_skeleton, source_tpose.local_rotation[..., 0, :]
)
source_tpose = SkeletonState.from_rotation_and_root_translation(
skeleton_tree=source_tpose.skeleton_tree,
r=new_local_rotation,
t=quat_rotate(rotation_to_target_skeleton, source_tpose.root_translation),
is_local=True,
)
new_local_rotation = source_state.local_rotation.clone()
new_local_rotation[..., 0, :] = quat_mul_norm(
rotation_to_target_skeleton, source_state.local_rotation[..., 0, :]
)
source_state = SkeletonState.from_rotation_and_root_translation(
skeleton_tree=source_state.skeleton_tree,
r=new_local_rotation,
t=quat_rotate(rotation_to_target_skeleton, source_state.root_translation),
is_local=True,
)
# STEP 3: Normalize to match the target scale
root_translation_diff = (
source_state.root_translation - source_tpose.root_translation
) * scale_to_target_skeleton
# STEP 4: the global rotation from source state relative to source tpose and
# re-apply to the target
current_skeleton_tree = source_state.skeleton_tree
target_tpose_global_rotation = source_state.global_rotation[0, :].clone()
for current_index, name in enumerate(current_skeleton_tree):
if name in target_tpose.skeleton_tree:
target_tpose_global_rotation[
current_index, :
] = target_tpose.global_rotation[
target_tpose.skeleton_tree.index(name), :
]
global_rotation_diff = quat_mul_norm(
source_state.global_rotation, quat_inverse(source_tpose.global_rotation)
)
new_global_rotation = quat_mul_norm(
global_rotation_diff, target_tpose_global_rotation
)
# STEP 5: Putting 3 and 4 together
current_skeleton_tree = source_state.skeleton_tree
shape = source_state.global_rotation.shape[:-1]
shape = shape[:-1] + target_tpose.global_rotation.shape[-2:-1]
new_global_rotation_output = quat_identity(shape)
for current_index, name in enumerate(target_skeleton_tree):
while name not in current_skeleton_tree:
name = target_skeleton_tree.parent_of(name)
parent_index = current_skeleton_tree.index(name)
new_global_rotation_output[:, current_index, :] = new_global_rotation[
:, parent_index, :
]
source_state = SkeletonState.from_rotation_and_root_translation(
skeleton_tree=target_skeleton_tree,
r=new_global_rotation_output,
t=target_tpose.root_translation + root_translation_diff,
is_local=False,
).local_repr()
return source_state
def retarget_to_by_tpose(
self,
joint_mapping: Dict[str, str],
source_tpose: "SkeletonState",
target_tpose: "SkeletonState",
rotation_to_target_skeleton,
scale_to_target_skeleton: float,
) -> "SkeletonState":
"""
Retarget the skeleton state to a target skeleton tree. This is a naive retarget
implementation with rough approximations. See the method `retarget_to()` for more information
:param joint_mapping: a dictionary of that maps the joint node from the source skeleton to \
the target skeleton
:type joint_mapping: Dict[str, str]
:param source_tpose: t-pose of the source skeleton
:type source_tpose: SkeletonState
:param target_tpose: t-pose of the target skeleton
:type target_tpose: SkeletonState
:param rotation_to_target_skeleton: the rotation that needs to be applied to the source\
skeleton to align with the target skeleton. Essentially the rotation is t_R_s, where t is\
the frame of reference of the target skeleton and s is the frame of reference of the source\
skeleton
:type rotation_to_target_skeleton: Tensor
:param scale_to_target_skeleton: the factor that needs to be multiplied from source\
skeleton to target skeleton (unit in distance). For example, to go from `cm` to `m`, the \
factor needs to be 0.01.
:type scale_to_target_skeleton: float
:rtype: SkeletonState
"""
assert (
len(source_tpose.shape) == 0 and len(target_tpose.shape) == 0
), "the retargeting script currently doesn't support vectorized operations"
return self.retarget_to(
joint_mapping,
source_tpose.local_rotation,
source_tpose.root_translation,
target_tpose.skeleton_tree,
target_tpose.local_rotation,
target_tpose.root_translation,
rotation_to_target_skeleton,
scale_to_target_skeleton,
)
class SkeletonMotion(SkeletonState):
def __init__(self, tensor_backend, skeleton_tree, is_local, fps, *args, **kwargs):
self._fps = fps
super().__init__(tensor_backend, skeleton_tree, is_local, *args, **kwargs)
def clone(self):
return SkeletonMotion(
self.tensor.clone(), self.skeleton_tree, self._is_local, self._fps
)
@property
def invariant_property(self):
return {
"skeleton_tree": self.skeleton_tree,
"is_local": self.is_local,
"fps": self.fps,
}
@property
def global_velocity(self):
""" global velocity """
curr_index = self.num_joints * 4 + 3
return self.tensor[..., curr_index : curr_index + self.num_joints * 3].reshape(
*(self.tensor.shape[:-1] + (self.num_joints, 3))
)
@property
def global_angular_velocity(self):
""" global angular velocity """
curr_index = self.num_joints * 7 + 3
return self.tensor[..., curr_index : curr_index + self.num_joints * 3].reshape(
*(self.tensor.shape[:-1] + (self.num_joints, 3))
)
@property
def fps(self):
""" number of frames per second """
return self._fps
@property
def time_delta(self):
""" time between two adjacent frames """
return 1.0 / self.fps
@property
def global_root_velocity(self):
""" global root velocity """
return self.global_velocity[..., 0, :]
@property
def global_root_angular_velocity(self):
""" global root angular velocity """
return self.global_angular_velocity[..., 0, :]
@classmethod
def from_state_vector_and_velocity(
cls,
skeleton_tree,
state_vector,
global_velocity,
global_angular_velocity,
is_local,
fps,
):
"""
Construct a skeleton motion from a skeleton state vector, global velocity and angular
velocity at each joint.
:param skeleton_tree: the skeleton tree that the motion is based on
:type skeleton_tree: SkeletonTree
:param state_vector: the state vector from the skeleton state by `.tensor`
:type state_vector: Tensor
:param global_velocity: the global velocity at each joint
:type global_velocity: Tensor
:param global_angular_velocity: the global angular velocity at each joint
:type global_angular_velocity: Tensor
:param is_local: if the rotation ins the state vector is given in local frame
:type is_local: boolean
:param fps: number of frames per second
:type fps: int
:rtype: SkeletonMotion
"""
state_shape = state_vector.shape[:-1]
v = global_velocity.reshape(*(state_shape + (-1,)))
av = global_angular_velocity.reshape(*(state_shape + (-1,)))
new_state_vector = torch.cat([state_vector, v, av], axis=-1)
return cls(
new_state_vector, skeleton_tree=skeleton_tree, is_local=is_local, fps=fps,
)
@classmethod
def from_skeleton_state(
cls: Type["SkeletonMotion"], skeleton_state: SkeletonState, fps: int
):
"""
Construct a skeleton motion from a skeleton state. The velocities are estimated using second
order gaussian filter along the last axis. The skeleton state must have at least .dim >= 1
:param skeleton_state: the skeleton state that the motion is based on
:type skeleton_state: SkeletonState
:param fps: number of frames per second
:type fps: int
:rtype: SkeletonMotion
"""
assert (
type(skeleton_state) == SkeletonState
), "expected type of {}, got {}".format(SkeletonState, type(skeleton_state))
global_velocity = SkeletonMotion._compute_velocity(
p=skeleton_state.global_translation, time_delta=1 / fps
)
global_angular_velocity = SkeletonMotion._compute_angular_velocity(
r=skeleton_state.global_rotation, time_delta=1 / fps
)
return cls.from_state_vector_and_velocity(
skeleton_tree=skeleton_state.skeleton_tree,
state_vector=skeleton_state.tensor,
global_velocity=global_velocity,
global_angular_velocity=global_angular_velocity,
is_local=skeleton_state.is_local,
fps=fps,
)
@staticmethod
def _to_state_vector(rot, rt, vel, avel):
state_shape = rot.shape[:-2]
skeleton_state_v = SkeletonState._to_state_vector(rot, rt)
v = vel.reshape(*(state_shape + (-1,)))
av = avel.reshape(*(state_shape + (-1,)))
skeleton_motion_v = torch.cat([skeleton_state_v, v, av], axis=-1)
return skeleton_motion_v
@classmethod
def from_dict(
cls: Type["SkeletonMotion"], dict_repr: OrderedDict, *args, **kwargs
) -> "SkeletonMotion":
rot = TensorUtils.from_dict(dict_repr["rotation"], *args, **kwargs)
rt = TensorUtils.from_dict(dict_repr["root_translation"], *args, **kwargs)
vel = TensorUtils.from_dict(dict_repr["global_velocity"], *args, **kwargs)
avel = TensorUtils.from_dict(
dict_repr["global_angular_velocity"], *args, **kwargs
)
return cls(
SkeletonMotion._to_state_vector(rot, rt, vel, avel),
skeleton_tree=SkeletonTree.from_dict(
dict_repr["skeleton_tree"], *args, **kwargs
),
is_local=dict_repr["is_local"],
fps=dict_repr["fps"],
)
def to_dict(self) -> OrderedDict:
return OrderedDict(
[
("rotation", tensor_to_dict(self.rotation)),
("root_translation", tensor_to_dict(self.root_translation)),
("global_velocity", tensor_to_dict(self.global_velocity)),
("global_angular_velocity", tensor_to_dict(self.global_angular_velocity)),
("skeleton_tree", self.skeleton_tree.to_dict()),
("is_local", self.is_local),
("fps", self.fps),
]
)
@classmethod
def from_fbx(
cls: Type["SkeletonMotion"],
fbx_file_path,
skeleton_tree=None,
is_local=True,
fps=120,
root_joint="",
root_trans_index=0,
*args,
**kwargs,
) -> "SkeletonMotion":
"""
Construct a skeleton motion from a fbx file (TODO - generalize this). If the skeleton tree
is not given, it will use the first frame of the mocap to construct the skeleton tree.
:param fbx_file_path: the path of the fbx file
:type fbx_file_path: string
:param fbx_configs: the configuration in terms of {"tmp_path": ..., "fbx_py27_path": ...}
:type fbx_configs: dict
:param skeleton_tree: the optional skeleton tree that the rotation will be applied to
:type skeleton_tree: SkeletonTree, optional
:param is_local: the state vector uses local or global rotation as the representation
:type is_local: bool, optional, default=True
:param fps: FPS of the FBX animation
:type fps: int, optional, default=120
:param root_joint: the name of the root joint for the skeleton
:type root_joint: string, optional, default="" or the first node in the FBX scene with animation data
:param root_trans_index: index of joint to extract root transform from
:type root_trans_index: int, optional, default=0 or the root joint in the parsed skeleton
:rtype: SkeletonMotion
"""
joint_names, joint_parents, transforms, fps = fbx_to_array(
fbx_file_path, root_joint, fps
)
# swap the last two axis to match the convention
local_transform = euclidean_to_transform(
transformation_matrix=torch.from_numpy(
np.swapaxes(np.array(transforms), -1, -2),
).float()
)
local_rotation = transform_rotation(local_transform)
root_translation = transform_translation(local_transform)[..., root_trans_index, :]
joint_parents = torch.from_numpy(np.array(joint_parents)).int()
if skeleton_tree is None:
local_translation = transform_translation(local_transform).reshape(
-1, len(joint_parents), 3
)[0]
skeleton_tree = SkeletonTree(joint_names, joint_parents, local_translation)
skeleton_state = SkeletonState.from_rotation_and_root_translation(
skeleton_tree, r=local_rotation, t=root_translation, is_local=True
)
if not is_local:
skeleton_state = skeleton_state.global_repr()
return cls.from_skeleton_state(
skeleton_state=skeleton_state, fps=fps
)
@staticmethod
def _compute_velocity(p, time_delta, guassian_filter=True):
velocity = torch.from_numpy(
filters.gaussian_filter1d(
np.gradient(p.numpy(), axis=-3), 2, axis=-3, mode="nearest"
)
/ time_delta,
)
return velocity
@staticmethod
def _compute_angular_velocity(r, time_delta: float, guassian_filter=True):
# assume the second last dimension is the time axis
diff_quat_data = quat_identity_like(r)
diff_quat_data[..., :-1, :, :] = quat_mul_norm(
r[..., 1:, :, :], quat_inverse(r[..., :-1, :, :])
)
diff_angle, diff_axis = quat_angle_axis(diff_quat_data)
angular_velocity = diff_axis * diff_angle.unsqueeze(-1) / time_delta
angular_velocity = torch.from_numpy(
filters.gaussian_filter1d(
angular_velocity.numpy(), 2, axis=-3, mode="nearest"
),
)
return angular_velocity
def crop(self, start: int, end: int, fps: Optional[int] = None):
"""
Crop the motion along its last axis. This is equivalent to performing a slicing on the
object with [..., start: end: skip_every] where skip_every = old_fps / fps. Note that the
new fps provided must be a factor of the original fps.
:param start: the beginning frame index
:type start: int
:param end: the ending frame index
:type end: int
:param fps: number of frames per second in the output (if not given the original fps will be used)
:type fps: int, optional
:rtype: SkeletonMotion
"""
if fps is None:
new_fps = int(self.fps)
old_fps = int(self.fps)
else:
new_fps = int(fps)
old_fps = int(self.fps)
assert old_fps % fps == 0, (
"the resampling doesn't support fps with non-integer division "
"from the original fps: {} => {}".format(old_fps, fps)
)
skip_every = old_fps // new_fps
return SkeletonMotion.from_skeleton_state(
SkeletonState.from_rotation_and_root_translation(
skeleton_tree=self.skeleton_tree,
t=self.root_translation[start:end:skip_every],
r=self.local_rotation[start:end:skip_every],
is_local=True
),
fps=self.fps
)
def retarget_to(
self,
joint_mapping: Dict[str, str],
source_tpose_local_rotation,
source_tpose_root_translation: np.ndarray,
target_skeleton_tree: "SkeletonTree",
target_tpose_local_rotation,
target_tpose_root_translation: np.ndarray,
rotation_to_target_skeleton,
scale_to_target_skeleton: float,
z_up: bool = True,
) -> "SkeletonMotion":
"""
Same as the one in :class:`SkeletonState`. This method discards all velocity information before
retargeting and re-estimate the velocity after the retargeting. The same fps is used in the
new retargetted motion.
:param joint_mapping: a dictionary of that maps the joint node from the source skeleton to \
the target skeleton
:type joint_mapping: Dict[str, str]
:param source_tpose_local_rotation: the local rotation of the source skeleton
:type source_tpose_local_rotation: Tensor
:param source_tpose_root_translation: the root translation of the source tpose
:type source_tpose_root_translation: np.ndarray
:param target_skeleton_tree: the target skeleton tree
:type target_skeleton_tree: SkeletonTree
:param target_tpose_local_rotation: the local rotation of the target skeleton
:type target_tpose_local_rotation: Tensor
:param target_tpose_root_translation: the root translation of the target tpose
:type target_tpose_root_translation: Tensor
:param rotation_to_target_skeleton: the rotation that needs to be applied to the source\
skeleton to align with the target skeleton. Essentially the rotation is t_R_s, where t is\
the frame of reference of the target skeleton and s is the frame of reference of the source\
skeleton
:type rotation_to_target_skeleton: Tensor
:param scale_to_target_skeleton: the factor that needs to be multiplied from source\
skeleton to target skeleton (unit in distance). For example, to go from `cm` to `m`, the \
factor needs to be 0.01.
:type scale_to_target_skeleton: float
:rtype: SkeletonMotion
"""
return SkeletonMotion.from_skeleton_state(
super().retarget_to(
joint_mapping,
source_tpose_local_rotation,
source_tpose_root_translation,
target_skeleton_tree,
target_tpose_local_rotation,
target_tpose_root_translation,
rotation_to_target_skeleton,
scale_to_target_skeleton,
z_up,
),
self.fps,
)
def retarget_to_by_tpose(
self,
joint_mapping: Dict[str, str],
source_tpose: "SkeletonState",
target_tpose: "SkeletonState",
rotation_to_target_skeleton,
scale_to_target_skeleton: float,
z_up: bool = True,
) -> "SkeletonMotion":
"""
Same as the one in :class:`SkeletonState`. This method discards all velocity information before
retargeting and re-estimate the velocity after the retargeting. The same fps is used in the
new retargetted motion.
:param joint_mapping: a dictionary of that maps the joint node from the source skeleton to \
the target skeleton
:type joint_mapping: Dict[str, str]
:param source_tpose: t-pose of the source skeleton
:type source_tpose: SkeletonState
:param target_tpose: t-pose of the target skeleton
:type target_tpose: SkeletonState
:param rotation_to_target_skeleton: the rotation that needs to be applied to the source\
skeleton to align with the target skeleton. Essentially the rotation is t_R_s, where t is\
the frame of reference of the target skeleton and s is the frame of reference of the source\
skeleton
:type rotation_to_target_skeleton: Tensor
:param scale_to_target_skeleton: the factor that needs to be multiplied from source\
skeleton to target skeleton (unit in distance). For example, to go from `cm` to `m`, the \
factor needs to be 0.01.
:type scale_to_target_skeleton: float
:rtype: SkeletonMotion
"""
return self.retarget_to(
joint_mapping,
source_tpose.local_rotation,
source_tpose.root_translation,
target_tpose.skeleton_tree,
target_tpose.local_rotation,
target_tpose.root_translation,
rotation_to_target_skeleton,
scale_to_target_skeleton,
z_up,
)
| 57,916 | Python | 39.78662 | 217 | 0.585089 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/skeleton/backend/fbx/fbx_read_wrapper.py | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""
Script that reads in fbx files from python
This requires a configs file, which contains the command necessary to switch conda
environments to run the fbx reading script from python
"""
from ....core import logger
import inspect
import os
import numpy as np
from .fbx_backend import parse_fbx
def fbx_to_array(fbx_file_path, root_joint, fps):
"""
Reads an fbx file to an array.
:param fbx_file_path: str, file path to fbx
:return: tuple with joint_names, parents, transforms, frame time
"""
# Ensure the file path is valid
fbx_file_path = os.path.abspath(fbx_file_path)
assert os.path.exists(fbx_file_path)
# Parse FBX file
joint_names, parents, local_transforms, fbx_fps = parse_fbx(fbx_file_path, root_joint, fps)
return joint_names, parents, local_transforms, fbx_fps
| 1,253 | Python | 30.349999 | 95 | 0.746209 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/skeleton/backend/fbx/__init__.py | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited. | 423 | Python | 69.666655 | 76 | 0.817967 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/amp/poselib/poselib/skeleton/backend/fbx/fbx_backend.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.
"""
This script reads an fbx file and returns the joint names, parents, and transforms.
NOTE: It requires the Python FBX package to be installed.
"""
import sys
import numpy as np
try:
import fbx
import FbxCommon
except ImportError as e:
print("Error: FBX library failed to load - importing FBX data will not succeed. Message: {}".format(e))
print("FBX tools must be installed from https://help.autodesk.com/view/FBX/2020/ENU/?guid=FBX_Developer_Help_scripting_with_python_fbx_installing_python_fbx_html")
def fbx_to_npy(file_name_in, root_joint_name, fps):
"""
This function reads in an fbx file, and saves the relevant info to a numpy array
Fbx files have a series of animation curves, each of which has animations at different
times. This script assumes that for mocap data, there is only one animation curve that
contains all the joints. Otherwise it is unclear how to read in the data.
If this condition isn't met, then the method throws an error
:param file_name_in: str, file path in. Should be .fbx file
:return: nothing, it just writes a file.
"""
# Create the fbx scene object and load the .fbx file
fbx_sdk_manager, fbx_scene = FbxCommon.InitializeSdkObjects()
FbxCommon.LoadScene(fbx_sdk_manager, fbx_scene, file_name_in)
"""
To read in the animation, we must find the root node of the skeleton.
