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fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/lift/config/franka/agents/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from . import rsl_rl_cfg # noqa: F401, F403
| 168 | Python | 23.142854 | 56 | 0.720238 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/manipulation/lift/config/franka/agents/rl_games_ppo_cfg.yaml | params:
seed: 42
# environment wrapper clipping
env:
clip_observations: 100.0
clip_actions: 100.0
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: False # flag which sets whether to load the checkpoint
load_path: '' # path to the checkpoint to load
config:
name: reach
env_name: rlgpu
device: 'cuda:0'
device_name: 'cuda:0'
multi_gpu: False
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
value_bootstrap: False
num_actors: -1
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: 100000000
max_epochs: 500
save_best_after: 100
save_frequency: 50
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 4096 #2048
mini_epochs: 8
critic_coef: 4
clip_value: True
seq_len: 4
bounds_loss_coef: 0.0001
| 1,566 | YAML | 18.835443 | 73 | 0.605364 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Locomotion environments for legged robots."""
from .velocity import * # noqa
| 205 | Python | 21.888886 | 56 | 0.731707 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/velocity_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import math
from dataclasses import MISSING
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg
from omni.isaac.orbit.envs import RLTaskEnvCfg
from omni.isaac.orbit.managers import CurriculumTermCfg as CurrTerm
from omni.isaac.orbit.managers import EventTermCfg as EventTerm
from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup
from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm
from omni.isaac.orbit.managers import RewardTermCfg as RewTerm
from omni.isaac.orbit.managers import SceneEntityCfg
from omni.isaac.orbit.managers import TerminationTermCfg as DoneTerm
from omni.isaac.orbit.scene import InteractiveSceneCfg
from omni.isaac.orbit.sensors import ContactSensorCfg, RayCasterCfg, patterns
from omni.isaac.orbit.terrains import TerrainImporterCfg
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit.utils.noise import AdditiveUniformNoiseCfg as Unoise
import omni.isaac.orbit_tasks.locomotion.velocity.mdp as mdp
##
# Pre-defined configs
##
from omni.isaac.orbit.terrains.config.rough import ROUGH_TERRAINS_CFG # isort: skip
##
# Scene definition
##
@configclass
class MySceneCfg(InteractiveSceneCfg):
"""Configuration for the terrain scene with a legged robot."""
# ground terrain
terrain = TerrainImporterCfg(
prim_path="/World/ground",
terrain_type="generator",
terrain_generator=ROUGH_TERRAINS_CFG,
max_init_terrain_level=5,
collision_group=-1,
physics_material=sim_utils.RigidBodyMaterialCfg(
friction_combine_mode="multiply",
restitution_combine_mode="multiply",
static_friction=1.0,
dynamic_friction=1.0,
),
visual_material=sim_utils.MdlFileCfg(
mdl_path="{NVIDIA_NUCLEUS_DIR}/Materials/Base/Architecture/Shingles_01.mdl",
project_uvw=True,
),
debug_vis=False,
)
# robots
robot: ArticulationCfg = MISSING
# sensors
height_scanner = RayCasterCfg(
prim_path="{ENV_REGEX_NS}/Robot/base",
offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)),
attach_yaw_only=True,
pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]),
debug_vis=False,
mesh_prim_paths=["/World/ground"],
)
contact_forces = ContactSensorCfg(prim_path="{ENV_REGEX_NS}/Robot/.*", history_length=3, track_air_time=True)
# lights
light = AssetBaseCfg(
prim_path="/World/light",
spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0),
)
sky_light = AssetBaseCfg(
prim_path="/World/skyLight",
spawn=sim_utils.DomeLightCfg(color=(0.13, 0.13, 0.13), intensity=1000.0),
)
##
# MDP settings
##
@configclass
class CommandsCfg:
"""Command specifications for the MDP."""
base_velocity = mdp.UniformVelocityCommandCfg(
asset_name="robot",
resampling_time_range=(10.0, 10.0),
rel_standing_envs=0.02,
rel_heading_envs=1.0,
heading_command=True,
heading_control_stiffness=0.5,
debug_vis=True,
ranges=mdp.UniformVelocityCommandCfg.Ranges(
lin_vel_x=(-1.0, 1.0), lin_vel_y=(-1.0, 1.0), ang_vel_z=(-1.0, 1.0), heading=(-math.pi, math.pi)
),
)
@configclass
class ActionsCfg:
"""Action specifications for the MDP."""
joint_pos = mdp.JointPositionActionCfg(asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True)
@configclass
class ObservationsCfg:
"""Observation specifications for the MDP."""