Unfortunately fbx files can have "scene parents" and other parts of the tree that are
not joints
As a crude fix, this reader just takes and finds the first thing which has an
animation curve attached
"""
search_root = (root_joint_name is None or root_joint_name == "")
# Get the root node of the skeleton, which is the child of the scene's root node
possible_root_nodes = [fbx_scene.GetRootNode()]
found_root_node = False
max_key_count = 0
root_joint = None
while len(possible_root_nodes) > 0:
joint = possible_root_nodes.pop(0)
if not search_root:
if joint.GetName() == root_joint_name:
root_joint = joint
try:
curve = _get_animation_curve(joint, fbx_scene)
except RuntimeError:
curve = None
if curve is not None:
key_count = curve.KeyGetCount()
if key_count > max_key_count:
found_root_node = True
max_key_count = key_count
root_curve = curve
if search_root and not root_joint:
root_joint = joint
for child_index in range(joint.GetChildCount()):
possible_root_nodes.append(joint.GetChild(child_index))
if not found_root_node:
raise RuntimeError("No root joint found!! Exiting")
joint_list, joint_names, parents = _get_skeleton(root_joint)
"""
Read in the transformation matrices of the animation, taking the scaling into account
"""
anim_range, frame_count, frame_rate = _get_frame_count(fbx_scene)
local_transforms = []
#for frame in range(frame_count):
time_sec = anim_range.GetStart().GetSecondDouble()
time_range_sec = anim_range.GetStop().GetSecondDouble() - time_sec
fbx_fps = frame_count / time_range_sec
if fps != 120:
fbx_fps = fps
print("FPS: ", fbx_fps)
while time_sec < anim_range.GetStop().GetSecondDouble():
fbx_time = fbx.FbxTime()
fbx_time.SetSecondDouble(time_sec)
fbx_time = fbx_time.GetFramedTime()
transforms_current_frame = []
# Fbx has a unique time object which you need
#fbx_time = root_curve.KeyGetTime(frame)
for joint in joint_list:
arr = np.array(_recursive_to_list(joint.EvaluateLocalTransform(fbx_time)))
scales = np.array(_recursive_to_list(joint.EvaluateLocalScaling(fbx_time)))
if not np.allclose(scales[0:3], scales[0]):
raise ValueError(
"Different X, Y and Z scaling. Unsure how this should be handled. "
"To solve this, look at this link and try to upgrade the script "
"http://help.autodesk.com/view/FBX/2017/ENU/?guid=__files_GUID_10CDD"
"63C_79C1_4F2D_BB28_AD2BE65A02ED_htm"
)
# Adjust the array for scaling
arr /= scales[0]
arr[3, 3] = 1.0
transforms_current_frame.append(arr)
local_transforms.append(transforms_current_frame)
time_sec += (1.0/fbx_fps)
local_transforms = np.array(local_transforms)
print("Frame Count: ", len(local_transforms))
return joint_names, parents, local_transforms, fbx_fps
def _get_frame_count(fbx_scene):
# Get the animation stacks and layers, in order to pull off animation curves later
num_anim_stacks = fbx_scene.GetSrcObjectCount(
FbxCommon.FbxCriteria.ObjectType(FbxCommon.FbxAnimStack.ClassId)
)
# if num_anim_stacks != 1:
# raise RuntimeError(
# "More than one animation stack was found. "
# "This script must be modified to handle this case. Exiting"
# )
if num_anim_stacks > 1:
index = 1
else:
index = 0
anim_stack = fbx_scene.GetSrcObject(
FbxCommon.FbxCriteria.ObjectType(FbxCommon.FbxAnimStack.ClassId), index
)
anim_range = anim_stack.GetLocalTimeSpan()
duration = anim_range.GetDuration()
fps = duration.GetFrameRate(duration.GetGlobalTimeMode())
frame_count = duration.GetFrameCount(True)
return anim_range, frame_count, fps
def _get_animation_curve(joint, fbx_scene):
# Get the animation stacks and layers, in order to pull off animation curves later
num_anim_stacks = fbx_scene.GetSrcObjectCount(
FbxCommon.FbxCriteria.ObjectType(FbxCommon.FbxAnimStack.ClassId)
)
# if num_anim_stacks != 1:
# raise RuntimeError(
# "More than one animation stack was found. "
# "This script must be modified to handle this case. Exiting"
# )
if num_anim_stacks > 1:
index = 1
else:
index = 0
anim_stack = fbx_scene.GetSrcObject(
FbxCommon.FbxCriteria.ObjectType(FbxCommon.FbxAnimStack.ClassId), index
)
num_anim_layers = anim_stack.GetSrcObjectCount(
FbxCommon.FbxCriteria.ObjectType(FbxCommon.FbxAnimLayer.ClassId)
)
if num_anim_layers != 1:
raise RuntimeError(
"More than one animation layer was found. "
"This script must be modified to handle this case. Exiting"
)
animation_layer = anim_stack.GetSrcObject(
FbxCommon.FbxCriteria.ObjectType(FbxCommon.FbxAnimLayer.ClassId), 0
)
def _check_longest_curve(curve, max_curve_key_count):
longest_curve = None
if curve and curve.KeyGetCount() > max_curve_key_count[0]:
max_curve_key_count[0] = curve.KeyGetCount()
return True
return False
max_curve_key_count = [0]
longest_curve = None
for c in ["X", "Y", "Z"]:
curve = joint.LclTranslation.GetCurve(
animation_layer, c
) # sample curve for translation
if _check_longest_curve(curve, max_curve_key_count):
longest_curve = curve
curve = joint.LclRotation.GetCurve(
animation_layer, "X"
)
if _check_longest_curve(curve, max_curve_key_count):
longest_curve = curve
return longest_curve
def _get_skeleton(root_joint):
# Do a depth first search of the skeleton to extract all the joints
joint_list = [root_joint]
joint_names = [root_joint.GetName()]
parents = [-1] # -1 means no parent
def append_children(joint, pos):
"""
Depth first search function
:param joint: joint item in the fbx
:param pos: position of current element (for parenting)
:return: Nothing
"""
for child_index in range(joint.GetChildCount()):
child = joint.GetChild(child_index)
joint_list.append(child)
joint_names.append(child.GetName())
parents.append(pos)
append_children(child, len(parents) - 1)
append_children(root_joint, 0)
return joint_list, joint_names, parents
def _recursive_to_list(array):
"""
Takes some iterable that might contain iterables and converts it to a list of lists
[of lists... etc]
Mainly used for converting the strange fbx wrappers for c++ arrays into python lists
:param array: array to be converted
:return: array converted to lists
"""
try:
return float(array)
except TypeError:
return [_recursive_to_list(a) for a in array]
def parse_fbx(file_name_in, root_joint_name, fps):
return fbx_to_npy(file_name_in, root_joint_name, fps)
| 10,372 | Python | 36.72 | 167 | 0.659661 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/industreal/industreal_task_pegs_insert.py | # Copyright (c) 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.
"""IndustReal: class for peg insertion task.
Inherits IndustReal pegs environment class and Factory abstract task class (not enforced).
Trains a peg insertion policy with Simulation-Aware Policy Update (SAPU), SDF-Based Reward, and Sampling-Based Curriculum (SBC).
Can be executed with python train.py task=IndustRealTaskPegsInsert.
"""
import hydra
import numpy as np
import omegaconf
import os
import torch
import warp as wp
from isaacgym import gymapi, gymtorch, torch_utils
from isaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from isaacgymenvs.tasks.factory.factory_schema_config_task import (
FactorySchemaConfigTask,
)
import isaacgymenvs.tasks.industreal.industreal_algo_utils as algo_utils
from isaacgymenvs.tasks.industreal.industreal_env_pegs import IndustRealEnvPegs
from isaacgymenvs.utils import torch_jit_utils
class IndustRealTaskPegsInsert(IndustRealEnvPegs, FactoryABCTask):
def __init__(
self,
cfg,
rl_device,
sim_device,
graphics_device_id,
headless,
virtual_screen_capture,
force_render,
):
"""Initialize instance variables. Initialize task superclass."""
self.cfg = cfg
self._get_task_yaml_params()
super().__init__(
cfg,
rl_device,
sim_device,
graphics_device_id,
headless,
virtual_screen_capture,
force_render,
)
self._acquire_task_tensors()
self.parse_controller_spec()
# Get Warp mesh objects for SAPU and SDF-based reward
wp.init()
self.wp_device = wp.get_preferred_device()
(
self.wp_plug_meshes,
self.wp_plug_meshes_sampled_points,
self.wp_socket_meshes,
) = algo_utils.load_asset_meshes_in_warp(
plug_files=self.plug_files,
socket_files=self.socket_files,
num_samples=self.cfg_task.rl.sdf_reward_num_samples,
device=self.wp_device,
)
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
ppo_path = os.path.join(
"train/IndustRealTaskPegsInsertPPO.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."""
self.identity_quat = (
torch.tensor([0.0, 0.0, 0.0, 1.0], device=self.device)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
# Compute pose of gripper goal and top of socket in socket frame
self.gripper_goal_pos_local = torch.tensor(
[
[
0.0,
0.0,
(self.cfg_task.env.socket_base_height + self.plug_grasp_offsets[i]),
]
for i in range(self.num_envs)
],
device=self.device,
)
self.gripper_goal_quat_local = self.identity_quat.clone()
self.socket_top_pos_local = torch.tensor(
[[0.0, 0.0, self.socket_heights[i]] for i in range(self.num_envs)],
device=self.device,
)
self.socket_quat_local = self.identity_quat.clone()
# Define keypoint tensors
self.keypoint_offsets = (
algo_utils.get_keypoint_offsets(self.cfg_task.rl.num_keypoints, self.device)
* self.cfg_task.rl.keypoint_scale
)
self.keypoints_plug = torch.zeros(
(self.num_envs, self.cfg_task.rl.num_keypoints, 3),
dtype=torch.float32,
device=self.device,
)
self.keypoints_socket = torch.zeros_like(
self.keypoints_plug, device=self.device
)
self.actions = torch.zeros(
(self.num_envs, self.cfg_task.env.numActions), device=self.device
)
self.curr_max_disp = self.cfg_task.rl.initial_max_disp
def _refresh_task_tensors(self):
"""Refresh tensors."""
# Compute pose of gripper goal and top of socket in global frame
self.gripper_goal_quat, self.gripper_goal_pos = torch_jit_utils.tf_combine(
self.socket_quat,
self.socket_pos,
self.gripper_goal_quat_local,
self.gripper_goal_pos_local,
)
self.socket_top_quat, self.socket_top_pos = torch_jit_utils.tf_combine(
self.socket_quat,
self.socket_pos,
self.socket_quat_local,
self.socket_top_pos_local,
)
# Add observation noise to socket pos
self.noisy_socket_pos = torch.zeros_like(
self.socket_pos, dtype=torch.float32, device=self.device
)
socket_obs_pos_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
)
socket_obs_pos_noise = socket_obs_pos_noise @ torch.diag(
torch.tensor(
self.cfg_task.env.socket_pos_obs_noise,
dtype=torch.float32,
device=self.device,
)
)
self.noisy_socket_pos[:, 0] = self.socket_pos[:, 0] + socket_obs_pos_noise[:, 0]
self.noisy_socket_pos[:, 1] = self.socket_pos[:, 1] + socket_obs_pos_noise[:, 1]
self.noisy_socket_pos[:, 2] = self.socket_pos[:, 2] + socket_obs_pos_noise[:, 2]
# Add observation noise to socket rot
socket_rot_euler = torch.zeros(
(self.num_envs, 3), dtype=torch.float32, device=self.device
)
socket_obs_rot_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
)
socket_obs_rot_noise = socket_obs_rot_noise @ torch.diag(
torch.tensor(
self.cfg_task.env.socket_rot_obs_noise,
dtype=torch.float32,
device=self.device,
)
)
socket_obs_rot_euler = socket_rot_euler + socket_obs_rot_noise
self.noisy_socket_quat = torch_utils.quat_from_euler_xyz(
socket_obs_rot_euler[:, 0],
socket_obs_rot_euler[:, 1],
socket_obs_rot_euler[:, 2],
)
# Compute observation noise on socket
(
self.noisy_gripper_goal_quat,
self.noisy_gripper_goal_pos,
) = torch_jit_utils.tf_combine(
self.noisy_socket_quat,
self.noisy_socket_pos,
self.gripper_goal_quat_local,
self.gripper_goal_pos_local,
)
# Compute pos of keypoints on plug and socket in world frame
for idx, keypoint_offset in enumerate(self.keypoint_offsets):
self.keypoints_plug[:, idx] = torch_jit_utils.tf_combine(
self.plug_quat,
self.plug_pos,
self.identity_quat,
keypoint_offset.repeat(self.num_envs, 1),
)[1]
self.keypoints_socket[:, idx] = torch_jit_utils.tf_combine(
self.socket_quat,
self.socket_pos,
self.identity_quat,
keypoint_offset.repeat(self.num_envs, 1),
)[1]
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]
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."""
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."""
delta_pos = self.gripper_goal_pos - self.fingertip_centered_pos
noisy_delta_pos = self.noisy_gripper_goal_pos - self.fingertip_centered_pos
# Define observations (for actor)
obs_tensors = [
self.arm_dof_pos, # 7
self.pose_world_to_robot_base(
self.fingertip_centered_pos, self.fingertip_centered_quat
)[
0
], # 3
self.pose_world_to_robot_base(
self.fingertip_centered_pos, self.fingertip_centered_quat
)[
1
], # 4
self.pose_world_to_robot_base(
self.noisy_gripper_goal_pos, self.noisy_gripper_goal_quat
)[
0
], # 3
self.pose_world_to_robot_base(
self.noisy_gripper_goal_pos, self.noisy_gripper_goal_quat
)[
1
], # 4
noisy_delta_pos,
] # 3
# Define state (for critic)
state_tensors = [
self.arm_dof_pos, # 7
self.arm_dof_vel, # 7
self.pose_world_to_robot_base(
self.fingertip_centered_pos, self.fingertip_centered_quat
)[
0
], # 3
self.pose_world_to_robot_base(
self.fingertip_centered_pos, self.fingertip_centered_quat
)[
1
], # 4
self.fingertip_centered_linvel, # 3
self.fingertip_centered_angvel, # 3
self.pose_world_to_robot_base(
self.gripper_goal_pos, self.gripper_goal_quat
)[
0
], # 3
self.pose_world_to_robot_base(
self.gripper_goal_pos, self.gripper_goal_quat
)[
1
], # 4
delta_pos, # 3
self.pose_world_to_robot_base(self.plug_pos, self.plug_quat)[0], # 3
self.pose_world_to_robot_base(self.plug_pos, self.plug_quat)[1], # 4
noisy_delta_pos - delta_pos,
] # 3
self.obs_buf = torch.cat(
obs_tensors, dim=-1
) # shape = (num_envs, num_observations)
self.states_buf = torch.cat(state_tensors, dim=-1)
return self.obs_buf
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."""
self.prev_rew_buf = self.rew_buf.clone()
# SDF-Based Reward: Compute reward based on SDF distance
sdf_reward = algo_utils.get_sdf_reward(
wp_plug_meshes_sampled_points=self.wp_plug_meshes_sampled_points,
asset_indices=self.asset_indices,
plug_pos=self.plug_pos,
plug_quat=self.plug_quat,
plug_goal_sdfs=self.plug_goal_sdfs,
wp_device=self.wp_device,
device=self.device,
)
# SDF-Based Reward: Apply reward
self.rew_buf[:] = self.cfg_task.rl.sdf_reward_scale * sdf_reward
# SDF-Based Reward: Log reward
self.extras["sdf_reward"] = torch.mean(self.rew_buf)
# SAPU: Compute reward scale based on interpenetration distance
low_interpen_envs, high_interpen_envs = [], []
(
low_interpen_envs,
high_interpen_envs,
sapu_reward_scale,
) = algo_utils.get_sapu_reward_scale(
asset_indices=self.asset_indices,
plug_pos=self.plug_pos,
plug_quat=self.plug_quat,
socket_pos=self.socket_pos,
socket_quat=self.socket_quat,
wp_plug_meshes_sampled_points=self.wp_plug_meshes_sampled_points,
wp_socket_meshes=self.wp_socket_meshes,
interpen_thresh=self.cfg_task.rl.interpen_thresh,
wp_device=self.wp_device,
device=self.device,
)
# SAPU: For envs with low interpenetration, apply reward scale ("weight" step)
self.rew_buf[low_interpen_envs] *= sapu_reward_scale
# SAPU: For envs with high interpenetration, do not update reward ("filter" step)
if len(high_interpen_envs) > 0:
self.rew_buf[high_interpen_envs] = self.prev_rew_buf[high_interpen_envs]
# SAPU: Log reward after scaling and adjustment from SAPU
self.extras["sapu_adjusted_reward"] = torch.mean(self.rew_buf)
is_last_step = self.progress_buf[0] == self.max_episode_length - 1
if is_last_step:
# Success bonus: Check which envs have plug engaged (partially inserted) or fully inserted
is_plug_engaged_w_socket = algo_utils.check_plug_engaged_w_socket(
plug_pos=self.plug_pos,
socket_top_pos=self.socket_top_pos,
keypoints_plug=self.keypoints_plug,
keypoints_socket=self.keypoints_socket,
cfg_task=self.cfg_task,
progress_buf=self.progress_buf,
)
is_plug_inserted_in_socket = algo_utils.check_plug_inserted_in_socket(
plug_pos=self.plug_pos,
socket_pos=self.socket_pos,
keypoints_plug=self.keypoints_plug,
keypoints_socket=self.keypoints_socket,
cfg_task=self.cfg_task,
progress_buf=self.progress_buf,
)
# Success bonus: Compute reward scale based on whether plug is engaged with socket, as well as closeness to full insertion
engagement_reward_scale = algo_utils.get_engagement_reward_scale(
plug_pos=self.plug_pos,
socket_pos=self.socket_pos,
is_plug_engaged_w_socket=is_plug_engaged_w_socket,
success_height_thresh=self.cfg_task.rl.success_height_thresh,
device=self.device,
)
# Success bonus: Apply reward with reward scale
self.rew_buf[:] += (
engagement_reward_scale * self.cfg_task.rl.engagement_bonus
)
# Success bonus: Log success rate, ignoring environments with large interpenetration
if len(high_interpen_envs) > 0:
is_plug_inserted_in_socket_low_interpen = is_plug_inserted_in_socket[
low_interpen_envs
]
self.extras["insertion_successes"] = torch.mean(
is_plug_inserted_in_socket_low_interpen.float()
)
else:
self.extras["insertion_successes"] = torch.mean(
is_plug_inserted_in_socket.float()
)
# SBC: Compute reward scale based on curriculum difficulty
sbc_rew_scale = algo_utils.get_curriculum_reward_scale(
cfg_task=self.cfg_task, curr_max_disp=self.curr_max_disp
)
# SBC: Apply reward scale (shrink negative rewards, grow positive rewards)
self.rew_buf[:] = torch.where(
self.rew_buf[:] < 0.0,
self.rew_buf[:] / sbc_rew_scale,
self.rew_buf[:] * sbc_rew_scale,
)
# SBC: Log current max downward displacement of plug at beginning of episode
self.extras["curr_max_disp"] = self.curr_max_disp
# SBC: Update curriculum difficulty based on success rate
self.curr_max_disp = algo_utils.get_new_max_disp(
curr_success=self.extras["insertion_successes"],
cfg_task=self.cfg_task,
curr_max_disp=self.curr_max_disp,
)
def _update_reset_buf(self):
"""Assign environments for reset if maximum 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 reset_idx(self, env_ids):
"""Reset specified environments."""
self._reset_franka()
# Close gripper onto plug
self.disable_gravity() # to prevent plug from falling
self._reset_object()
self._move_gripper_to_grasp_pose(
sim_steps=self.cfg_task.env.num_gripper_move_sim_steps
)
self.close_gripper(sim_steps=self.cfg_task.env.num_gripper_close_sim_steps)
self.enable_gravity()
# Get plug SDF in goal pose for SDF-based reward
self.plug_goal_sdfs = algo_utils.get_plug_goal_sdfs(
wp_plug_meshes=self.wp_plug_meshes,
asset_indices=self.asset_indices,
socket_pos=self.socket_pos,
socket_quat=self.socket_quat,
wp_device=self.wp_device,
)
self._reset_buffers()
def _reset_franka(self):
"""Reset DOF states, DOF torques, and DOF targets of Franka."""
# Randomize DOF pos
self.dof_pos[:] = 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
) # shape = (num_envs, num_dofs)
# Stabilize Franka
self.dof_vel[:, :] = 0.0 # shape = (num_envs, num_dofs)
self.dof_torque[:, :] = 0.0
self.ctrl_target_dof_pos = self.dof_pos.clone()
self.ctrl_target_fingertip_centered_pos = self.fingertip_centered_pos.clone()
self.ctrl_target_fingertip_centered_quat = self.fingertip_centered_quat.clone()
# Set DOF state
franka_actor_ids_sim = self.franka_actor_ids_sim.clone().to(dtype=torch.int32)
self.gym.set_dof_state_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(franka_actor_ids_sim),
len(franka_actor_ids_sim),
)
# Set DOF torque
self.gym.set_dof_actuation_force_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.dof_torque),
gymtorch.unwrap_tensor(franka_actor_ids_sim),
len(franka_actor_ids_sim),
)
# Simulate one step to apply changes
self.simulate_and_refresh()
def _reset_object(self):
"""Reset root state of plug and socket."""
self._reset_socket()
self._reset_plug(before_move_to_grasp=True)
def _reset_socket(self):
"""Reset root state of socket."""