@configclass
class PolicyCfg(ObsGroup):
"""Observations for policy group."""
# observation terms (order preserved)
base_lin_vel = ObsTerm(func=mdp.base_lin_vel, noise=Unoise(n_min=-0.1, n_max=0.1))
base_ang_vel = ObsTerm(func=mdp.base_ang_vel, noise=Unoise(n_min=-0.2, n_max=0.2))
projected_gravity = ObsTerm(
func=mdp.projected_gravity,
noise=Unoise(n_min=-0.05, n_max=0.05),
)
velocity_commands = ObsTerm(func=mdp.generated_commands, params={"command_name": "base_velocity"})
joint_pos = ObsTerm(func=mdp.joint_pos_rel, noise=Unoise(n_min=-0.01, n_max=0.01))
joint_vel = ObsTerm(func=mdp.joint_vel_rel, noise=Unoise(n_min=-1.5, n_max=1.5))
actions = ObsTerm(func=mdp.last_action)
height_scan = ObsTerm(
func=mdp.height_scan,
params={"sensor_cfg": SceneEntityCfg("height_scanner")},
noise=Unoise(n_min=-0.1, n_max=0.1),
clip=(-1.0, 1.0),
)
def __post_init__(self):
self.enable_corruption = True
self.concatenate_terms = True
# observation groups
policy: PolicyCfg = PolicyCfg()
@configclass
class EventCfg:
"""Configuration for events."""
# startup
physics_material = EventTerm(
func=mdp.randomize_rigid_body_material,
mode="startup",
params={
"asset_cfg": SceneEntityCfg("robot", body_names=".*"),
"static_friction_range": (0.8, 0.8),
"dynamic_friction_range": (0.6, 0.6),
"restitution_range": (0.0, 0.0),
"num_buckets": 64,
},
)
add_base_mass = EventTerm(
func=mdp.randomize_rigid_body_mass,
mode="startup",
params={"asset_cfg": SceneEntityCfg("robot", body_names="base"), "mass_range": (-5.0, 5.0), "operation": "add"},
)
# reset
base_external_force_torque = EventTerm(
func=mdp.apply_external_force_torque,
mode="reset",
params={
"asset_cfg": SceneEntityCfg("robot", body_names="base"),
"force_range": (0.0, 0.0),
"torque_range": (-0.0, 0.0),
},
)
reset_base = EventTerm(
func=mdp.reset_root_state_uniform,
mode="reset",
params={
"pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)},
"velocity_range": {
"x": (-0.5, 0.5),
"y": (-0.5, 0.5),
"z": (-0.5, 0.5),
"roll": (-0.5, 0.5),
"pitch": (-0.5, 0.5),
"yaw": (-0.5, 0.5),
},
},
)
reset_robot_joints = EventTerm(
func=mdp.reset_joints_by_scale,
mode="reset",
params={
"position_range": (0.5, 1.5),
"velocity_range": (0.0, 0.0),
},
)
# interval
push_robot = EventTerm(
func=mdp.push_by_setting_velocity,
mode="interval",
interval_range_s=(10.0, 15.0),
params={"velocity_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5)}},
)
@configclass
class RewardsCfg:
"""Reward terms for the MDP."""
# -- task
track_lin_vel_xy_exp = RewTerm(
func=mdp.track_lin_vel_xy_exp, weight=1.0, params={"command_name": "base_velocity", "std": math.sqrt(0.25)}
)
track_ang_vel_z_exp = RewTerm(
func=mdp.track_ang_vel_z_exp, weight=0.5, params={"command_name": "base_velocity", "std": math.sqrt(0.25)}
)
# -- penalties
lin_vel_z_l2 = RewTerm(func=mdp.lin_vel_z_l2, weight=-2.0)
ang_vel_xy_l2 = RewTerm(func=mdp.ang_vel_xy_l2, weight=-0.05)
dof_torques_l2 = RewTerm(func=mdp.joint_torques_l2, weight=-1.0e-5)
dof_acc_l2 = RewTerm(func=mdp.joint_acc_l2, weight=-2.5e-7)
action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-0.01)
feet_air_time = RewTerm(
func=mdp.feet_air_time,
weight=0.125,
params={
"sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*FOOT"),
"command_name": "base_velocity",
"threshold": 0.5,
},
)
undesired_contacts = RewTerm(
func=mdp.undesired_contacts,
weight=-1.0,
params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*THIGH"), "threshold": 1.0},
)
# -- optional penalties
flat_orientation_l2 = RewTerm(func=mdp.flat_orientation_l2, weight=0.0)
dof_pos_limits = RewTerm(func=mdp.joint_pos_limits, weight=0.0)
@configclass
class TerminationsCfg:
"""Termination terms for the MDP."""