# Randomize socket pos
socket_noise_xy = 2 * (
torch.rand((self.num_envs, 2), dtype=torch.float32, device=self.device)
- 0.5
)
socket_noise_xy = socket_noise_xy @ torch.diag(
torch.tensor(
self.cfg_task.randomize.socket_pos_xy_noise,
dtype=torch.float32,
device=self.device,
)
)
socket_noise_z = torch.zeros(
(self.num_envs), dtype=torch.float32, device=self.device
)
socket_noise_z_mag = (
self.cfg_task.randomize.socket_pos_z_noise_bounds[1]
- self.cfg_task.randomize.socket_pos_z_noise_bounds[0]
)
socket_noise_z = (
socket_noise_z_mag
* torch.rand((self.num_envs), dtype=torch.float32, device=self.device)
+ self.cfg_task.randomize.socket_pos_z_noise_bounds[0]
)
self.socket_pos[:, 0] = (
self.robot_base_pos[:, 0]
+ self.cfg_task.randomize.socket_pos_xy_initial[0]
+ socket_noise_xy[:, 0]
)
self.socket_pos[:, 1] = (
self.robot_base_pos[:, 1]
+ self.cfg_task.randomize.socket_pos_xy_initial[1]
+ socket_noise_xy[:, 1]
)
self.socket_pos[:, 2] = self.cfg_base.env.table_height + socket_noise_z
# Randomize socket rot
socket_rot_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
)
socket_rot_noise = socket_rot_noise @ torch.diag(
torch.tensor(
self.cfg_task.randomize.socket_rot_noise,
dtype=torch.float32,
device=self.device,
)
)
socket_rot_euler = (
torch.zeros((self.num_envs, 3), dtype=torch.float32, device=self.device)
+ socket_rot_noise
)
socket_rot_quat = torch_utils.quat_from_euler_xyz(
socket_rot_euler[:, 0], socket_rot_euler[:, 1], socket_rot_euler[:, 2]
)
self.socket_quat[:, :] = socket_rot_quat.clone()
# Stabilize socket
self.socket_linvel[:, :] = 0.0
self.socket_angvel[:, :] = 0.0
# Set socket root state
socket_actor_ids_sim = self.socket_actor_ids_sim.clone().to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.root_state),
gymtorch.unwrap_tensor(socket_actor_ids_sim),
len(socket_actor_ids_sim),
)
# Simulate one step to apply changes
self.simulate_and_refresh()
def _reset_plug(self, before_move_to_grasp):
"""Reset root state of plug."""
if before_move_to_grasp:
# Generate randomized downward displacement based on curriculum
curr_curriculum_disp_range = (
self.curr_max_disp - self.cfg_task.rl.curriculum_height_bound[0]
)
self.curriculum_disp = self.cfg_task.rl.curriculum_height_bound[
0
] + curr_curriculum_disp_range * (
torch.rand((self.num_envs,), dtype=torch.float32, device=self.device)
)
# Generate plug pos noise
self.plug_pos_xy_noise = 2 * (
torch.rand((self.num_envs, 2), dtype=torch.float32, device=self.device)
- 0.5
)
self.plug_pos_xy_noise = self.plug_pos_xy_noise @ torch.diag(
torch.tensor(
self.cfg_task.randomize.plug_pos_xy_noise,
dtype=torch.float32,
device=self.device,
)
)
# Set plug pos to assembled state, but offset plug Z-coordinate by height of socket,
# minus curriculum displacement
self.plug_pos[:, :] = self.socket_pos.clone()
self.plug_pos[:, 2] += self.socket_heights
self.plug_pos[:, 2] -= self.curriculum_disp
# Apply XY noise to plugs not partially inserted into sockets
socket_top_height = self.socket_pos[:, 2] + self.socket_heights
plug_partial_insert_idx = np.argwhere(
self.plug_pos[:, 2].cpu().numpy() > socket_top_height.cpu().numpy()
).squeeze()
self.plug_pos[plug_partial_insert_idx, :2] += self.plug_pos_xy_noise[
plug_partial_insert_idx
]
self.plug_quat[:, :] = self.identity_quat.clone()
# Stabilize plug
self.plug_linvel[:, :] = 0.0
self.plug_angvel[:, :] = 0.0
# Set plug root state
plug_actor_ids_sim = self.plug_actor_ids_sim.clone().to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.root_state),
gymtorch.unwrap_tensor(plug_actor_ids_sim),
len(plug_actor_ids_sim),
)
# Simulate one step to apply changes
self.simulate_and_refresh()
def _reset_buffers(self):
"""Reset buffers."""
self.reset_buf[:] = 0
self.progress_buf[:] = 0
def _set_viewer_params(self):
"""Set viewer parameters."""
cam_pos = gymapi.Vec3(-1.0, -1.0, 2.0)
cam_target = gymapi.Vec3(0.0, 0.0, 1.5)
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_centered_pos = (
self.fingertip_centered_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_centered_quat = torch_utils.quat_mul(
rot_actions_quat, self.fingertip_centered_quat
)
self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos
self.generate_ctrl_signals()
def _move_gripper_to_grasp_pose(self, sim_steps):
"""Define grasp pose for plug and move gripper to pose."""
# Set target_pos
self.ctrl_target_fingertip_midpoint_pos = self.plug_pos.clone()
self.ctrl_target_fingertip_midpoint_pos[:, 2] += self.plug_grasp_offsets
# Set target rot
ctrl_target_fingertip_centered_euler = (
torch.tensor(
self.cfg_task.randomize.fingertip_centered_rot_initial,
device=self.device,
)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz(
ctrl_target_fingertip_centered_euler[:, 0],
ctrl_target_fingertip_centered_euler[:, 1],
ctrl_target_fingertip_centered_euler[:, 2],
)
self.move_gripper_to_target_pose(
gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
sim_steps=sim_steps,
)
# Reset plug in case it is knocked away by gripper movement
self._reset_plug(before_move_to_grasp=False)
| 29,491 | Python | 36.237374 | 134 | 0.572514 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/industreal/industreal_task_gears_insert.py | # Copyright (c) 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.
"""IndustReal: class for gear insertion task.
Inherits IndustReal gears environment class and Factory abstract task class (not enforced).
Trains a gear insertion policy with Simulation-Aware Policy Update (SAPU), SDF-Based Reward, and Sampling-Based Curriculum (SBC).
Can be executed with python train.py task=IndustRealTaskGearsInsert.
"""
import hydra
import numpy as np
import omegaconf
import os
import torch
import warp as wp
from isaacgym import gymapi, gymtorch, torch_utils
from isaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from isaacgymenvs.tasks.factory.factory_schema_config_task import (
FactorySchemaConfigTask,
)
import isaacgymenvs.tasks.industreal.industreal_algo_utils as algo_utils
from isaacgymenvs.tasks.industreal.industreal_env_gears import IndustRealEnvGears
from isaacgymenvs.utils import torch_jit_utils
class IndustRealTaskGearsInsert(IndustRealEnvGears, FactoryABCTask):
def __init__(
self,
cfg,
rl_device,
sim_device,
graphics_device_id,
headless,
virtual_screen_capture,
force_render,
):
"""Initialize instance variables. Initialize task superclass."""
self.cfg = cfg
self._get_task_yaml_params()
super().__init__(
cfg,
rl_device,
sim_device,
graphics_device_id,
headless,
virtual_screen_capture,
force_render,
)
self._acquire_task_tensors()
self.parse_controller_spec()
# Get Warp mesh objects for SAPU and SDF-based reward
wp.init()
self.wp_device = wp.get_preferred_device()
(
self.wp_gear_meshes,
self.wp_gear_meshes_sampled_points,
self.wp_shaft_meshes,
) = algo_utils.load_asset_meshes_in_warp(
plug_files=self.gear_files,
socket_files=self.shaft_files,
num_samples=self.cfg_task.rl.sdf_reward_num_samples,
device=self.wp_device,
)
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
ppo_path = os.path.join(
"train/IndustRealTaskGearsInsertPPO.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."""
self.identity_quat = (
torch.tensor([0.0, 0.0, 0.0, 1.0], device=self.device)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
# Compute pose of gripper goal in gear base frame
self.gripper_goal_pos_local = (
torch.tensor(
[
0.0,
0.0,
self.asset_info_gears.base.height
+ self.asset_info_gears.gears.grasp_offset,
]
)
.to(self.device)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
self.gripper_goal_quat_local = self.identity_quat.clone()
# Define keypoint tensors
self.keypoint_offsets = (
algo_utils.get_keypoint_offsets(self.cfg_task.rl.num_keypoints, self.device)
* self.cfg_task.rl.keypoint_scale
)
self.keypoints_gear = torch.zeros(
(self.num_envs, self.cfg_task.rl.num_keypoints, 3),
dtype=torch.float32,
device=self.device,
)
self.keypoints_shaft = torch.zeros_like(self.keypoints_gear, device=self.device)
self.actions = torch.zeros(
(self.num_envs, self.cfg_task.env.numActions), device=self.device
)
self.curr_max_disp = self.cfg_task.rl.initial_max_disp
def _refresh_task_tensors(self):
"""Refresh tensors."""
# From CAD, gear origin is offset from gear; reverse offset to get pos of gear and base of corresponding shaft
self.gear_medium_pos_center = self.gear_medium_pos - torch.tensor(
[self.cfg_task.env.gear_medium_pos_offset[1], 0.0, 0.0], device=self.device
)
self.shaft_pos = self.base_pos - torch.tensor(
[self.cfg_task.env.gear_medium_pos_offset[1], 0.0, 0.0], device=self.device
)
# Compute pose of gripper goal in global frame
self.gripper_goal_quat, self.gripper_goal_pos = torch_jit_utils.tf_combine(
self.base_quat,
self.shaft_pos,
self.gripper_goal_quat_local,
self.gripper_goal_pos_local,
)
# Add observation noise to gear base pos
self.noisy_base_pos = torch.zeros_like(
self.base_pos, dtype=torch.float32, device=self.device
)
base_obs_pos_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
)
base_obs_pos_noise = base_obs_pos_noise @ torch.diag(
torch.tensor(
self.cfg_task.env.base_pos_obs_noise,
dtype=torch.float32,
device=self.device,
)
)
self.noisy_base_pos[:, 0] = self.base_pos[:, 0] + base_obs_pos_noise[:, 0]
self.noisy_base_pos[:, 1] = self.base_pos[:, 1] + base_obs_pos_noise[:, 1]
self.noisy_base_pos[:, 2] = self.base_pos[:, 2] + base_obs_pos_noise[:, 2]
# Add observation noise to gear base rot
base_rot_euler = torch.zeros(
(self.num_envs, 3), dtype=torch.float32, device=self.device
)
base_obs_rot_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
)
base_obs_rot_noise = base_obs_rot_noise @ torch.diag(
torch.tensor(
self.cfg_task.env.base_rot_obs_noise,
dtype=torch.float32,
device=self.device,
)
)
base_obs_rot_euler = base_rot_euler + base_obs_rot_noise
self.noisy_base_quat = torch_utils.quat_from_euler_xyz(
base_obs_rot_euler[:, 0], base_obs_rot_euler[:, 1], base_obs_rot_euler[:, 2]
)
# Compute observation noise on gear base
(
self.noisy_gripper_goal_quat,
self.noisy_gripper_goal_pos,
) = torch_jit_utils.tf_combine(
self.noisy_base_quat,
self.noisy_base_pos,
self.gripper_goal_quat_local,
self.gripper_goal_pos_local,
)
# Compute pos of keypoints on gear and shaft in world frame
for idx, keypoint_offset in enumerate(self.keypoint_offsets):
self.keypoints_gear[:, idx] = torch_jit_utils.tf_combine(
self.gear_medium_quat,
self.gear_medium_pos_center,
self.identity_quat,
keypoint_offset.repeat(self.num_envs, 1),
)[1]
self.keypoints_shaft[:, idx] = torch_jit_utils.tf_combine(
self.base_quat,
self.shaft_pos,
self.identity_quat,
keypoint_offset.repeat(self.num_envs, 1),
)[1]
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]
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."""
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."""
delta_pos = self.gripper_goal_pos - self.fingertip_centered_pos
noisy_delta_pos = self.noisy_gripper_goal_pos - self.fingertip_centered_pos
# Define observations (for actor)
obs_tensors = [
self.arm_dof_pos, # 7
self.pose_world_to_robot_base(
self.fingertip_centered_pos, self.fingertip_centered_quat
)[
0
], # 3
self.pose_world_to_robot_base(
self.fingertip_centered_pos, self.fingertip_centered_quat
)[
1
], # 4
self.pose_world_to_robot_base(
self.noisy_gripper_goal_pos, self.noisy_gripper_goal_quat
)[
0
], # 3
self.pose_world_to_robot_base(
self.noisy_gripper_goal_pos, self.noisy_gripper_goal_quat
)[
1
], # 4
noisy_delta_pos,
]
# Define state (for critic)
state_tensors = [
self.arm_dof_pos, # 7
self.arm_dof_vel, # 7
self.pose_world_to_robot_base(
self.fingertip_centered_pos, self.fingertip_centered_quat
)[
0
], # 3
self.pose_world_to_robot_base(
self.fingertip_centered_pos, self.fingertip_centered_quat
)[
1
], # 4
self.fingertip_centered_linvel, # 3
self.fingertip_centered_angvel, # 3
self.pose_world_to_robot_base(
self.gripper_goal_pos, self.gripper_goal_quat
)[
0
], # 3
self.pose_world_to_robot_base(
self.gripper_goal_pos, self.gripper_goal_quat
)[
1
], # 4
delta_pos, # 3
self.pose_world_to_robot_base(self.gear_medium_pos, self.gear_medium_quat)[
0
], # 3
self.pose_world_to_robot_base(self.gear_medium_pos, self.gear_medium_quat)[
1
], # 4
noisy_delta_pos - delta_pos,
] # 3
self.obs_buf = torch.cat(
obs_tensors, dim=-1
) # shape = (num_envs, num_observations)
self.states_buf = torch.cat(state_tensors, dim=-1)
return self.obs_buf
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."""
self.prev_rew_buf = self.rew_buf.clone()
# SDF-Based Reward: Compute reward based on SDF distance
sdf_reward = algo_utils.get_sdf_reward(
wp_plug_meshes_sampled_points=self.wp_gear_meshes_sampled_points,
asset_indices=self.asset_indices,
plug_pos=self.gear_medium_pos,
plug_quat=self.gear_medium_quat,
plug_goal_sdfs=self.gear_goal_sdfs,
wp_device=self.wp_device,
device=self.device,
)
# SDF-Based Reward: Apply reward
self.rew_buf[:] = self.cfg_task.rl.sdf_reward_scale * sdf_reward
self.extras["sdf_reward"] = torch.mean(self.rew_buf)
# SAPU: Compute reward scale based on interpenetration distance
low_interpen_envs, high_interpen_envs = [], []
(
low_interpen_envs,
high_interpen_envs,
sapu_reward_scale,
) = algo_utils.get_sapu_reward_scale(
asset_indices=self.asset_indices,
plug_pos=self.gear_medium_pos,
plug_quat=self.gear_medium_quat,
socket_pos=self.base_pos,
socket_quat=self.base_quat,
wp_plug_meshes_sampled_points=self.wp_gear_meshes_sampled_points,
wp_socket_meshes=self.wp_shaft_meshes,
interpen_thresh=self.cfg_task.rl.interpen_thresh,
wp_device=self.wp_device,
device=self.device,
)
# SAPU: For envs with low interpenetration, apply reward scale ("weight" step)
self.rew_buf[low_interpen_envs] *= sapu_reward_scale
# SAPU: For envs with high interpenetration, do not update reward ("filter" step)
if len(high_interpen_envs) > 0:
self.rew_buf[high_interpen_envs] = self.prev_rew_buf[high_interpen_envs]
self.extras["sapu_adjusted_reward"] = torch.mean(self.rew_buf)
is_last_step = self.progress_buf[0] == self.max_episode_length - 1
if is_last_step:
# Check which envs have gear engaged (partially inserted) or fully inserted
is_gear_engaged_w_shaft = algo_utils.check_gear_engaged_w_shaft(
gear_pos=self.gear_medium_pos,
shaft_pos=self.shaft_pos,
keypoints_gear=self.keypoints_gear,
keypoints_shaft=self.keypoints_shaft,
asset_info_gears=self.asset_info_gears,
cfg_task=self.cfg_task,
progress_buf=self.progress_buf,
)
is_gear_inserted_on_shaft = algo_utils.check_gear_inserted_on_shaft(
gear_pos=self.gear_medium_pos,
shaft_pos=self.shaft_pos,
keypoints_gear=self.keypoints_gear,
keypoints_shaft=self.keypoints_shaft,
cfg_task=self.cfg_task,
progress_buf=self.progress_buf,
)
# Success bonus: Compute reward scale based on whether gear is engaged with shaft, as well as closeness to full insertion
engagement_reward_scale = algo_utils.get_engagement_reward_scale(
plug_pos=self.gear_medium_pos,
socket_pos=self.base_pos,
is_plug_engaged_w_socket=is_gear_engaged_w_shaft,
success_height_thresh=self.cfg_task.rl.success_height_thresh,
device=self.device,
)
# Success bonus: Apply reward with reward scale
self.rew_buf[:] += (
engagement_reward_scale * self.cfg_task.rl.engagement_bonus
)
# Success bonus: Log success rate, ignoring environments with large interpenetration
if len(high_interpen_envs) > 0:
is_gear_inserted_on_shaft_low_interpen = is_gear_inserted_on_shaft[
low_interpen_envs
]
self.extras["insertion_successes"] = torch.mean(
is_gear_inserted_on_shaft_low_interpen.float()
)
else:
self.extras["insertion_successes"] = torch.mean(
is_gear_inserted_on_shaft.float()
)
# SBC: Compute reward scale based on curriculum difficulty
sbc_rew_scale = algo_utils.get_curriculum_reward_scale(
cfg_task=self.cfg_task, curr_max_disp=self.curr_max_disp
)
# SBC: Apply reward scale (shrink negative rewards, grow positive rewards)
self.rew_buf[:] = torch.where(
self.rew_buf[:] < 0.0,
self.rew_buf[:] / sbc_rew_scale,
self.rew_buf[:] * sbc_rew_scale,
)
# SBC: Log current max downward displacement of gear at beginning of episode
self.extras["curr_max_disp"] = self.curr_max_disp
# SBC: Update curriculum difficulty based on success rate
self.curr_max_disp = algo_utils.get_new_max_disp(
curr_success=self.extras["insertion_successes"],
cfg_task=self.cfg_task,
curr_max_disp=self.curr_max_disp,
)
def _update_reset_buf(self):
"""Assign environments for reset if maximum 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 reset_idx(self, env_ids):
"""Reset specified environments."""
self._reset_franka()
# Close gripper onto gear
self.disable_gravity() # to prevent gear from falling
self._reset_object()
self._move_gripper_to_grasp_pose(
sim_steps=self.cfg_task.env.num_gripper_move_sim_steps
)
self.close_gripper(sim_steps=self.cfg_task.env.num_gripper_close_sim_steps)
self.enable_gravity()
# Get gear SDF in goal pose for SDF-based reward
self.gear_goal_sdfs = algo_utils.get_plug_goal_sdfs(
wp_plug_meshes=self.wp_gear_meshes,
asset_indices=self.asset_indices,
socket_pos=self.base_pos,
socket_quat=self.base_quat,
wp_device=self.wp_device,
)
self._reset_buffers()
def _reset_franka(self):
"""Reset DOF states, DOF torques, and DOF targets of Franka."""
self.dof_pos[:] = 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
) # shape = (num_envs, num_dofs)
# Stabilize Franka
self.dof_vel[:, :] = 0.0 # shape = (num_envs, num_dofs)
self.dof_torque[:, :] = 0.0
self.ctrl_target_dof_pos = self.dof_pos.clone()
self.ctrl_target_fingertip_centered_pos = self.fingertip_centered_pos.clone()
self.ctrl_target_fingertip_centered_quat = self.fingertip_centered_quat.clone()
# Set DOF state
franka_actor_ids_sim = self.franka_actor_ids_sim.clone().to(dtype=torch.int32)
self.gym.set_dof_state_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(franka_actor_ids_sim),
len(franka_actor_ids_sim),
)
# Set DOF torque
self.gym.set_dof_actuation_force_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(torch.zeros_like(self.dof_torque)),
gymtorch.unwrap_tensor(franka_actor_ids_sim),
len(franka_actor_ids_sim),
)
# Simulate one step to apply changes
self.simulate_and_refresh()
def _reset_object(self):
"""Reset root state of gears and gear base."""
self._reset_base()
self._reset_small_large_gears()
self._reset_medium_gear(before_move_to_grasp=True)
def _reset_base(self):
"""Reset root state of gear base."""
# Randomize gear base pos
base_noise_xy = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
)
base_noise_xy = base_noise_xy @ torch.diag(
torch.tensor(
self.cfg_task.randomize.base_pos_xy_noise,
dtype=torch.float32,
device=self.device,
)
)
base_noise_z = torch.zeros(
(self.num_envs), dtype=torch.float32, device=self.device
)
base_noise_z_mag = (
self.cfg_task.randomize.base_pos_z_noise_bounds[1]
- self.cfg_task.randomize.base_pos_z_noise_bounds[0]
)
base_noise_z = base_noise_z_mag * torch.rand(
(self.num_envs), dtype=torch.float32, device=self.device
)
self.base_pos[:, 0] = (
self.robot_base_pos[:, 0]
+ self.cfg_task.randomize.base_pos_xy_initial[0]
+ base_noise_xy[:, 0]
)
self.base_pos[:, 1] = (
self.robot_base_pos[:, 1]
+ self.cfg_task.randomize.base_pos_xy_initial[1]
+ base_noise_xy[:, 1]
)
self.base_pos[:, 2] = self.cfg_base.env.table_height + base_noise_z
# Set gear base rot
self.base_quat[:] = self.identity_quat
# Stabilize gear base
self.base_linvel[:, :] = 0.0
self.base_angvel[:, :] = 0.0
# Set gear base root state
base_actor_ids_sim = self.base_actor_ids_sim.clone().to(dtype=torch.int32)
self.gym.set_actor_root_state_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.root_state),
gymtorch.unwrap_tensor(base_actor_ids_sim),
len(base_actor_ids_sim),
)
# Simulate one step to apply changes
self.simulate_and_refresh()
def _reset_small_large_gears(self):
"""Reset root state of small and large gears."""
# Set small and large gear pos to be pos in assembled state, plus vertical offset to prevent initial collision
self.gear_small_pos[:, :] = self.base_pos + torch.tensor(
[0.0, 0.0, 0.002], device=self.device
)
self.gear_large_pos[:, :] = self.base_pos + torch.tensor(
[0.0, 0.0, 0.002], device=self.device
)
# Set small and large gear rot
self.gear_small_quat[:] = self.identity_quat
self.gear_large_quat[:] = self.identity_quat
# Stabilize small and large gears
self.gear_small_linvel[:, :] = 0.0
self.gear_large_linvel[:, :] = 0.0
self.gear_small_angvel[:, :] = 0.0
self.gear_large_angvel[:, :] = 0.0
# Set small and large gear root state
gears_small_large_actor_ids_sim = torch.cat(
(self.gear_small_actor_ids_sim, self.gear_large_actor_ids_sim), dim=0
).to(torch.int32)
self.gym.set_actor_root_state_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.root_state),
gymtorch.unwrap_tensor(gears_small_large_actor_ids_sim),
len(gears_small_large_actor_ids_sim),
)
# Simulate one step to apply changes
self.simulate_and_refresh()
def _reset_medium_gear(self, before_move_to_grasp):
"""Reset root state of medium gear."""
if before_move_to_grasp:
# Generate randomized downward displacement based on curriculum
curr_curriculum_disp_range = (
self.curr_max_disp - self.cfg_task.rl.curriculum_height_bound[0]
)
self.curriculum_disp = self.cfg_task.rl.curriculum_height_bound[
0
] + curr_curriculum_disp_range * (
torch.rand((self.num_envs,), dtype=torch.float32, device=self.device)
)
# Generate gear pos noise
self.gear_medium_pos_xyz_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
)
self.gear_medium_pos_xyz_noise = (
self.gear_medium_pos_xyz_noise
@ torch.diag(
torch.tensor(
self.cfg_task.randomize.gear_pos_xyz_noise,
dtype=torch.float32,
device=self.device,
)
)
)
# Set medium gear pos to assembled state, but offset gear Z-coordinate by height of gear,
# minus curriculum displacement
self.gear_medium_pos[:, :] = self.base_pos.clone()
self.gear_medium_pos[:, 2] += self.asset_info_gears.shafts.height
self.gear_medium_pos[:, 2] -= self.curriculum_disp
# Apply XY noise to gears not partially inserted onto gear shafts
gear_base_top_height = (
self.base_pos[:, 2]
+ self.asset_info_gears.base.height
+ self.asset_info_gears.shafts.height
)
gear_partial_insert_idx = np.argwhere(
self.gear_medium_pos[:, 2].cpu().numpy()
> gear_base_top_height.cpu().numpy()
).squeeze()
self.gear_medium_pos[
gear_partial_insert_idx, :2
] += self.gear_medium_pos_xyz_noise[gear_partial_insert_idx, :2]
self.gear_medium_quat[:, :] = self.identity_quat.clone()
# Stabilize plug
self.gear_medium_linvel[:, :] = 0.0
self.gear_medium_angvel[:, :] = 0.0
# Set medium gear root state
gear_medium_actor_ids_sim = self.gear_medium_actor_ids_sim.clone().to(
dtype=torch.int32
)
self.gym.set_actor_root_state_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.root_state),
gymtorch.unwrap_tensor(gear_medium_actor_ids_sim),
len(gear_medium_actor_ids_sim),
)
# Simulate one step to apply changes
self.simulate_and_refresh()
def _reset_buffers(self):
"""Reset buffers."""
self.reset_buf[:] = 0
self.progress_buf[:] = 0
def _set_viewer_params(self):
"""Set viewer parameters."""
cam_pos = gymapi.Vec3(-1.0, -1.0, 2.0)
cam_target = gymapi.Vec3(0.0, 0.0, 1.5)
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_centered_pos = (
self.fingertip_centered_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_centered_quat = torch_utils.quat_mul(
rot_actions_quat, self.fingertip_centered_quat
)
self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos
self.generate_ctrl_signals()
def _move_gripper_to_grasp_pose(self, sim_steps):
"""Define grasp pose for medium gear and move gripper to pose."""
# Set target pos
self.ctrl_target_fingertip_midpoint_pos = self.gear_medium_pos_center.clone()
self.ctrl_target_fingertip_midpoint_pos[
:, 2
] += self.asset_info_gears.gears.grasp_offset
# Set target rot
ctrl_target_fingertip_centered_euler = (
torch.tensor(
self.cfg_task.randomize.fingertip_centered_rot_initial,
device=self.device,
)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz(
ctrl_target_fingertip_centered_euler[:, 0],
ctrl_target_fingertip_centered_euler[:, 1],
ctrl_target_fingertip_centered_euler[:, 2],
)
self.move_gripper_to_target_pose(
gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
sim_steps=sim_steps,
)
# Reset medium gear in case it is knocked away by gripper movement
self._reset_medium_gear(before_move_to_grasp=False)
| 30,487 | Python | 36.408589 | 133 | 0.572572 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/industreal/industreal_base.py | # Copyright (c) 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.
"""IndustReal: base class.
Inherits Factory base class and Factory abstract base class. Inherited by IndustReal environment classes. Not directly executed.
Configuration defined in IndustRealBase.yaml. Asset info defined in industreal_asset_info_franka_table.yaml.
"""
import hydra
import math
import os
import torch
from isaacgym import gymapi, gymtorch, torch_utils
from isaacgymenvs.tasks.factory.factory_base import FactoryBase
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 IndustRealBase(FactoryBase, 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/IndustRealBase.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/industreal/yaml/industreal_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"]["industreal"][
"yaml"
] # strip superfluous nesting
def import_franka_assets(self):
"""Set Franka and table asset options. Import assets."""
urdf_root = os.path.join(
os.path.dirname(__file__), "..", "..", "..", "assets", "industreal", "urdf"
)
franka_file = "industreal_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 = 1.0 # default = 1000.0
franka_options.angular_damping = 0.5 # default = 0.5
franka_options.max_angular_velocity = 2 * math.pi # 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_dof_vel = self.dof_vel[:, 0:7]
self.arm_mass_matrix = self.mass_matrix[
:, 0:7, 0:7
] # for Franka arm (not gripper)
self.robot_base_pos = self.body_pos[:, self.robot_base_body_id_env, 0:3]
self.robot_base_quat = self.body_quat[:, self.robot_base_body_id_env, 0:4]
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_actor - 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_actor - 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_actor - 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_actor - 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_centered_pos = torch.zeros(
(self.num_envs, 3), device=self.device
)
self.ctrl_target_fingertip_centered_quat = torch.zeros(
(self.num_envs, 4), 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 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_centered_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_centered_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_centered_pos,
fingertip_midpoint_quat=self.fingertip_centered_quat,
jacobian=self.fingertip_midpoint_jacobian_tf,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_centered_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_centered_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_centered_pos,
fingertip_midpoint_quat=self.fingertip_centered_quat,
fingertip_midpoint_linvel=self.fingertip_centered_linvel,
fingertip_midpoint_angvel=self.fingertip_centered_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_centered_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_centered_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 simulate_and_refresh(self):
"""Simulate one step, refresh tensors, and render results."""
self.gym.simulate(self.sim)
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
self.render()
def enable_gravity(self):
"""Enable gravity."""
sim_params = self.gym.get_sim_params(self.sim)
sim_params.gravity = gymapi.Vec3(*self.cfg_base.sim.gravity)
self.gym.set_sim_params(self.sim, sim_params)
def open_gripper(self, sim_steps):
"""Open gripper using controller. Called outside RL loop (i.e., after last step of episode)."""
self.move_gripper_to_target_pose(gripper_dof_pos=0.1, sim_steps=sim_steps)
def close_gripper(self, sim_steps):
"""Fully close gripper using controller. Called outside RL loop (i.e., after last step of episode)."""
self.move_gripper_to_target_pose(gripper_dof_pos=0.0, sim_steps=sim_steps)
def move_gripper_to_target_pose(self, gripper_dof_pos, sim_steps):
"""Move gripper to control target pose."""
for _ in range(sim_steps):
# NOTE: midpoint is calculated based on the midpoint between the actual gripper finger pos,
# and centered is calculated with the assumption that the gripper fingers are perfectly mirrored.
# Here we **intentionally** use *_centered_* pos and quat instead of *_midpoint_*,
# since the fingertips are exactly mirrored in the real world.
pos_error, axis_angle_error = fc.get_pose_error(
fingertip_midpoint_pos=self.fingertip_centered_pos,
fingertip_midpoint_quat=self.fingertip_centered_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=gripper_dof_pos,
do_scale=False,
)
# Simulate one step
self.simulate_and_refresh()
# Stabilize Franka
self.dof_vel[:, :] = 0.0
self.dof_torque[:, :] = 0.0
self.ctrl_target_fingertip_centered_pos = self.fingertip_centered_pos.clone()
self.ctrl_target_fingertip_centered_quat = self.fingertip_centered_quat.clone()
# Set DOF state
franka_actor_ids_sim = self.franka_actor_ids_sim.clone().to(dtype=torch.int32)
self.gym.set_dof_state_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.dof_state),
gymtorch.unwrap_tensor(franka_actor_ids_sim),
len(franka_actor_ids_sim),
)
# Set DOF torque
self.gym.set_dof_actuation_force_tensor_indexed(
self.sim,
gymtorch.unwrap_tensor(self.dof_torque),
gymtorch.unwrap_tensor(franka_actor_ids_sim),
len(franka_actor_ids_sim),
)
# Simulate one step to apply changes
self.simulate_and_refresh()
def pose_world_to_robot_base(self, pos, quat):
"""Convert pose from world frame to robot base frame."""
robot_base_transform_inv = torch_utils.tf_inverse(
self.robot_base_quat, self.robot_base_pos
)
quat_in_robot_base, pos_in_robot_base = torch_utils.tf_combine(
robot_base_transform_inv[0], robot_base_transform_inv[1], quat, pos
)
return pos_in_robot_base, quat_in_robot_base
| 22,518 | Python | 43.592079 | 145 | 0.612266 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/industreal/industreal_env_pegs.py | # Copyright (c) 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.
"""IndustReal: class for pegs environment.
Inherits IndustReal base class and Factory abstract environment class. Inherited by IndustReal peg insertion task class. Not directly executed.
Configuration defined in IndustRealEnvPegs.yaml. Asset info defined in industreal_asset_info_pegs.yaml.
"""
import hydra
import math
import numpy as np
import os
import torch
from isaacgym import gymapi
from isaacgymenvs.tasks.factory.factory_schema_class_env import FactoryABCEnv
from isaacgymenvs.tasks.factory.factory_schema_config_env import FactorySchemaConfigEnv
from isaacgymenvs.tasks.industreal.industreal_base import IndustRealBase
class IndustRealEnvPegs(IndustRealBase, 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/IndustRealEnvPegs.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/industreal/yaml/industreal_asset_info_pegs.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"
]["industreal"][
"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."""
self.plug_files, self.socket_files = [], []
urdf_root = os.path.join(
os.path.dirname(__file__), "..", "..", "..", "assets", "industreal", "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 = 0.5 # default = 0.0
plug_options.max_linear_velocity = 1000.0 # default = 1000.0
plug_options.angular_damping = 0.5 # 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 = 1.0 # default = 1000.0
socket_options.angular_damping = 0.0 # default = 0.5
socket_options.max_angular_velocity = 2 * math.pi # 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)
# Save URDF file paths (for loading appropriate meshes during SAPU and SDF-Based Reward calculations)
self.plug_files.append(os.path.join(urdf_root, plug_file))
self.socket_files.append(os.path.join(urdf_root, socket_file))
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."""
# NOTE: Closely adapted from FactoryEnvInsertion; however, plug grasp offsets, plug widths, socket heights,
# and asset indices are now stored for possible use during policy learning."""
franka_pose = gymapi.Transform()
franka_pose.p.x = -self.cfg_base.env.franka_depth
franka_pose.p.y = 0.0
franka_pose.p.z = self.cfg_base.env.table_height
franka_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.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
self.plug_grasp_offsets = []
self.plug_widths = []
self.socket_heights = []
self.asset_indices = []
for i in range(self.num_envs):
env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row)
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)
plug_grasp_offset = self.asset_info_insertion[subassembly][components[0]][
"grasp_offset"
]
plug_width = self.asset_info_insertion[subassembly][components[0]][
"plug_width"
]
socket_height = self.asset_info_insertion[subassembly][components[1]][
"height"
]
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.plug_grasp_offsets.append(plug_grasp_offset)
self.plug_widths.append(plug_width)
self.socket_heights.append(socket_height)
self.asset_indices.append(j)
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.robot_base_body_id_env = self.gym.find_actor_rigid_body_index(
env_ptr, franka_handle, "panda_link0", gymapi.DOMAIN_ENV
)
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
)
self.hand_body_id_env_actor = self.gym.find_actor_rigid_body_index(
env_ptr, franka_handle, "panda_hand", gymapi.DOMAIN_ACTOR
)
self.left_finger_body_id_env_actor = self.gym.find_actor_rigid_body_index(
env_ptr, franka_handle, "panda_leftfinger", gymapi.DOMAIN_ACTOR
)
self.right_finger_body_id_env_actor = self.gym.find_actor_rigid_body_index(
env_ptr, franka_handle, "panda_rightfinger", gymapi.DOMAIN_ACTOR
)
self.fingertip_centered_body_id_env_actor = (
self.gym.find_actor_rigid_body_index(
env_ptr, franka_handle, "panda_fingertip_centered", gymapi.DOMAIN_ACTOR
)
)
# For computing body COM pos
self.plug_grasp_offsets = torch.tensor(
self.plug_grasp_offsets, device=self.device
)
self.plug_widths = torch.tensor(self.plug_widths, device=self.device)
self.socket_heights = torch.tensor(self.socket_heights, device=self.device)
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]
self.socket_linvel = self.root_linvel[:, self.socket_actor_id_env, 0:3]
self.socket_angvel = self.root_angvel[:, self.socket_actor_id_env, 0:3]
# 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.
pass
| 20,854 | Python | 42.538622 | 143 | 0.596145 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/industreal/industreal_env_gears.py | # Copyright (c) 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.
"""IndustReal: class for gears environment.
Inherits IndustReal base class and Factory abstract environment class. Inherited by IndustReal gear insertion task class. Not directly executed.
Configuration defined in IndustRealEnvGears.yaml. Asset info defined in industreal_asset_info_gears.yaml.
"""
import hydra
import os
import torch
import numpy as np
from isaacgym import gymapi
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
from isaacgymenvs.tasks.industreal.industreal_base import IndustRealBase
class IndustRealEnvGears(IndustRealBase, 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/IndustRealEnvGears.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/industreal/yaml/industreal_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"][
"industreal"
][
"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", "industreal", "urdf"
)
gear_small_file = "industreal_gear_small.urdf"
gear_medium_file = "industreal_gear_medium.urdf"
gear_large_file = "industreal_gear_large.urdf"
base_file = "industreal_gear_base.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.asset_info_gears.gears.density # default = 1000.0
gear_options.armature = 0.0 # default = 0.0
gear_options.use_physx_armature = True
gear_options.linear_damping = 0.5 # default = 0.0
gear_options.max_linear_velocity = 1000.0 # default = 1000.0
gear_options.angular_damping = 0.5 # 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.asset_info_gears.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)
# Save URDF file paths and asset indices (for loading appropriate meshes during SAPU and SDF-Based Reward calculations)
self.gear_files = [os.path.join(urdf_root, gear_medium_file)]
self.shaft_files = [os.path.join(urdf_root, base_file)]
# NOTE: Saving asset indices is not necessary for IndustRealEnvGears, as each parallel env has same assets; however, asset
# indices are saved anyway for parity with IndustRealEnvPegs.
self.asset_indices = [0 for _ in range(self.num_envs)]
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 = self.cfg_base.env.table_height
franka_pose.r = gymapi.Quat(
0.0, 0.0, 0.0, 1.0
) # TODO: Verify pose with Michael
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)
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.robot_base_body_id_env = self.gym.find_actor_rigid_body_index(
env_ptr, franka_handle, "panda_link0", gymapi.DOMAIN_ENV
)
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
)
self.hand_body_id_env_actor = self.gym.find_actor_rigid_body_index(
env_ptr, franka_handle, "panda_hand", gymapi.DOMAIN_ACTOR
)
self.left_finger_body_id_env_actor = self.gym.find_actor_rigid_body_index(
env_ptr, franka_handle, "panda_leftfinger", gymapi.DOMAIN_ACTOR
)
self.right_finger_body_id_env_actor = self.gym.find_actor_rigid_body_index(
env_ptr, franka_handle, "panda_rightfinger", gymapi.DOMAIN_ACTOR
)
self.fingertip_centered_body_id_env_actor = (
self.gym.find_actor_rigid_body_index(
env_ptr, franka_handle, "panda_fingertip_centered", gymapi.DOMAIN_ACTOR
)
)
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.base_linvel = self.root_linvel[:, self.base_actor_id_env, 0:3]
self.base_angvel = self.root_angvel[:, self.base_actor_id_env, 0:3]
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.base.height
+ self.asset_info_gears.gears.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.base.height
+ self.asset_info_gears.gears.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.base.height
+ self.asset_info_gears.gears.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.base.height
+ self.asset_info_gears.gears.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.base.height
+ self.asset_info_gears.gears.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.base.height
+ self.asset_info_gears.gears.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,
)
| 26,679 | Python | 42.666121 | 144 | 0.598373 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/tasks/industreal/industreal_algo_utils.py | # Copyright (c) 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.
"""IndustReal: algorithms module.
Contains functions that implement Simulation-Aware Policy Update (SAPU), SDF-Based Reward, and Sampling-Based Curriculum (SBC).
Not intended to be executed as a standalone script.
"""
import numpy as np
from pysdf import SDF
import torch
import trimesh
from urdfpy import URDF
import warp as wp
"""
Simulation-Aware Policy Update (SAPU)
"""
def load_asset_mesh_in_warp(urdf_path, sample_points, num_samples, device):
"""Create mesh object in Warp."""
urdf = URDF.load(urdf_path)
mesh = urdf.links[0].collision_mesh
wp_mesh = wp.Mesh(
points=wp.array(mesh.vertices, dtype=wp.vec3, device=device),
indices=wp.array(mesh.faces.flatten(), dtype=wp.int32, device=device),
)
if sample_points:
# Sample points on surface of mesh
sampled_points, _ = trimesh.sample.sample_surface_even(mesh, num_samples)
wp_mesh_sampled_points = wp.array(sampled_points, dtype=wp.vec3, device=device)
return wp_mesh, wp_mesh_sampled_points
else:
return wp_mesh
def load_asset_meshes_in_warp(plug_files, socket_files, num_samples, device):
"""Create mesh objects in Warp for all environments."""