time_out = DoneTerm(func=mdp.time_out, time_out=True)
base_contact = DoneTerm(
func=mdp.illegal_contact,
params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names="base"), "threshold": 1.0},
)
@configclass
class CurriculumCfg:
"""Curriculum terms for the MDP."""
terrain_levels = CurrTerm(func=mdp.terrain_levels_vel)
##
# Environment configuration
##
@configclass
class LocomotionVelocityRoughEnvCfg(RLTaskEnvCfg):
"""Configuration for the locomotion velocity-tracking environment."""
# Scene settings
scene: MySceneCfg = MySceneCfg(num_envs=4096, env_spacing=2.5)
# Basic settings
observations: ObservationsCfg = ObservationsCfg()
actions: ActionsCfg = ActionsCfg()
commands: CommandsCfg = CommandsCfg()
# MDP settings
rewards: RewardsCfg = RewardsCfg()
terminations: TerminationsCfg = TerminationsCfg()
events: EventCfg = EventCfg()
curriculum: CurriculumCfg = CurriculumCfg()
def __post_init__(self):
"""Post initialization."""
# general settings
self.decimation = 4
self.episode_length_s = 20.0
# simulation settings
self.sim.dt = 0.005
self.sim.disable_contact_processing = True
self.sim.physics_material = self.scene.terrain.physics_material
# update sensor update periods
# we tick all the sensors based on the smallest update period (physics update period)
if self.scene.height_scanner is not None:
self.scene.height_scanner.update_period = self.decimation * self.sim.dt
if self.scene.contact_forces is not None:
self.scene.contact_forces.update_period = self.sim.dt
# check if terrain levels curriculum is enabled - if so, enable curriculum for terrain generator
# this generates terrains with increasing difficulty and is useful for training
if getattr(self.curriculum, "terrain_levels", None) is not None:
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.curriculum = True
else:
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.curriculum = False
| 10,604 | Python | 32.666667 | 120 | 0.624953 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Locomotion environments with velocity-tracking commands.
These environments are based on the `legged_gym` environments provided by Rudin et al.
Reference:
https://github.com/leggedrobotics/legged_gym
"""
| 336 | Python | 24.923075 | 86 | 0.764881 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/mdp/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""This sub-module contains the functions that are specific to the locomotion environments."""
from omni.isaac.orbit.envs.mdp import * # noqa: F401, F403
from .curriculums import * # noqa: F401, F403
from .rewards import * # noqa: F401, F403
| 370 | Python | 29.916664 | 94 | 0.732432 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/mdp/curriculums.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Common functions that can be used to create curriculum for the learning environment.
The functions can be passed to the :class:`omni.isaac.orbit.managers.CurriculumTermCfg` object to enable
the curriculum introduced by the function.
"""
from __future__ import annotations
import torch
from collections.abc import Sequence
from typing import TYPE_CHECKING
from omni.isaac.orbit.assets import Articulation
from omni.isaac.orbit.managers import SceneEntityCfg
from omni.isaac.orbit.terrains import TerrainImporter
if TYPE_CHECKING:
from omni.isaac.orbit.envs import RLTaskEnv
def terrain_levels_vel(
env: RLTaskEnv, env_ids: Sequence[int], asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
) -> torch.Tensor:
"""Curriculum based on the distance the robot walked when commanded to move at a desired velocity.
This term is used to increase the difficulty of the terrain when the robot walks far enough and decrease the
difficulty when the robot walks less than half of the distance required by the commanded velocity.
.. note::
It is only possible to use this term with the terrain type ``generator``. For further information
on different terrain types, check the :class:`omni.isaac.orbit.terrains.TerrainImporter` class.
Returns:
The mean terrain level for the given environment ids.