# Load and store plug meshes and (if desired) sampled points
plug_meshes, plug_meshes_sampled_points = [], []
for i in range(len(plug_files)):
plug_mesh, sampled_points = load_asset_mesh_in_warp(
urdf_path=plug_files[i],
sample_points=True,
num_samples=num_samples,
device=device,
)
plug_meshes.append(plug_mesh)
plug_meshes_sampled_points.append(sampled_points)
# Load and store socket meshes
socket_meshes = [
load_asset_mesh_in_warp(
urdf_path=socket_files[i],
sample_points=False,
num_samples=-1,
device=device,
)
for i in range(len(socket_files))
]
return plug_meshes, plug_meshes_sampled_points, socket_meshes
def get_max_interpen_dists(
asset_indices,
plug_pos,
plug_quat,
socket_pos,
socket_quat,
wp_plug_meshes_sampled_points,
wp_socket_meshes,
wp_device,
device,
):
"""Get maximum interpenetration distances between plugs and sockets."""
num_envs = len(plug_pos)
max_interpen_dists = torch.zeros((num_envs,), dtype=torch.float32, device=device)
for i in range(num_envs):
asset_idx = asset_indices[i]
# Compute transform from plug frame to socket frame
plug_transform = wp.transform(plug_pos[i], plug_quat[i])
socket_transform = wp.transform(socket_pos[i], socket_quat[i])
socket_inv_transform = wp.transform_inverse(socket_transform)
plug_to_socket_transform = wp.transform_multiply(
plug_transform, socket_inv_transform
)
# Transform plug mesh vertices to socket frame
plug_points = wp.clone(wp_plug_meshes_sampled_points[asset_idx])
wp.launch(
kernel=transform_points,
dim=len(plug_points),
inputs=[plug_points, plug_points, plug_to_socket_transform],
device=wp_device,
)
# Compute max interpenetration distance between plug and socket
interpen_dist_plug_socket = wp.zeros(
(len(plug_points),), dtype=wp.float32, device=wp_device
)
wp.launch(
kernel=get_interpen_dist,
dim=len(plug_points),
inputs=[
plug_points,
wp_socket_meshes[asset_idx].id,
interpen_dist_plug_socket,
],
device=wp_device,
)
max_interpen_dist = -torch.min(wp.to_torch(interpen_dist_plug_socket))
# Store interpenetration flag and max interpenetration distance
if max_interpen_dist > 0.0:
max_interpen_dists[i] = max_interpen_dist
return max_interpen_dists
def get_sapu_reward_scale(
asset_indices,
plug_pos,
plug_quat,
socket_pos,
socket_quat,
wp_plug_meshes_sampled_points,
wp_socket_meshes,
interpen_thresh,
wp_device,
device,
):
"""Compute reward scale for SAPU."""
# Get max interpenetration distances
max_interpen_dists = get_max_interpen_dists(
asset_indices=asset_indices,
plug_pos=plug_pos,
plug_quat=plug_quat,
socket_pos=socket_pos,
socket_quat=socket_quat,
wp_plug_meshes_sampled_points=wp_plug_meshes_sampled_points,
wp_socket_meshes=wp_socket_meshes,
wp_device=wp_device,
device=device,
)
# Determine if envs have low interpenetration or high interpenetration
low_interpen_envs = torch.nonzero(max_interpen_dists <= interpen_thresh)
high_interpen_envs = torch.nonzero(max_interpen_dists > interpen_thresh)
# Compute reward scale
reward_scale = 1 - torch.tanh(
max_interpen_dists[low_interpen_envs] / interpen_thresh
)
return low_interpen_envs, high_interpen_envs, reward_scale
"""
SDF-Based Reward
"""
def get_plug_goal_sdfs(
wp_plug_meshes, asset_indices, socket_pos, socket_quat, wp_device
):
"""Get SDFs of plug meshes at goal pose."""
num_envs = len(socket_pos)
plug_goal_sdfs = []
for i in range(num_envs):
# Create copy of plug mesh
mesh = wp_plug_meshes[asset_indices[i]]
mesh_points = wp.clone(mesh.points)
mesh_indices = wp.clone(mesh.indices)
mesh_copy = wp.Mesh(points=mesh_points, indices=mesh_indices)
# Transform plug mesh from current pose to goal pose
# NOTE: In source OBJ files, when plug and socket are assembled,
# their poses are identical
goal_transform = wp.transform(socket_pos[i], socket_quat[i])
wp.launch(
kernel=transform_points,
dim=len(mesh_copy.points),
inputs=[mesh_copy.points, mesh_copy.points, goal_transform],
device=wp_device,
)
# Rebuild BVH (see https://nvidia.github.io/warp/_build/html/modules/runtime.html#meshes)
mesh_copy.refit()
# Create SDF from transformed mesh
sdf = SDF(mesh_copy.points.numpy(), mesh_copy.indices.numpy().reshape(-1, 3))
plug_goal_sdfs.append(sdf)
return plug_goal_sdfs
def get_sdf_reward(
wp_plug_meshes_sampled_points,
asset_indices,
plug_pos,
plug_quat,
plug_goal_sdfs,
wp_device,
device,
):
"""Calculate SDF-based reward."""
num_envs = len(plug_pos)
sdf_reward = torch.zeros((num_envs,), dtype=torch.float32, device=device)
for i in range(num_envs):
# Create copy of sampled points
sampled_points = wp.clone(wp_plug_meshes_sampled_points[asset_indices[i]])
# Transform sampled points from original plug pose to current plug pose
curr_transform = wp.transform(plug_pos[i], plug_quat[i])
wp.launch(
kernel=transform_points,
dim=len(sampled_points),
inputs=[sampled_points, sampled_points, curr_transform],
device=wp_device,
)
# Get SDF values at transformed points
sdf_dists = torch.from_numpy(plug_goal_sdfs[i](sampled_points.numpy())).double()
# Clamp values outside isosurface and take absolute value
sdf_dists = torch.abs(torch.where(sdf_dists > 0.0, 0.0, sdf_dists))
sdf_reward[i] = torch.mean(sdf_dists)
sdf_reward = -torch.log(sdf_reward)
return sdf_reward
"""
Sampling-Based Curriculum (SBC)
"""
def get_curriculum_reward_scale(cfg_task, curr_max_disp):
"""Compute reward scale for SBC."""
# Compute difference between max downward displacement at beginning of training (easiest condition)
# and current max downward displacement (based on current curriculum stage)
# NOTE: This number increases as curriculum gets harder
curr_stage_diff = cfg_task.rl.curriculum_height_bound[1] - curr_max_disp
# Compute difference between max downward displacement at beginning of training (easiest condition)
# and min downward displacement (hardest condition)
final_stage_diff = (
cfg_task.rl.curriculum_height_bound[1] - cfg_task.rl.curriculum_height_bound[0]
)
# Compute reward scale
reward_scale = curr_stage_diff / final_stage_diff + 1.0
return reward_scale
def get_new_max_disp(curr_success, cfg_task, curr_max_disp):
"""Update max downward displacement of plug at beginning of episode, based on success rate."""
if curr_success > cfg_task.rl.curriculum_success_thresh:
# If success rate is above threshold, reduce max downward displacement until min value
# NOTE: height_step[0] is negative
new_max_disp = max(
curr_max_disp + cfg_task.rl.curriculum_height_step[0],
cfg_task.rl.curriculum_height_bound[0],
)
elif curr_success < cfg_task.rl.curriculum_failure_thresh:
# If success rate is below threshold, increase max downward displacement until max value
# NOTE: height_step[1] is positive
new_max_disp = min(
curr_max_disp + cfg_task.rl.curriculum_height_step[1],
cfg_task.rl.curriculum_height_bound[1],
)
else:
# Maintain current max downward displacement
new_max_disp = curr_max_disp
return new_max_disp
"""
Bonus and Success Checking
"""
def get_keypoint_offsets(num_keypoints, device):
"""Get uniformly-spaced keypoints along a line of unit length, centered at 0."""
keypoint_offsets = torch.zeros((num_keypoints, 3), device=device)
keypoint_offsets[:, -1] = (
torch.linspace(0.0, 1.0, num_keypoints, device=device) - 0.5
)
return keypoint_offsets
def check_plug_close_to_socket(
keypoints_plug, keypoints_socket, dist_threshold, progress_buf
):
"""Check if plug is close to socket."""
# Compute keypoint distance between plug and socket
keypoint_dist = torch.norm(keypoints_socket - keypoints_plug, p=2, dim=-1)
# Check if keypoint distance is below threshold
is_plug_close_to_socket = torch.where(
torch.sum(keypoint_dist, dim=-1) < dist_threshold,
torch.ones_like(progress_buf),
torch.zeros_like(progress_buf),
)
return is_plug_close_to_socket
def check_plug_engaged_w_socket(
plug_pos, socket_top_pos, keypoints_plug, keypoints_socket, cfg_task, progress_buf
):
"""Check if plug is engaged with socket."""
# Check if base of plug is below top of socket
# NOTE: In assembled state, plug origin is coincident with socket origin;
# thus plug pos must be offset to compute actual pos of base of plug
is_plug_below_engagement_height = (
plug_pos[:, 2] + cfg_task.env.socket_base_height < socket_top_pos[:, 2]
)
# Check if plug is close to socket
# NOTE: This check addresses edge case where base of plug is below top of socket,
# but plug is outside socket
is_plug_close_to_socket = check_plug_close_to_socket(
keypoints_plug=keypoints_plug,
keypoints_socket=keypoints_socket,
dist_threshold=cfg_task.rl.close_error_thresh,
progress_buf=progress_buf,
)
# Combine both checks
is_plug_engaged_w_socket = torch.logical_and(
is_plug_below_engagement_height, is_plug_close_to_socket
)
return is_plug_engaged_w_socket
def check_plug_inserted_in_socket(
plug_pos, socket_pos, keypoints_plug, keypoints_socket, cfg_task, progress_buf
):
"""Check if plug is inserted in socket."""
# Check if plug is within threshold distance of assembled state
is_plug_below_insertion_height = (
plug_pos[:, 2] < socket_pos[:, 2] + cfg_task.rl.success_height_thresh
)
# Check if plug is close to socket
# NOTE: This check addresses edge case where plug is within threshold distance of
# assembled state, but plug is outside socket
is_plug_close_to_socket = check_plug_close_to_socket(
keypoints_plug=keypoints_plug,
keypoints_socket=keypoints_socket,
dist_threshold=cfg_task.rl.close_error_thresh,
progress_buf=progress_buf,
)
# Combine both checks
is_plug_inserted_in_socket = torch.logical_and(
is_plug_below_insertion_height, is_plug_close_to_socket
)
return is_plug_inserted_in_socket
def check_gear_engaged_w_shaft(
keypoints_gear,
keypoints_shaft,
gear_pos,
shaft_pos,
asset_info_gears,
cfg_task,
progress_buf,
):
"""Check if gear is engaged with shaft."""
# Check if bottom of gear is below top of shaft
is_gear_below_engagement_height = (
gear_pos[:, 2]
< shaft_pos[:, 2]
+ asset_info_gears.base.height
+ asset_info_gears.shafts.height
)
# Check if gear is close to shaft
# Note: This check addresses edge case where gear is within threshold distance of
# assembled state, but gear is outside shaft
is_gear_close_to_shaft = check_plug_close_to_socket(
keypoints_plug=keypoints_gear,
keypoints_socket=keypoints_shaft,
dist_threshold=cfg_task.rl.close_error_thresh,
progress_buf=progress_buf,
)
# Combine both checks
is_gear_engaged_w_shaft = torch.logical_and(
is_gear_below_engagement_height, is_gear_close_to_shaft
)
return is_gear_engaged_w_shaft
def check_gear_inserted_on_shaft(
gear_pos, shaft_pos, keypoints_gear, keypoints_shaft, cfg_task, progress_buf
):
"""Check if gear is inserted on shaft."""
# Check if gear is within threshold distance of assembled state
is_gear_below_insertion_height = (
gear_pos[:, 2] < shaft_pos[:, 2] + cfg_task.rl.success_height_thresh
)
# Check if keypoint distance is below threshold
is_gear_close_to_shaft = check_plug_close_to_socket(
keypoints_plug=keypoints_gear,
keypoints_socket=keypoints_shaft,
dist_threshold=cfg_task.rl.close_error_thresh,
progress_buf=progress_buf,
)
# Combine both checks
is_gear_inserted_on_shaft = torch.logical_and(
is_gear_below_insertion_height, is_gear_close_to_shaft
)
return is_gear_inserted_on_shaft
def get_engagement_reward_scale(
plug_pos, socket_pos, is_plug_engaged_w_socket, success_height_thresh, device
):
"""Compute scale on reward. If plug is not engaged with socket, scale is zero.
If plug is engaged, scale is proportional to distance between plug and bottom of socket."""
# Set default value of scale to zero
num_envs = len(plug_pos)
reward_scale = torch.zeros((num_envs,), dtype=torch.float32, device=device)
# For envs in which plug and socket are engaged, compute positive scale
engaged_idx = np.argwhere(is_plug_engaged_w_socket.cpu().numpy().copy()).squeeze()
height_dist = plug_pos[engaged_idx, 2] - socket_pos[engaged_idx, 2]
# NOTE: Edge case: if success_height_thresh is greater than 0.1,
# denominator could be negative
reward_scale[engaged_idx] = 1.0 / ((height_dist - success_height_thresh) + 0.1)
return reward_scale
"""
Warp Kernels
"""
# Transform points from source coordinate frame to destination coordinate frame
@wp.kernel
def transform_points(
src: wp.array(dtype=wp.vec3), dest: wp.array(dtype=wp.vec3), xform: wp.transform
):
tid = wp.tid()
p = src[tid]
m = wp.transform_point(xform, p)
dest[tid] = m
# Return interpenetration distances between query points (e.g., plug vertices in current pose)
# and mesh surfaces (e.g., of socket mesh in current pose)
@wp.kernel
def get_interpen_dist(
queries: wp.array(dtype=wp.vec3),
mesh: wp.uint64,
interpen_dists: wp.array(dtype=wp.float32),
):
tid = wp.tid()
# Declare arguments to wp.mesh_query_point() that will not be modified
q = queries[tid] # query point
max_dist = 1.5 # max distance on mesh from query point
# Declare arguments to wp.mesh_query_point() that will be modified
sign = float(
0.0
) # -1 if query point inside mesh; 0 if on mesh; +1 if outside mesh (NOTE: Mesh must be watertight!)
face_idx = int(0) # index of closest face
face_u = float(0.0) # barycentric u-coordinate of closest point
face_v = float(0.0) # barycentric v-coordinate of closest point
# Get closest point on mesh to query point
closest_mesh_point_exists = wp.mesh_query_point(
mesh, q, max_dist, sign, face_idx, face_u, face_v
)
# If point exists within max_dist
if closest_mesh_point_exists:
# Get 3D position of point on mesh given face index and barycentric coordinates
p = wp.mesh_eval_position(mesh, face_idx, face_u, face_v)
# Get signed distance between query point and mesh point
delta = q - p
signed_dist = sign * wp.length(delta)
# If signed distance is negative
if signed_dist < 0.0:
# Store interpenetration distance
interpen_dists[tid] = signed_dist
| 18,554 | Python | 31.957371 | 127 | 0.664924 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/config.yaml |
# Task name - used to pick the class to load
task_name: ${task.name}
# experiment name. defaults to name of training config
experiment: ''
# if set to positive integer, overrides the default number of environments
num_envs: ''
# seed - set to -1 to choose random seed
seed: 42
# set to True for deterministic performance
torch_deterministic: False
# set the maximum number of learning iterations to train for. overrides default per-environment setting
max_iterations: ''
## Device config
# 'physx' or 'flex'
physics_engine: 'physx'
# whether to use cpu or gpu pipeline
pipeline: 'gpu'
# device for running physics simulation
sim_device: 'cuda:0'
# device to run RL
rl_device: 'cuda:0'
graphics_device_id: 0
## PhysX arguments
num_threads: 4 # Number of worker threads per scene used by PhysX - for CPU PhysX only.
solver_type: 1 # 0: pgs, 1: tgs
num_subscenes: 4 # Splits the simulation into N physics scenes and runs each one in a separate thread
# RLGames Arguments
# test - if set, run policy in inference mode (requires setting checkpoint to load)
test: False
# used to set checkpoint path
checkpoint: ''
# set sigma when restoring network
sigma: ''
# set to True to use multi-gpu training
multi_gpu: False
wandb_activate: False
wandb_group: ''
wandb_name: ${train.params.config.name}
wandb_entity: ''
wandb_project: 'isaacgymenvs'
wandb_tags: []
wandb_logcode_dir: ''
capture_video: False
capture_video_freq: 1464
capture_video_len: 100
force_render: True
# disables rendering
headless: False
# set default task and default training config based on task
defaults:
- task: Ant
- train: ${task}PPO
- pbt: no_pbt
- override hydra/job_logging: disabled
- _self_
# set the directory where the output files get saved
hydra:
output_subdir: null
run:
dir: .
| 1,788 | YAML | 23.175675 | 103 | 0.735459 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FactoryTaskNutBoltScrew.yaml | # See schema in factory_schema_config_task.py for descriptions of common parameters.
defaults:
- FactoryBase
- _self_
# - /factory_schema_config_task
name: FactoryTaskNutBoltScrew
physics_engine: ${..physics_engine}
sim:
disable_gravity: False
env:
numEnvs: ${resolve_default:128,${...num_envs}}
numObservations: 32
numActions: 12
randomize:
franka_arm_initial_dof_pos: [1.5178e-03, -1.9651e-01, -1.4364e-03, -1.9761e+00, -2.7717e-04, 1.7796e+00, 7.8556e-01]
nut_rot_initial: 30.0 # initial rotation of nut from configuration in CAD [deg]; default = 30.0 (gripper aligns with flat surfaces of nut)
rl:
pos_action_scale: [0.1, 0.1, 0.1]
rot_action_scale: [0.1, 0.1, 0.1]
force_action_scale: [1.0, 1.0, 1.0]
torque_action_scale: [1.0, 1.0, 1.0]
unidirectional_rot: True # constrain Franka Z-rot to be unidirectional
unidirectional_force: False # constrain Franka Z-force to be unidirectional (useful for debugging)
clamp_rot: True
clamp_rot_thresh: 1.0e-6
add_obs_finger_force: False # add observations of force on left and right fingers
keypoint_reward_scale: 1.0 # scale on keypoint-based reward
action_penalty_scale: 0.0 # scale on action penalty
max_episode_length: 8192 # terminate episode after this number of timesteps (failure)
far_error_thresh: 0.100 # threshold above which nut is considered too far from bolt
success_bonus: 0.0 # bonus if nut is close enough to base of bolt shank
ctrl:
ctrl_type: operational_space_motion # {gym_default,
# joint_space_ik, joint_space_id,
# task_space_impedance, operational_space_motion,
# open_loop_force, closed_loop_force,
# hybrid_force_motion}
all:
jacobian_type: geometric
gripper_prop_gains: [100, 100]
gripper_deriv_gains: [1, 1]
gym_default:
ik_method: dls
joint_prop_gains: [40, 40, 40, 40, 40, 40, 40]
joint_deriv_gains: [8, 8, 8, 8, 8, 8, 8]
gripper_prop_gains: [500, 500]
gripper_deriv_gains: [20, 20]
joint_space_ik:
ik_method: dls
joint_prop_gains: [1, 1, 1, 1, 1, 1, 1]
joint_deriv_gains: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
joint_space_id:
ik_method: dls
joint_prop_gains: [40, 40, 40, 40, 40, 40, 40]
joint_deriv_gains: [8, 8, 8, 8, 8, 8, 8]
task_space_impedance:
motion_ctrl_axes: [1, 1, 1, 1, 1, 1]
task_prop_gains: [40, 40, 40, 40, 40, 40]
task_deriv_gains: [8, 8, 8, 8, 8, 8]
operational_space_motion:
motion_ctrl_axes: [0, 0, 1, 0, 0, 1]
task_prop_gains: [1, 1, 1, 1, 1, 200]
task_deriv_gains: [1, 1, 1, 1, 1, 1]
open_loop_force:
force_ctrl_axes: [0, 0, 1, 0, 0, 0]
closed_loop_force:
force_ctrl_axes: [0, 0, 1, 0, 0, 0]
wrench_prop_gains: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
hybrid_force_motion:
motion_ctrl_axes: [1, 1, 0, 1, 1, 1]
task_prop_gains: [40, 40, 40, 40, 40, 40]
task_deriv_gains: [8, 8, 8, 8, 8, 8]
force_ctrl_axes: [0, 0, 1, 0, 0, 0]
wrench_prop_gains: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1] | 3,309 | YAML | 37.045977 | 143 | 0.576307 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/AllegroKukaTwoArmsLSTM.yaml | defaults:
- AllegroKukaLSTM
- _self_
name: AllegroKukaTwoArms
env:
numArms: 2
envSpacing: 1.75
# two arms essentially need to throw the object to each other
# training is much harder with random forces, so we disable it here as we do for the throw task
# forceScale: 0.0
armXOfs: 1.1 # distance from the center of the table, distance between arms is 2x this
armYOfs: 0.0
| 395 | YAML | 20.999999 | 97 | 0.718987 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FrankaCabinet.yaml | # used to create the object
name: FrankaCabinet
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 1.5
episodeLength: 500
enableDebugVis: False
clipObservations: 5.0
clipActions: 1.0
startPositionNoise: 0.0
startRotationNoise: 0.0
numProps: 16
aggregateMode: 3
actionScale: 7.5
dofVelocityScale: 0.1
distRewardScale: 2.0
rotRewardScale: 0.5
aroundHandleRewardScale: 0.25
openRewardScale: 7.5
fingerDistRewardScale: 5.0
actionPenaltyScale: 0.01
asset:
assetRoot: "../../assets"
assetFileNameFranka: "urdf/franka_description/robots/franka_panda.urdf"
assetFileNameCabinet: "urdf/sektion_cabinet_model/urdf/sektion_cabinet_2.urdf"
# set to True if you use camera sensors in the environment
enableCameraSensors: False
sim:
dt: 0.0166 # 1/60
substeps: 1
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 12
num_velocity_iterations: 1
contact_offset: 0.005
rest_offset: 0.0
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 1000.0
default_buffer_size_multiplier: 5.0
max_gpu_contact_pairs: 1048576 # 1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 0 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
| 1,680 | YAML | 26.112903 | 171 | 0.693452 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/IndustRealBase.yaml | # See schema in factory_schema_config_base.py for descriptions of parameters.
defaults:
- _self_
mode:
export_scene: False
export_states: False
sim:
dt: 0.016667
substeps: 2
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_damping: True
disable_franka_collisions: False
physx:
solver_type: ${....solver_type}
num_threads: ${....num_threads}
num_subscenes: ${....num_subscenes}
use_gpu: ${contains:"cuda",${....sim_device}}
num_position_iterations: 16
num_velocity_iterations: 0
contact_offset: 0.01
rest_offset: 0.0
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 5.0
friction_offset_threshold: 0.01
friction_correlation_distance: 0.00625
max_gpu_contact_pairs: 6553600 # 50 * 1024 * 1024
default_buffer_size_multiplier: 8.0
contact_collection: 1 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
env:
env_spacing: 0.7
franka_depth: 0.37 # Franka origin 37 cm behind table midpoint
table_height: 1.04
franka_friction: 4.0
table_friction: 0.3 | 1,286 | YAML | 28.930232 | 175 | 0.619751 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/Ant.yaml | # used to create the object
name: Ant
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 5
episodeLength: 1000
enableDebugVis: False
clipActions: 1.0
powerScale: 1.0
controlFrequencyInv: 1 # 60 Hz
# reward parameters
headingWeight: 0.5
upWeight: 0.1
# cost parameters
actionsCost: 0.005
energyCost: 0.05
dofVelocityScale: 0.2
contactForceScale: 0.1
jointsAtLimitCost: 0.1
deathCost: -2.0
terminationHeight: 0.31
plane:
staticFriction: 1.0
dynamicFriction: 1.0
restitution: 0.0
asset:
assetFileName: "mjcf/nv_ant.xml"
# set to True if you use camera sensors in the environment
enableCameraSensors: False
sim:
dt: 0.0166 # 1/60 s
substeps: 2
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 4
num_velocity_iterations: 0
contact_offset: 0.02
rest_offset: 0.0
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 10.0
default_buffer_size_multiplier: 5.0
max_gpu_contact_pairs: 8388608 # 8*1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 0 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
randomization_params:
# specify which attributes to randomize for each actor type and property
frequency: 600 # Define how many environment steps between generating new randomizations
observations:
range: [0, .002] # range for the white noise
operation: "additive"
distribution: "gaussian"
actions:
range: [0., .02]
operation: "additive"
distribution: "gaussian"
actor_params:
ant:
color: True
rigid_body_properties:
mass:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
setup_only: True # Property will only be randomized once before simulation is started. See Domain Randomization Documentation for more info.
dof_properties:
damping:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
stiffness:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
lower:
range: [0, 0.01]
operation: "additive"
distribution: "gaussian"
upper:
range: [0, 0.01]
operation: "additive"
distribution: "gaussian"
| 2,841 | YAML | 26.862745 | 171 | 0.62302 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FrankaCubeStack.yaml | # used to create the object
name: FrankaCubeStack
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:8192,${...num_envs}}
envSpacing: 1.5
episodeLength: 300
enableDebugVis: False
clipObservations: 5.0
clipActions: 1.0
startPositionNoise: 0.25
startRotationNoise: 0.785
frankaPositionNoise: 0.0
frankaRotationNoise: 0.0
frankaDofNoise: 0.25
aggregateMode: 3
actionScale: 1.0
distRewardScale: 0.1
liftRewardScale: 1.5
alignRewardScale: 2.0
stackRewardScale: 16.0
controlType: osc # options are {joint_tor, osc}
asset:
assetRoot: "../../assets"
assetFileNameFranka: "urdf/franka_description/robots/franka_panda_gripper.urdf"
# set to True if you use camera sensors in the environment
enableCameraSensors: False
sim:
dt: 0.01667 # 1/60
substeps: 2
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 8
num_velocity_iterations: 1
contact_offset: 0.005
rest_offset: 0.0
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 1000.0
default_buffer_size_multiplier: 5.0
max_gpu_contact_pairs: 1048576 # 1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 0 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
| 1,639 | YAML | 25.451612 | 171 | 0.688225 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/HumanoidAMP.yaml | # used to create the object
name: HumanoidAMP
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 5
episodeLength: 300
cameraFollow: True # if the camera follows humanoid or not
enableDebugVis: False
pdControl: True
powerScale: 1.0
controlFrequencyInv: 2 # 30 Hz
stateInit: "Random"
hybridInitProb: 0.5
numAMPObsSteps: 2
localRootObs: False
contactBodies: ["right_foot", "left_foot"]
terminationHeight: 0.5
enableEarlyTermination: True
# animation files to learn from
# these motions should use hyperparameters from HumanoidAMPPPO.yaml
#motion_file: "amp_humanoid_walk.npy"
motion_file: "amp_humanoid_run.npy"
#motion_file: "amp_humanoid_dance.npy"
# these motions should use hyperparameters from HumanoidAMPPPOLowGP.yaml
#motion_file: "amp_humanoid_hop.npy"
#motion_file: "amp_humanoid_backflip.npy"
asset:
assetFileName: "mjcf/amp_humanoid.xml"
plane:
staticFriction: 1.0
dynamicFriction: 1.0
restitution: 0.0
sim:
dt: 0.0166 # 1/60 s
substeps: 2
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 4
num_velocity_iterations: 0
contact_offset: 0.02
rest_offset: 0.0
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 10.0
default_buffer_size_multiplier: 5.0
max_gpu_contact_pairs: 8388608 # 8*1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 2 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
randomization_params:
# specify which attributes to randomize for each actor type and property
frequency: 600 # Define how many environment steps between generating new randomizations
observations:
range: [0, .002] # range for the white noise
operation: "additive"
distribution: "gaussian"
actions:
range: [0., .02]
operation: "additive"
distribution: "gaussian"
sim_params:
gravity:
range: [0, 0.4]
operation: "additive"
distribution: "gaussian"
schedule: "linear" # "linear" will linearly interpolate between no rand and max rand
schedule_steps: 3000
actor_params:
humanoid:
color: True
rigid_body_properties:
mass:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
setup_only: True # Property will only be randomized once before simulation is started. See Domain Randomization Documentation for more info.
schedule: "linear" # "linear" will linearly interpolate between no rand and max rand
schedule_steps: 3000
rigid_shape_properties:
friction:
num_buckets: 500
range: [0.7, 1.3]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
restitution:
range: [0., 0.7]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
dof_properties:
damping:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
stiffness:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
lower:
range: [0, 0.01]
operation: "additive"
distribution: "gaussian"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
upper:
range: [0, 0.01]
operation: "additive"
distribution: "gaussian"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
| 4,881 | YAML | 34.897059 | 171 | 0.629379 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/AnymalTerrain.yaml | # used to create the object
name: AnymalTerrain
physics_engine: 'physx'
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
numObservations: 188
numActions: 12
envSpacing: 3. # [m]
enableDebugVis: False
terrain:
terrainType: trimesh # none, plane, or trimesh
staticFriction: 1.0 # [-]
dynamicFriction: 1.0 # [-]
restitution: 0. # [-]
# rough terrain only:
curriculum: true
maxInitMapLevel: 0
mapLength: 8.
mapWidth: 8.
numLevels: 10
numTerrains: 20
# terrain types: [smooth slope, rough slope, stairs up, stairs down, discrete]
terrainProportions: [0.1, 0.1, 0.35, 0.25, 0.2]
# tri mesh only:
slopeTreshold: 0.5
baseInitState:
pos: [0.0, 0.0, 0.62] # x,y,z [m]
rot: [0.0, 0.0, 0.0, 1.0] # x,y,z,w [quat]
vLinear: [0.0, 0.0, 0.0] # x,y,z [m/s]
vAngular: [0.0, 0.0, 0.0] # x,y,z [rad/s]
randomCommandVelocityRanges:
# train
linear_x: [-1., 1.] # min max [m/s]
linear_y: [-1., 1.] # min max [m/s]
yaw: [-3.14, 3.14] # min max [rad/s]
control:
# PD Drive parameters:
stiffness: 80.0 # [N*m/rad]
damping: 2.0 # [N*m*s/rad]
# action scale: target angle = actionScale * action + defaultAngle
actionScale: 0.5
# decimation: Number of control action updates @ sim DT per policy DT
decimation: 4
defaultJointAngles: # = target angles when action = 0.0
LF_HAA: 0.03 # [rad]
LH_HAA: 0.03 # [rad]
RF_HAA: -0.03 # [rad]
RH_HAA: -0.03 # [rad]
LF_HFE: 0.4 # [rad]
LH_HFE: -0.4 # [rad]
RF_HFE: 0.4 # [rad]
RH_HFE: -0.4 # [rad]
LF_KFE: -0.8 # [rad]
LH_KFE: 0.8 # [rad]
RF_KFE: -0.8 # [rad]
RH_KFE: 0.8 # [rad]
urdfAsset:
file: "urdf/anymal_c/urdf/anymal_minimal.urdf"
footName: SHANK # SHANK if collapsing fixed joint, FOOT otherwise
kneeName: THIGH
collapseFixedJoints: True
fixBaseLink: false
defaultDofDriveMode: 4 # see GymDofDriveModeFlags (0 is none, 1 is pos tgt, 2 is vel tgt, 4 effort)
learn:
allowKneeContacts: true
# rewards
terminalReward: 0.0
linearVelocityXYRewardScale: 1.0
linearVelocityZRewardScale: -4.0
angularVelocityXYRewardScale: -0.05
angularVelocityZRewardScale: 0.5
orientationRewardScale: -0. #-1.
torqueRewardScale: -0.00002 # -0.000025
jointAccRewardScale: -0.0005 # -0.0025
baseHeightRewardScale: -0.0 #5
feetAirTimeRewardScale: 1.0
kneeCollisionRewardScale: -0.25
feetStumbleRewardScale: -0. #-2.0
actionRateRewardScale: -0.01
# cosmetics
hipRewardScale: -0. #25
# normalization
linearVelocityScale: 2.0
angularVelocityScale: 0.25
dofPositionScale: 1.0
dofVelocityScale: 0.05
heightMeasurementScale: 5.0
# noise
addNoise: true
noiseLevel: 1.0 # scales other values
dofPositionNoise: 0.01
dofVelocityNoise: 1.5
linearVelocityNoise: 0.1
angularVelocityNoise: 0.2
gravityNoise: 0.05
heightMeasurementNoise: 0.06
#randomization
randomizeFriction: true
frictionRange: [0.5, 1.25]
pushRobots: true
pushInterval_s: 15
# episode length in seconds
episodeLength_s: 20
# viewer cam:
viewer:
refEnv: 0
pos: [0, 0, 10] # [m]
lookat: [1., 1, 9] # [m]
# set to True if you use camera sensors in the environment
enableCameraSensors: False
sim:
dt: 0.005
substeps: 1
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 4
num_velocity_iterations: 1
contact_offset: 0.02
rest_offset: 0.0
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 100.0
default_buffer_size_multiplier: 5.0
max_gpu_contact_pairs: 8388608 # 8*1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 1 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
| 4,203 | YAML | 26.657895 | 171 | 0.621461 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/BallBalance.yaml | # used to create the object
name: BallBalance
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 2.0
maxEpisodeLength: 500
actionSpeedScale: 20
enableDebugVis: False
clipObservations: 5.0
clipActions: 1.0
# set to True if you use camera sensors in the environment
enableCameraSensors: False
sim:
dt: 0.01
substeps: 1
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 8
num_velocity_iterations: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 1000.0
default_buffer_size_multiplier: 5.0
max_gpu_contact_pairs: 8388608 # 8*1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 0 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
| 1,208 | YAML | 27.785714 | 171 | 0.677152 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/IndustRealEnvGears.yaml | # See schema in factory_schema_config_env.py for descriptions of common parameters.
defaults:
- IndustRealBase
- _self_
- /factory_schema_config_env
env:
env_name: 'IndustRealEnvGears'
gears_lateral_offset: 0.1 # Y-axis offset of gears before initial reset to prevent initial interpenetration with base plate
gears_friction: 0.5 # coefficient of friction associated with gears
base_friction: 0.5 # coefficient of friction associated with base plate
| 487 | YAML | 33.85714 | 128 | 0.735113 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FactoryBase.yaml | # See schema in factory_schema_config_base.py for descriptions of parameters.
defaults:
- _self_
#- /factory_schema_config_base
mode:
export_scene: False
export_states: False
sim:
dt: 0.016667
substeps: 2
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_damping: True
physx:
solver_type: ${....solver_type}
num_threads: ${....num_threads}
num_subscenes: ${....num_subscenes}
use_gpu: ${contains:"cuda",${....sim_device}}
num_position_iterations: 16
num_velocity_iterations: 0
contact_offset: 0.005
rest_offset: 0.0
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 5.0
friction_offset_threshold: 0.01
friction_correlation_distance: 0.00625
max_gpu_contact_pairs: 1048576 # 1024 * 1024
default_buffer_size_multiplier: 8.0
contact_collection: 1 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
env:
env_spacing: 0.5
franka_depth: 0.5
table_height: 0.4
franka_friction: 1.0
table_friction: 0.3
| 1,229 | YAML | 26.954545 | 175 | 0.613507 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/Humanoid.yaml | # used to create the object
name: Humanoid
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 5
episodeLength: 1000
enableDebugVis: False
clipActions: 1.0
powerScale: 1.0
# reward parameters
headingWeight: 0.5
upWeight: 0.1
# cost parameters
actionsCost: 0.01
energyCost: 0.05
dofVelocityScale: 0.1
angularVelocityScale: 0.25
contactForceScale: 0.01
jointsAtLimitCost: 0.25
deathCost: -1.0
terminationHeight: 0.8
asset:
assetFileName: "mjcf/nv_humanoid.xml"
plane:
staticFriction: 1.0
dynamicFriction: 1.0
restitution: 0.0
# set to True if you use camera sensors in the environment
enableCameraSensors: False
sim:
dt: 0.0166 # 1/60 s
substeps: 2
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 4
num_velocity_iterations: 0
contact_offset: 0.02
rest_offset: 0.0
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 10.0
default_buffer_size_multiplier: 5.0
max_gpu_contact_pairs: 8388608 # 8*1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 0 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
randomization_params:
# specify which attributes to randomize for each actor type and property
frequency: 600 # Define how many environment steps between generating new randomizations
observations:
range: [0, .002] # range for the white noise
operation: "additive"
distribution: "gaussian"
actions:
range: [0., .02]
operation: "additive"
distribution: "gaussian"
sim_params:
gravity:
range: [0, 0.4]
operation: "additive"
distribution: "gaussian"
schedule: "linear" # "linear" will linearly interpolate between no rand and max rand
schedule_steps: 3000
actor_params:
humanoid:
color: True
rigid_body_properties:
mass:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
setup_only: True # Property will only be randomized once before simulation is started. See Domain Randomization Documentation for more info.
schedule: "linear" # "linear" will linearly interpolate between no rand and max rand
schedule_steps: 3000
rigid_shape_properties:
friction:
num_buckets: 500
range: [0.7, 1.3]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
restitution:
range: [0., 0.7]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
dof_properties:
damping:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
stiffness:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
lower:
range: [0, 0.01]
operation: "additive"
distribution: "gaussian"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
upper:
range: [0, 0.01]
operation: "additive"
distribution: "gaussian"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
| 4,571 | YAML | 33.37594 | 171 | 0.619996 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FactoryEnvGears.yaml | # See schema in factory_schema_config_env.py for descriptions of common parameters.
defaults:
- FactoryBase
- _self_
- /factory_schema_config_env
sim:
disable_franka_collisions: False
env:
env_name: 'FactoryEnvGears'
tight_or_loose: loose # use assets with loose (maximal clearance) or tight (minimal clearance) shafts
gears_lateral_offset: 0.1 # Y-axis offset of gears before initial reset to prevent initial interpenetration with base plate
gears_density: 1000.0 # density of gears
base_density: 2700.0 # density of base plate
gears_friction: 0.3 # coefficient of friction associated with gears
base_friction: 0.3 # coefficient of friction associated with base plate
| 723 | YAML | 35.199998 | 128 | 0.73029 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FactoryTaskNutBoltPick.yaml | # See schema in factory_schema_config_task.py for descriptions of common parameters.
defaults:
- FactoryBase
- _self_
# - /factory_schema_config_task
name: FactoryTaskNutBoltPick
physics_engine: ${..physics_engine}
sim:
disable_gravity: False
env:
numEnvs: ${resolve_default:128,${...num_envs}}
numObservations: 20
numActions: 12
close_and_lift: True # close gripper and lift after last step of episode
num_gripper_move_sim_steps: 20 # number of timesteps to reserve for moving gripper before first step of episode
num_gripper_close_sim_steps: 25 # number of timesteps to reserve for closing gripper after last step of episode
num_gripper_lift_sim_steps: 25 # number of timesteps to reserve for lift after last step of episode
randomize:
franka_arm_initial_dof_pos: [0.3413, -0.8011, -0.0670, -1.8299, 0.0266, 1.0185, 1.0927]
fingertip_midpoint_pos_initial: [0.0, -0.2, 0.2] # initial position of hand above table
fingertip_midpoint_pos_noise: [0.2, 0.2, 0.1] # noise on hand position
fingertip_midpoint_rot_initial: [3.1416, 0, 3.1416] # initial rotation of fingertips (Euler)
fingertip_midpoint_rot_noise: [0.3, 0.3, 1] # noise on rotation
nut_pos_xy_initial: [0.0, -0.3] # initial XY position of nut on table
nut_pos_xy_initial_noise: [0.1, 0.1] # noise on nut position
bolt_pos_xy_initial: [0.0, 0.0] # initial position of bolt on table
bolt_pos_xy_noise: [0.1, 0.1] # noise on bolt position
rl:
pos_action_scale: [0.1, 0.1, 0.1]
rot_action_scale: [0.1, 0.1, 0.1]
force_action_scale: [1.0, 1.0, 1.0]
torque_action_scale: [1.0, 1.0, 1.0]
clamp_rot: True
clamp_rot_thresh: 1.0e-6
num_keypoints: 4 # number of keypoints used in reward
keypoint_scale: 0.5 # length of line of keypoints
keypoint_reward_scale: 1.0 # scale on keypoint-based reward
action_penalty_scale: 0.0 # scale on action penalty
max_episode_length: 100
success_bonus: 0.0 # bonus if nut has been lifted
ctrl:
ctrl_type: joint_space_id # {gym_default,
# joint_space_ik, joint_space_id,
# task_space_impedance, operational_space_motion,
# open_loop_force, closed_loop_force,
# hybrid_force_motion}
all:
jacobian_type: geometric
gripper_prop_gains: [50, 50]
gripper_deriv_gains: [2, 2]
gym_default:
ik_method: dls
joint_prop_gains: [40, 40, 40, 40, 40, 40, 40]
joint_deriv_gains: [8, 8, 8, 8, 8, 8, 8]
gripper_prop_gains: [500, 500]
gripper_deriv_gains: [20, 20]
joint_space_ik:
ik_method: dls
joint_prop_gains: [1, 1, 1, 1, 1, 1, 1]
joint_deriv_gains: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
joint_space_id:
ik_method: dls
joint_prop_gains: [40, 40, 40, 40, 40, 40, 40]
joint_deriv_gains: [8, 8, 8, 8, 8, 8, 8]
task_space_impedance:
motion_ctrl_axes: [1, 1, 1, 1, 1, 1]
task_prop_gains: [40, 40, 40, 40, 40, 40]
task_deriv_gains: [8, 8, 8, 8, 8, 8]
operational_space_motion:
motion_ctrl_axes: [1, 1, 1, 1, 1, 1]
task_prop_gains: [1, 1, 1, 1, 1, 1]
task_deriv_gains: [1, 1, 1, 1, 1, 1]
open_loop_force:
force_ctrl_axes: [0, 0, 1, 0, 0, 0]
closed_loop_force:
force_ctrl_axes: [0, 0, 1, 0, 0, 0]
wrench_prop_gains: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
hybrid_force_motion:
motion_ctrl_axes: [1, 1, 0, 1, 1, 1]
task_prop_gains: [40, 40, 40, 40, 40, 40]
task_deriv_gains: [8, 8, 8, 8, 8, 8]
force_ctrl_axes: [0, 0, 1, 0, 0, 0]
wrench_prop_gains: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1] | 3,784 | YAML | 38.427083 | 116 | 0.589059 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/HumanoidSAC.yaml | # used to create the object
defaults:
- Humanoid
- _self_
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:64,${...num_envs}} | 168 | YAML | 20.124997 | 52 | 0.678571 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FactoryTaskInsertion.yaml | # See schema in factory_schema_config_task.py for descriptions of common parameters.
defaults:
- FactoryBase
- _self_
# - /factory_schema_config_task
name: FactoryTaskInsertion
physics_engine: ${..physics_engine}
env:
numEnvs: ${resolve_default:128,${...num_envs}}
numObservations: 32
numActions: 12
randomize:
joint_noise: 0.0 # noise on Franka DOF positions [deg]
initial_state: random # initialize plugs in random state or goal state {random, goal}
plug_bias_y: -0.1 # if random, Y-axis offset of plug during each reset to prevent initial interpenetration with socket
plug_bias_z: 0.0 # if random, Z-axis offset of plug during each reset to prevent initial interpenetration with ground plane
plug_noise_xy: 0.05 # if random, XY-axis noise on plug position during each reset
rl:
max_episode_length: 1024
| 864 | YAML | 33.599999 | 128 | 0.716435 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FactoryEnvInsertion.yaml | # See schema in factory_schema_config_env.py for descriptions of common parameters.
defaults:
- FactoryBase
- _self_
- /factory_schema_config_env
sim:
disable_franka_collisions: False
env:
env_name: 'FactoryEnvInsertion'
desired_subassemblies: ['round_peg_hole_4mm_loose',
'round_peg_hole_8mm_loose',
'round_peg_hole_12mm_loose',
'round_peg_hole_16mm_loose',
'rectangular_peg_hole_4mm_loose',
'rectangular_peg_hole_8mm_loose',
'rectangular_peg_hole_12mm_loose',
'rectangular_peg_hole_16mm_loose']
plug_lateral_offset: 0.1 # Y-axis offset of plug before initial reset to prevent initial interpenetration with socket
# Subassembly options:
# {round_peg_hole_4mm_tight, round_peg_hole_4mm_loose,
# round_peg_hole_8mm_tight, round_peg_hole_8mm_loose,
# round_peg_hole_12mm_tight, round_peg_hole_12mm_loose,
# round_peg_hole_16mm_tight, round_peg_hole_16mm_loose,
# rectangular_peg_hole_4mm_tight, rectangular_peg_hole_4mm_loose,
# rectangular_peg_hole_8mm_tight, rectangular_peg_hole_8mm_loose,
# rectangular_peg_hole_12mm_tight, rectangular_peg_hole_12mm_loose,
# rectangular_peg_hole_16mm_tight, rectangular_peg_hole_16mm_loose,
# bnc, dsub, usb}
#
# NOTE: BNC, D-sub, and USB are currently unavailable while we await approval from manufacturers.
| 1,529 | YAML | 41.499999 | 122 | 0.626553 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/Ingenuity.yaml | # used to create the object
name: Ingenuity
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 2.5
maxEpisodeLength: 2000
enableDebugVis: False
clipObservations: 5.0
clipActions: 1.0
# set to True if you use camera sensors in the environment
enableCameraSensors: False
sim:
dt: 0.01
substeps: 2
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 6
num_velocity_iterations: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 1000.0
default_buffer_size_multiplier: 5.0
max_gpu_contact_pairs: 1048576 # 1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 0 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
| 1,184 | YAML | 26.558139 | 171 | 0.673986 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/Quadcopter.yaml | # used to create the object
name: Quadcopter
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:8192,${...num_envs}}
envSpacing: 1.25
maxEpisodeLength: 500
enableDebugVis: False
clipObservations: 5.0
clipActions: 1.0
# set to True if you use camera sensors in the environment
enableCameraSensors: False
sim:
dt: 0.01
substeps: 2
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 4
num_velocity_iterations: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 1000.0
default_buffer_size_multiplier: 5.0
max_gpu_contact_pairs: 1048576 # 1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 0 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
| 1,189 | YAML | 27.333333 | 171 | 0.671993 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FactoryTaskNutBoltPlace.yaml | # See schema in factory_schema_config_task.py for descriptions of common parameters.
defaults:
- FactoryBase
- _self_
# - /factory_schema_config_task
name: FactoryTaskNutBoltPlace
physics_engine: ${..physics_engine}
sim:
disable_gravity: True
env:
numEnvs: ${resolve_default:128,${...num_envs}}
numObservations: 27
numActions: 12
num_gripper_move_sim_steps: 40 # number of timesteps to reserve for moving gripper before first step of episode
num_gripper_close_sim_steps: 50 # number of timesteps to reserve for closing gripper onto nut during each reset
randomize:
franka_arm_initial_dof_pos: [0.00871, -0.10368, -0.00794, -1.49139, -0.00083, 1.38774, 0.7861]
fingertip_midpoint_pos_initial: [0.0, 0.0, 0.2] # initial position of midpoint between fingertips above table
fingertip_midpoint_pos_noise: [0.2, 0.2, 0.1] # noise on fingertip pos
fingertip_midpoint_rot_initial: [3.1416, 0, 3.1416] # initial rotation of fingertips (Euler)
fingertip_midpoint_rot_noise: [0.3, 0.3, 1] # noise on rotation
nut_noise_pos_in_gripper: [0.0, 0.0, 0.01] # noise on nut position within gripper
nut_noise_rot_in_gripper: 0.0 # noise on nut rotation within gripper
bolt_pos_xy_initial: [0.0, 0.0] # initial XY position of nut on table
bolt_pos_xy_noise: [0.1, 0.1] # noise on nut position
rl:
pos_action_scale: [0.1, 0.1, 0.1]
rot_action_scale: [0.1, 0.1, 0.1]
force_action_scale: [1.0, 1.0, 1.0]
torque_action_scale: [1.0, 1.0, 1.0]
clamp_rot: True
clamp_rot_thresh: 1.0e-6
add_obs_bolt_tip_pos: False # add observation of bolt tip position
num_keypoints: 4 # number of keypoints used in reward
keypoint_scale: 0.5 # length of line of keypoints
keypoint_reward_scale: 1.0 # scale on keypoint-based reward
action_penalty_scale: 0.0 # scale on action penalty
max_episode_length: 200
close_error_thresh: 0.1 # threshold below which nut is considered close enough to bolt
success_bonus: 0.0 # bonus if nut is close enough to bolt
ctrl:
ctrl_type: joint_space_id # {gym_default,
# joint_space_ik, joint_space_id,
# task_space_impedance, operational_space_motion,
# open_loop_force, closed_loop_force,
# hybrid_force_motion}
all:
jacobian_type: geometric
gripper_prop_gains: [100, 100]
gripper_deriv_gains: [2, 2]
gym_default:
ik_method: dls
joint_prop_gains: [40, 40, 40, 40, 40, 40, 40]
joint_deriv_gains: [8, 8, 8, 8, 8, 8, 8]
gripper_prop_gains: [500, 500]
gripper_deriv_gains: [20, 20]
joint_space_ik:
ik_method: dls
joint_prop_gains: [1, 1, 1, 1, 1, 1, 1]
joint_deriv_gains: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
joint_space_id:
ik_method: dls
joint_prop_gains: [40, 40, 40, 40, 40, 40, 40]
joint_deriv_gains: [8, 8, 8, 8, 8, 8, 8]
task_space_impedance:
motion_ctrl_axes: [1, 1, 1, 1, 1, 1]
task_prop_gains: [40, 40, 40, 40, 40, 40]
task_deriv_gains: [8, 8, 8, 8, 8, 8]
operational_space_motion:
motion_ctrl_axes: [1, 1, 1, 1, 1, 1]
task_prop_gains: [1, 1, 1, 1, 1, 1]
task_deriv_gains: [1, 1, 1, 1, 1, 1]
open_loop_force:
force_ctrl_axes: [0, 0, 1, 0, 0, 0]
closed_loop_force:
force_ctrl_axes: [0, 0, 1, 0, 0, 0]
wrench_prop_gains: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
hybrid_force_motion:
motion_ctrl_axes: [1, 1, 0, 1, 1, 1]
task_prop_gains: [40, 40, 40, 40, 40, 40]
task_deriv_gains: [8, 8, 8, 8, 8, 8]
force_ctrl_axes: [0, 0, 1, 0, 0, 0]
wrench_prop_gains: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
| 3,827 | YAML | 37.666666 | 116 | 0.593154 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/HumanoidAMPHands.yaml | # used to create the object
name: HumanoidAMP
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 5
episodeLength: 300
cameraFollow: True # if the camera follows humanoid or not
enableDebugVis: False
pdControl: True
powerScale: 1.0
controlFrequencyInv: 2 # 30 Hz
stateInit: "Random"
hybridInitProb: 0.5
numAMPObsSteps: 2
localRootObs: False
contactBodies: ["right_foot", "left_foot", "right_hand", "left_hand"]
terminationHeight: 0.5
enableEarlyTermination: True
# animation files to learn from
motion_file: "amp_humanoid_cartwheel.npy"
asset:
assetFileName: "mjcf/amp_humanoid.xml"
plane:
staticFriction: 1.0
dynamicFriction: 1.0
restitution: 0.0
sim:
dt: 0.0166 # 1/60 s
substeps: 2
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 4
num_velocity_iterations: 0
contact_offset: 0.02
rest_offset: 0.0
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 10.0
default_buffer_size_multiplier: 5.0
max_gpu_contact_pairs: 8388608 # 8*1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 1 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
randomization_params:
# specify which attributes to randomize for each actor type and property
frequency: 600 # Define how many environment steps between generating new randomizations
observations:
range: [0, .002] # range for the white noise
operation: "additive"
distribution: "gaussian"
actions:
range: [0., .02]
operation: "additive"
distribution: "gaussian"
sim_params:
gravity:
range: [0, 0.4]
operation: "additive"
distribution: "gaussian"
schedule: "linear" # "linear" will linearly interpolate between no rand and max rand
schedule_steps: 3000
actor_params:
humanoid:
color: True
rigid_body_properties:
mass:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
setup_only: True # Property will only be randomized once before simulation is started. See Domain Randomization Documentation for more info.
schedule: "linear" # "linear" will linearly interpolate between no rand and max rand
schedule_steps: 3000
rigid_shape_properties:
friction:
num_buckets: 500
range: [0.7, 1.3]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
restitution:
range: [0., 0.7]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
dof_properties:
damping:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
stiffness:
range: [0.5, 1.5]
operation: "scaling"
distribution: "uniform"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
lower:
range: [0, 0.01]
operation: "additive"
distribution: "gaussian"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
upper:
range: [0, 0.01]
operation: "additive"
distribution: "gaussian"
schedule: "linear" # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
schedule_steps: 3000
| 4,604 | YAML | 34.697674 | 171 | 0.620765 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FactoryTaskGears.yaml | # See schema in factory_schema_config_task.py for descriptions of common parameters.
defaults:
- FactoryBase
- _self_
# - /factory_schema_config_task
name: FactoryTaskGears
physics_engine: ${..physics_engine}
env:
numEnvs: ${resolve_default:128,${...num_envs}}
numObservations: 32
numActions: 12
randomize:
joint_noise: 0.0 # noise on Franka DOF positions [deg]
initial_state: random # initialize gears in random state or goal state {random, goal}
gears_bias_y: -0.1 # if random, Y-axis offset of gears during each reset to prevent initial interpenetration with base plate
gears_bias_z: 0.0 # if random, Z-axis offset of gears during each reset to prevent initial interpenetration with ground plane
gears_noise_xy: 0.05 # if random, XY-axis noise on gears during each reset
rl:
max_episode_length: 1024
| 861 | YAML | 33.479999 | 130 | 0.715447 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/IndustRealEnvPegs.yaml | # See schema in factory_schema_config_env.py for descriptions of common parameters.
defaults:
- IndustRealBase
- _self_
- /factory_schema_config_env
env:
env_name: 'IndustRealEnvPegs'
desired_subassemblies: ['round_peg_hole_8mm',
'round_peg_hole_12mm',
'round_peg_hole_16mm',
'rectangular_peg_hole_8mm',
'rectangular_peg_hole_12mm',
'rectangular_peg_hole_16mm']
plug_lateral_offset: 0.1 # Y-axis offset of plug before initial reset to prevent initial interpenetration with socket
# Density and friction values are specified in industreal_asset_info_pegs.yaml
| 736 | YAML | 35.849998 | 122 | 0.592391 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/FactoryEnvNutBolt.yaml | # See schema in factory_schema_config_env.py for descriptions of common parameters.
defaults:
- FactoryBase
- _self_
- /factory_schema_config_env
sim:
disable_franka_collisions: False
disable_nut_collisions: False
disable_bolt_collisions: False
env:
env_name: 'FactoryEnvNutBolt'
desired_subassemblies: ['nut_bolt_m16_tight', 'nut_bolt_m16_loose']
nut_lateral_offset: 0.1 # Y-axis offset of nut before initial reset to prevent initial interpenetration with bolt
nut_bolt_density: 7850.0
nut_bolt_friction: 0.3
# Subassembly options:
# {nut_bolt_m4_tight, nut_bolt_m4_loose,
# nut_bolt_m8_tight, nut_bolt_m8_loose,
# nut_bolt_m12_tight, nut_bolt_m12_loose,
# nut_bolt_m16_tight, nut_bolt_m16_loose,
# nut_bolt_m20_tight, nut_bolt_m20_loose} | 814 | YAML | 29.185184 | 118 | 0.692875 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/AntSAC.yaml | # used to create the object
defaults:
- Ant
- _self_
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:64,${...num_envs}} | 163 | YAML | 19.499998 | 52 | 0.668712 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/Cartpole.yaml | # used to create the object
name: Cartpole
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:512,${...num_envs}}
envSpacing: 4.0
resetDist: 3.0
maxEffort: 400.0
clipObservations: 5.0
clipActions: 1.0
asset:
assetRoot: "../../assets"
assetFileName: "urdf/cartpole.urdf"
# set to True if you use camera sensors in the environment
enableCameraSensors: False
sim:
dt: 0.0166 # 1/60 s
substeps: 2
up_axis: "z"
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
num_position_iterations: 4
num_velocity_iterations: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 100.0
default_buffer_size_multiplier: 2.0
max_gpu_contact_pairs: 1048576 # 1024*1024
num_subscenes: ${....num_subscenes}
contact_collection: 0 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (broken - do not use!)
task:
randomize: False
| 1,259 | YAML | 26.391304 | 171 | 0.663225 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/ShadowHandOpenAI_LSTM.yaml | # specifies what the config is when running `ShadowHandOpenAI` in LSTM mode
defaults:
- ShadowHandOpenAI_FF
- _self_
env:
numEnvs: ${resolve_default:8192,${...num_envs}}
| 178 | YAML | 18.888887 | 75 | 0.707865 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/env/regrasping.yaml | subtask: "regrasping"
episodeLength: 300
# requires holding a grasp for a whole second, thus trained policies develop a robust grasp
successSteps: 30
| 152 | YAML | 20.85714 | 91 | 0.796053 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/task/env/throw.yaml | subtask: "throw"
episodeLength: 300
forceScale: 0.0 # random forces don't allow us to throw precisely so we turn them off
# curriculum not needed - if we hit a bin, that's good!
successTolerance: 0.075
targetSuccessTolerance: 0.075
# adds a small pause every time we hit a target
successSteps: 5
# throwing big objects is hard and they don't fit in the bin, so focus on randomized but smaller objects
withSmallCuboids: True
withBigCuboids: False
withSticks: False
| 470 | YAML | 25.166665 | 104 | 0.774468 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/FactoryTaskGearsPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False}
load_path: ${...checkpoint}
config:
name: ${resolve_default:FactoryTaskGears,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 1e-4
lr_schedule: fixed
schedule_type: standard
kl_threshold: 0.016
score_to_win: 20000
max_epochs: ${resolve_default:8192,${....max_iterations}}
save_best_after: 50
save_frequency: 100
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: False
e_clip: 0.2
horizon_length: 32
minibatch_size: 512 # batch size = num_envs * horizon_length; minibatch_size = batch_size / num_minibatches
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
| 1,624 | YAML | 21.569444 | 112 | 0.600985 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/ShadowHandOpenAI_FFPPO.yaml | # specifies what the default training mode is when
# running `ShadowHandOpenAI_FF` (version with DR and asymmetric observations and feedforward network)
# (currently defaults to asymmetric training)
defaults:
- ShadowHandPPOAsymm
- _self_
| 243 | YAML | 33.857138 | 101 | 0.790123 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/AnymalTerrainPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: True
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0. # std = 1.
fixed_sigma: True
mlp:
units: [512, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
# rnn:
# name: lstm
# units: 128
# layers: 1
# before_mlp: True
# concat_input: True
# layer_norm: False
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:AnymalTerrain,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
ppo: True
multi_gpu: ${....multi_gpu}
mixed_precision: True
normalize_input: True
normalize_value: True
normalize_advantage: True
value_bootstrap: True
clip_actions: False
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
gamma: 0.99
tau: 0.95
e_clip: 0.2
entropy_coef: 0.001
learning_rate: 3.e-4 # overwritten by adaptive lr_schedule
lr_schedule: adaptive
kl_threshold: 0.008 # target kl for adaptive lr
truncate_grads: True
grad_norm: 1.
horizon_length: 24
minibatch_size: 16384
mini_epochs: 5
critic_coef: 2
clip_value: True
seq_len: 4 # only for rnn
bounds_loss_coef: 0.
max_epochs: ${resolve_default:1500,${....max_iterations}}
save_best_after: 100
score_to_win: 20000
save_frequency: 50
print_stats: True
| 1,854 | YAML | 21.349397 | 101 | 0.59493 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/AllegroKukaTwoArmsLSTMPPO.yaml | defaults:
- AllegroKukaLSTMPPO
- _self_
# TODO: try bigger network for two hands?
params:
network:
mlp:
units: [768, 512, 256]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 768
layers: 1
before_mlp: True
layer_norm: True
config:
name: ${resolve_default:AllegroKukaTwoArmsLSTMPPO,${....experiment}}
minibatch_size: 32768
mini_epochs: 2 | 496 | YAML | 18.115384 | 72 | 0.592742 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/HumanoidPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [400, 200, 100]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:Humanoid,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 5e-4
lr_schedule: adaptive
kl_threshold: 0.008
score_to_win: 20000
max_epochs: ${resolve_default:1000,${....max_iterations}}
save_best_after: 200
save_frequency: 100
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
ppo: True
e_clip: 0.2
horizon_length: 32
minibatch_size: 32768
mini_epochs: 5
critic_coef: 4
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001 | 1,589 | YAML | 21.083333 | 101 | 0.600378 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/AllegroHandDextremeManualDRPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
inputs:
dof_pos_randomized: { }
object_pose_cam_randomized: { }
goal_pose_randomized: { }
goal_relative_rot_cam_randomized: { }
last_actions_randomized: { }
mlp:
units: [256]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 512
layers: 1
before_mlp: True
layer_norm: True
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:AllegroHandManualDRAsymmLSTM,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
value_bootstrap: False
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.998
tau: 0.95
learning_rate: 1e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 100000
max_epochs: ${resolve_default:50000,${....max_iterations}}
save_best_after: 200
save_frequency: 500
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 4
critic_coef: 4
clip_value: True
seq_length: 16
bounds_loss_coef: 0.0001
zero_rnn_on_done: False
central_value_config:
minibatch_size: 16384
mini_epochs: 4
learning_rate: 1e-4
kl_threshold: 0.016
clip_value: True
normalize_input: True
truncate_grads: True
network:
name: actor_critic
central_value: True
inputs:
dof_pos: { }
dof_vel: { }
dof_force: { }
object_pose: { }
object_pose_cam_randomized: { }
object_vels: { }
goal_pose: { }
goal_relative_rot: {}
last_actions: { }
ft_force_torques: {}
gravity_vec: {}
ft_states: {}
mlp:
units: [512, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
player:
deterministic: True
use_vecenv: True
games_num: 1000000
print_stats: False
| 2,909 | YAML | 20.555555 | 101 | 0.55758 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/IndustRealTaskPegsInsertPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [512, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 256
layers: 2
before_mlp: True
concat_input: True
layer_norm: False
load_checkpoint: ${if:${...checkpoint},True,False}
load_path: ${...checkpoint}
config:
name: ${resolve_default:IndustRealTaskPegsInsert,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: False
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.998
tau: 0.95
learning_rate: 1e-3
lr_schedule: linear
schedule_type: standard
kl_threshold: 0.016
score_to_win: 200000
max_epochs: ${resolve_default:8192,${....max_iterations}}
save_best_after: 10
save_frequency: 100
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: False
e_clip: 0.2
horizon_length: 128
minibatch_size: 8192 # batch size = num_envs * horizon_length; minibatch_size = batch_size / num_minibatches
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_len: 8
bounds_loss_coef: 0.0001
central_value_config:
minibatch_size: 256
mini_epochs: 4
learning_rate: 1e-3
lr_schedule: linear
kl_threshold: 0.016
clip_value: True
normalize_input: True
truncate_grads: True
network:
name: actor_critic
central_value: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
# rnn:
# name: lstm
# units: 256
# layers: 2
# before_mlp: True
# concat_input: True
# layer_norm: False
| 2,434 | YAML | 20.359649 | 113 | 0.566557 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/ShadowHandPPOAsymmLSTM.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [512]
activation: relu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 1024
layers: 1
before_mlp: True
layer_norm: True
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:ShadowHandAsymmLSTM,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.998
tau: 0.95
learning_rate: 1e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 100000
max_epochs: ${resolve_default:10000,${....max_iterations}}
save_best_after: 500
save_frequency: 500
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 4
critic_coef: 4
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
central_value_config:
minibatch_size: 32768
mini_epochs: 4
learning_rate: 1e-4
kl_threshold: 0.016
clip_value: True
normalize_input: True
truncate_grads: True
network:
name: actor_critic
central_value: True
mlp:
units: [512]
activation: relu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 1024
layers: 1
before_mlp: True
layer_norm: True
player:
#render: True
deterministic: True
games_num: 1000000
print_stats: False | 2,410 | YAML | 21.119266 | 101 | 0.570954 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/FactoryTaskInsertionPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False}
load_path: ${...checkpoint}
config:
name: ${resolve_default:FactoryTaskInsertion,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 1e-4
lr_schedule: fixed
schedule_type: standard
kl_threshold: 0.016
score_to_win: 20000
max_epochs: ${resolve_default:8192,${....max_iterations}}
save_best_after: 50
save_frequency: 100
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: False
e_clip: 0.2
horizon_length: 32
minibatch_size: 512 # batch size = num_envs * horizon_length; minibatch_size = batch_size / num_minibatches
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
| 1,628 | YAML | 21.625 | 112 | 0.601966 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/AllegroHandDextremeADRPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
inputs:
dof_pos_randomized: {}
object_pose_cam_randomized: { }
goal_pose: { }
goal_relative_rot_cam_randomized: { }
last_actions: { }
mlp:
units: [512, 512]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 1024
layers: 1
before_mlp: True
layer_norm: True
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:AllegroHandADRAsymmLSTM,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
value_bootstrap: False
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.998
tau: 0.95
learning_rate: 1e-4
lr_schedule: linear #adaptive
schedule_type: standard
kl_threshold: 0.01
score_to_win: 1000000
max_epochs: ${resolve_default:1000_000,${....max_iterations}}
save_best_after: 10000
save_frequency: 500
print_stats: True
grad_norm: 1.0
entropy_coef: 0.002
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 4
critic_coef: 4
clip_value: True
seq_length: 16
bound_loss_type: regularization
bounds_loss_coef: 0.005
zero_rnn_on_done: False
# optimize summaries to prevent tf.event files from growing to gigabytes
force_interval_writer: True
central_value_config:
minibatch_size: 16384
mini_epochs: 4
learning_rate: 5e-5
kl_threshold: 0.016
clip_value: True
normalize_input: True
truncate_grads: True
network:
name: actor_critic
central_value: True
inputs:
dof_pos: { }
dof_vel: { }
dof_force: { }
object_pose: { }
object_pose_cam_randomized: { }
object_vels: { }
goal_pose: { }
goal_relative_rot: {}
last_actions: { }
stochastic_delay_params: { }
affine_params: { }
cube_random_params: {}
hand_random_params: {}
ft_force_torques: {}
gravity_vec: {}
ft_states: {}
rot_dist: {}
rb_forces: {}
mlp:
units: [1024, 512]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 2048
layers: 1
before_mlp: True
layer_norm: True
player:
deterministic: True
use_vecenv: True
games_num: 1000000
print_stats: False
| 3,354 | YAML | 21.366667 | 101 | 0.552475 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/AllegroKukaPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [1024, 1024, 512, 512]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:AllegroKukaPPO,${....experiment}}
# full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
normalize_advantage: True
reward_shaper:
scale_value: 0.01
num_actors: ${....task.env.numEnvs}
gamma: 0.99
tau: 0.95
learning_rate: 1e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 1000000
max_epochs: 100000
max_frames: 10_000_000_000
save_best_after: 100
save_frequency: 5000
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.1
minibatch_size: 32768
mini_epochs: 4
critic_coef: 4.0
clip_value: True
horizon_length: 16
seq_length: 16
# SampleFactory currently gives better results without bounds loss but I don't think this loss matters too much
# bounds_loss_coef: 0.0
bounds_loss_coef: 0.0001
# optimize summaries to prevent tf.event files from growing to gigabytes
defer_summaries_sec: ${if:${....pbt},240,5}
summaries_interval_sec_min: ${if:${....pbt},60,5}
summaries_interval_sec_max: 300
player:
#render: True
deterministic: False # be careful there's a typo in older versions of rl_games in this parameter name ("determenistic")
games_num: 100000
print_stats: False
| 2,180 | YAML | 23.784091 | 126 | 0.623853 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/ShadowHandPPOAsymm.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [400, 400, 200, 100]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:ShadowHandAsymm,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
reward_shaper:
scale_value: 0.01
normalize_advantage: True
num_actors: ${....task.env.numEnvs}
gamma: 0.99
tau: 0.95
learning_rate: 5e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 100000
max_epochs: ${resolve_default:10000,${....max_iterations}}
save_best_after: 500
save_frequency: 200
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 8
minibatch_size: 16384
mini_epochs: 8
critic_coef: 4
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
central_value_config:
minibatch_size: 16384
mini_epochs: 8
learning_rate: 5e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
clip_value: True
normalize_input: True
truncate_grads: True
network:
name: actor_critic
central_value: True
mlp:
units: [512, 512, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
player:
#render: True
deterministic: True
games_num: 1000000
print_stats: False
| 2,243 | YAML | 21.217822 | 101 | 0.586268 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/ShadowHandPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [512, 512, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:ShadowHand,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 5e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 100000
max_epochs: ${resolve_default:5000,${....max_iterations}}
save_best_after: 100
save_frequency: 200
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 8
minibatch_size: 32768
mini_epochs: 5
critic_coef: 4
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
player:
#render: True
deterministic: True
games_num: 100000
print_stats: True | 1,732 | YAML | 20.936709 | 101 | 0.601039 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/AllegroHandLSTM_BigPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: complex_net
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
inputs:
dof_pos_offset_randomized: {}
object_pose_delayed_randomized: {}
goal_pose: {}
goal_relative_rot_delayed_randomized: {}
last_actions: {}
mlp:
units: [512]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 1024
layers: 1
before_mlp: True
layer_norm: True
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:AllegroHandAsymmLSTM,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
use_smooth_clamp: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.998
tau: 0.95
learning_rate: 1e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 100000
max_epochs: ${resolve_default:1000000,${....max_iterations}}
save_best_after: 200
save_frequency: 500
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 4
critic_coef: 4
clip_value: True
seq_length: 16
bounds_loss_coef: 0.0001
central_value_config:
minibatch_size: 16384 #32768
mini_epochs: 4
learning_rate: 1e-4
kl_threshold: 0.016
clip_value: True
normalize_input: True
truncate_grads: True
use_smooth_clamp: True
network:
name: complex_net
central_value: True
inputs:
dof_pos: {}
dof_pos_offset_randomized: {}
dof_vel: {}
dof_torque: {}
object_pose: {}
object_vels: {}
goal_pose: {}
goal_relative_rot: {}
object_pose_delayed_randomized: {}
goal_relative_rot_delayed_randomized: {}
object_obs_delayed_age: {}
# ft_states: {}
# ft_force_torques: {}
last_actions: {}
mlp:
units: [512, 512, 256, 128] #[256] #
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
# rnn:
# name: lstm
# units: 512
# layers: 1
# before_mlp: True
# layer_norm: True
player:
deterministic: True
use_vecenv: True
games_num: 1000000
print_stats: False
| 3,152 | YAML | 22.183823 | 101 | 0.551713 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/AllegroHandLSTMPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
inputs:
dof_pos_randomized: { }
object_pose_cam_randomized: { }
goal_pose_randomized: { }
goal_relative_rot_cam_randomized: { }
last_actions_randomized: { }
mlp:
units: [256]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 512
layers: 1
before_mlp: True
layer_norm: True
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:AllegroHandAsymmLSTM,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
value_bootstrap: False # TODO: enable bootstrap?
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.998
tau: 0.95
learning_rate: 1e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 100000
max_epochs: ${resolve_default:50000,${....max_iterations}}
save_best_after: 200
save_frequency: 500
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 4
critic_coef: 4
clip_value: True
seq_length: 16
bounds_loss_coef: 0.0001
# optimize summaries to prevent tf.event files from growing to gigabytes
defer_summaries_sec: ${if:${....pbt},150,5}
summaries_interval_sec_min: ${if:${....pbt},20,5}
summaries_interval_sec_max: 100
central_value_config:
minibatch_size: 16384 #32768
mini_epochs: 4
learning_rate: 1e-4
kl_threshold: 0.016
clip_value: True
normalize_input: True
truncate_grads: True
network:
name: actor_critic
central_value: True
inputs:
dof_pos: { }
dof_vel: { }
dof_force: { }
object_pose: { }
object_pose_cam_randomized: { }
object_vels: { }
goal_pose: { }
goal_relative_rot: {}
last_actions: { }
ft_force_torques: {}
gravity_vec: {}
ft_states: {}
mlp:
units: [512, 256, 128] #[256] #
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 512
layers: 1
before_mlp: True
layer_norm: True
player:
deterministic: True
use_vecenv: True
games_num: 1000000
print_stats: False
| 3,269 | YAML | 21.39726 | 101 | 0.556133 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/HumanoidAMPPPO.yaml | params:
seed: ${...seed}
algo:
name: amp_continuous
model:
name: continuous_amp
network:
name: amp
separate: True
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: -2.9
fixed_sigma: True
learn_sigma: False
mlp:
units: [1024, 512]
activation: relu
d2rl: False
initializer:
name: default
regularizer:
name: None
disc:
units: [1024, 512]
activation: relu
initializer:
name: default
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:HumanoidAMP,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
ppo: True
multi_gpu: ${....multi_gpu}
mixed_precision: False
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 5e-5
lr_schedule: constant
kl_threshold: 0.008
score_to_win: 20000
max_epochs: ${resolve_default:5000,${....max_iterations}}
save_best_after: 100
save_frequency: 50
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: False
e_clip: 0.2
horizon_length: 16
minibatch_size: 32768
mini_epochs: 6
critic_coef: 5
clip_value: False
seq_len: 4
bounds_loss_coef: 10
amp_obs_demo_buffer_size: 200000
amp_replay_buffer_size: 1000000
amp_replay_keep_prob: 0.01
amp_batch_size: 512
amp_minibatch_size: 4096
disc_coef: 5
disc_logit_reg: 0.05
disc_grad_penalty: 5
disc_reward_scale: 2
disc_weight_decay: 0.0001
normalize_amp_input: True
task_reward_w: 0.0
disc_reward_w: 1.0
| 2,055 | YAML | 20.642105 | 101 | 0.60146 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/AnymalTerrainPPO_LSTM.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: True
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0. # std = 1.
fixed_sigma: True
mlp:
units: [512] #, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 256 #128
layers: 1
before_mlp: False #True
concat_input: True
layer_norm: False
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:AnymalTerrain,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
normalize_advantage: True
value_bootstrap: True
clip_actions: False
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
gamma: 0.99
tau: 0.95
e_clip: 0.2
entropy_coef: 0.001
learning_rate: 3.e-4 # overwritten by adaptive lr_schedule
lr_schedule: adaptive
kl_threshold: 0.008 # target kl for adaptive lr
truncate_grads: True
grad_norm: 1.
horizon_length: 24
minibatch_size: 16384
mini_epochs: 5
critic_coef: 2
clip_value: True
seq_len: 4 # only for rnn
bounds_loss_coef: 0.
max_epochs: ${resolve_default:750,${....max_iterations}}
save_best_after: 100
score_to_win: 20000
save_frequency: 50
print_stats: True
| 1,854 | YAML | 21.349397 | 101 | 0.598706 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/FrankaCubeStackPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:FrankaCubeStack,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 5e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.008
score_to_win: 10000
max_epochs: ${resolve_default:10000,${....max_iterations}}
save_best_after: 200
save_frequency: 100
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 32
minibatch_size: 16384
mini_epochs: 5
critic_coef: 4
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
| 1,623 | YAML | 21.555555 | 101 | 0.604436 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/HumanoidSAC.yaml | params:
seed: ${...seed}
algo:
name: sac
model:
name: soft_actor_critic
network:
name: soft_actor_critic
separate: True
space:
continuous:
mlp:
units: [512, 256]
activation: relu
initializer:
name: default
log_std_bounds: [-5, 2]
load_checkpoint: False
load_path: nn/Ant.pth
config:
name: ${resolve_default:HumanoidSAC,${....experiment}}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
normalize_input: True
reward_shaper:
scale_value: 1.0
max_epochs: 50000
num_steps_per_episode: 8
save_best_after: 100
save_frequency: 1000
gamma: 0.99
init_alpha: 1.0
alpha_lr: 0.005
actor_lr: 0.0005
critic_lr: 0.0005
critic_tau: 0.005
batch_size: 4096
learnable_temperature: true
num_seed_steps: 5
num_warmup_steps: 10
replay_buffer_size: 1000000
num_actors: ${....task.env.numEnvs}
| 945 | YAML | 17.92 | 58 | 0.594709 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/ShadowHandOpenAI_LSTMPPO.yaml | # specifies what the default training mode is when
# running `ShadowHandOpenAI_LSTM` (version with DR and asymmetric observations, and LSTM)
defaults:
- ShadowHandPPOAsymmLSTM
- _self_
| 189 | YAML | 30.666662 | 89 | 0.777778 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/IngenuityPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:Ingenuity,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 1e-3
lr_schedule: adaptive
kl_threshold: 0.016
score_to_win: 20000
max_epochs: ${resolve_default:500,${....max_iterations}}
save_best_after: 50
save_frequency: 50
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
| 1,546 | YAML | 21.1 | 101 | 0.595731 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/QuadcopterPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:Quadcopter,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.1
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 1e-3
lr_schedule: adaptive
kl_threshold: 0.016
score_to_win: 20000
max_epochs: ${resolve_default:500,${....max_iterations}}
save_best_after: 50
save_frequency: 50
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 8
minibatch_size: 16384
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
| 1,545 | YAML | 21.085714 | 101 | 0.595469 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/FactoryTaskNutBoltScrewPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False}
load_path: ${...checkpoint}
config:
name: ${resolve_default:FactoryTaskNutBoltScrew,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 1.0e-4
lr_schedule: fixed
schedule_type: standard
kl_threshold: 0.016
score_to_win: 20000
max_epochs: ${resolve_default:1024,${....max_iterations}}
save_best_after: 50
save_frequency: 100
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: False
e_clip: 0.2
horizon_length: 128
minibatch_size: 512 # batch size = num_envs * horizon_length; minibatch_size = batch_size / num_minibatches
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
| 1,634 | YAML | 21.708333 | 112 | 0.602815 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/ShadowHandPPOLSTM.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [512, 512, 256]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 256
layers: 1
before_mlp: False
concat_input: True
layer_norm: True
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:ShadowHandLSTM,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 5e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 100000
save_best_after: 500
save_frequency: 100
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 32
minibatch_size: 16384
mini_epochs: ${resolve_default:4,${....max_iterations}}
critic_coef: 4
clip_value: False
seq_len: 4
bounds_loss_coef: 0.0001
player:
#render: True
deterministic: True
games_num: 100000
print_stats: True | 1,818 | YAML | 20.4 | 101 | 0.594609 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/BallBalancePPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [128, 64, 32]
activation: elu
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:BallBalance,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.1
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 3e-4
lr_schedule: adaptive
kl_threshold: 0.008
score_to_win: 20000
max_epochs: ${resolve_default:250,${....max_iterations}}
save_best_after: 50
save_frequency: 100
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 8192
mini_epochs: 8
critic_coef: 4
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001 | 1,526 | YAML | 21.455882 | 101 | 0.596986 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/FactoryTaskNutBoltPlacePPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False}
load_path: ${...checkpoint}
config:
name: ${resolve_default:FactoryTaskNutBoltPlace,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 1e-4
lr_schedule: fixed
schedule_type: standard
kl_threshold: 0.016
score_to_win: 20000
max_epochs: ${resolve_default:1024,${....max_iterations}}
save_best_after: 50
save_frequency: 100
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: False
e_clip: 0.2
horizon_length: 120
minibatch_size: 512
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
| 1,543 | YAML | 20.444444 | 70 | 0.595593 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/IndustRealTaskGearsInsertPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [512, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 256
layers: 2
before_mlp: True
concat_input: True
layer_norm: False
load_checkpoint: ${if:${...checkpoint},True,False}
load_path: ${...checkpoint}
config:
name: ${resolve_default:IndustRealTaskGearsInsert,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: False
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.998
tau: 0.95
learning_rate: 1e-4
lr_schedule: linear
schedule_type: standard
kl_threshold: 0.008
score_to_win: 20000
max_epochs: ${resolve_default:8192,${....max_iterations}}
save_best_after: 50
save_frequency: 1000
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: False
e_clip: 0.2
horizon_length: 128
minibatch_size: 8 # batch size = num_envs * horizon_length; minibatch_size = batch_size / num_minibatches
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
central_value_config:
minibatch_size: 8
mini_epochs: 4
learning_rate: 1e-3
lr_schedule: linear
kl_threshold: 0.016
clip_value: True
normalize_input: True
truncate_grads: True
network:
name: actor_critic
central_value: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
| 2,254 | YAML | 20.682692 | 110 | 0.579858 |
NVIDIA-Omniverse/IsaacGymEnvs/isaacgymenvs/cfg/train/AntPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:Ant,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 3e-4
lr_schedule: adaptive
schedule_type: legacy
kl_threshold: 0.008
score_to_win: 20000
max_epochs: ${resolve_default:500,${....max_iterations}}
save_best_after: 200
save_frequency: 50
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: False
e_clip: 0.2
horizon_length: 16
minibatch_size: 32768
mini_epochs: 4
critic_coef: 2
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
| 1,592 | YAML | 21.125 | 101 | 0.597362 |
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