"""
# extract the used quantities (to enable type-hinting)
asset: Articulation = env.scene[asset_cfg.name]
terrain: TerrainImporter = env.scene.terrain
command = env.command_manager.get_command("base_velocity")
# compute the distance the robot walked
distance = torch.norm(asset.data.root_pos_w[env_ids, :2] - env.scene.env_origins[env_ids, :2], dim=1)
# robots that walked far enough progress to harder terrains
move_up = distance > terrain.cfg.terrain_generator.size[0] / 2
# robots that walked less than half of their required distance go to simpler terrains
move_down = distance < torch.norm(command[env_ids, :2], dim=1) * env.max_episode_length_s * 0.5
move_down *= ~move_up
# update terrain levels
terrain.update_env_origins(env_ids, move_up, move_down)
# return the mean terrain level
return torch.mean(terrain.terrain_levels.float())
| 2,376 | Python | 41.446428 | 112 | 0.742424 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/mdp/rewards.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from __future__ import annotations
import torch
from typing import TYPE_CHECKING
from omni.isaac.orbit.managers import SceneEntityCfg
from omni.isaac.orbit.sensors import ContactSensor
if TYPE_CHECKING:
from omni.isaac.orbit.envs import RLTaskEnv
def feet_air_time(env: RLTaskEnv, command_name: str, sensor_cfg: SceneEntityCfg, threshold: float) -> torch.Tensor:
"""Reward long steps taken by the feet using L2-kernel.
This function rewards the agent for taking steps that are longer than a threshold. This helps ensure
that the robot lifts its feet off the ground and takes steps. The reward is computed as the sum of
the time for which the feet are in the air.
If the commands are small (i.e. the agent is not supposed to take a step), then the reward is zero.
"""
# extract the used quantities (to enable type-hinting)
contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
# compute the reward
first_contact = contact_sensor.compute_first_contact(env.step_dt)[:, sensor_cfg.body_ids]
last_air_time = contact_sensor.data.last_air_time[:, sensor_cfg.body_ids]
reward = torch.sum((last_air_time - threshold) * first_contact, dim=1)
# no reward for zero command
reward *= torch.norm(env.command_manager.get_command(command_name)[:, :2], dim=1) > 0.1
return reward
def feet_air_time_positive_biped(env, command_name: str, threshold: float, sensor_cfg: SceneEntityCfg) -> torch.Tensor:
"""Reward long steps taken by the feet for bipeds.
This function rewards the agent for taking steps up to a specified threshold and also keep one foot at
a time in the air.
If the commands are small (i.e. the agent is not supposed to take a step), then the reward is zero.
"""
contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
# compute the reward
air_time = contact_sensor.data.current_air_time[:, sensor_cfg.body_ids]
contact_time = contact_sensor.data.current_contact_time[:, sensor_cfg.body_ids]
in_contact = contact_time > 0.0
in_mode_time = torch.where(in_contact, contact_time, air_time)
single_stance = torch.sum(in_contact.int(), dim=1) == 1
reward = torch.min(torch.where(single_stance.unsqueeze(-1), in_mode_time, 0.0), dim=1)[0]
reward = torch.clamp(reward, max=threshold)
# no reward for zero command
reward *= torch.norm(env.command_manager.get_command(command_name)[:, :2], dim=1) > 0.1
return reward
| 2,595 | Python | 43.75862 | 119 | 0.717148 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Configurations for velocity-based locomotion environments."""
# We leave this file empty since we don't want to expose any configs in this package directly.
# We still need this file to import the "config" module in the parent package.
| 363 | Python | 35.399996 | 94 | 0.763085 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go1/rough_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg
##
# Pre-defined configs
##
from omni.isaac.orbit_assets.unitree import UNITREE_GO1_CFG # isort: skip
@configclass
class UnitreeGo1RoughEnvCfg(LocomotionVelocityRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
self.scene.robot = UNITREE_GO1_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/trunk"
# scale down the terrains because the robot is small
self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_height_range = (0.025, 0.1)
self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_range = (0.01, 0.06)
self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_step = 0.01
# reduce action scale
self.actions.joint_pos.scale = 0.25
# event
self.events.push_robot = None
self.events.add_base_mass.params["mass_range"] = (-1.0, 3.0)
self.events.add_base_mass.params["asset_cfg"].body_names = "trunk"
self.events.base_external_force_torque.params["asset_cfg"].body_names = "trunk"
self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0)
self.events.reset_base.params = {
"pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)},
"velocity_range": {
"x": (0.0, 0.0),
"y": (0.0, 0.0),
"z": (0.0, 0.0),
"roll": (0.0, 0.0),
"pitch": (0.0, 0.0),
"yaw": (0.0, 0.0),
},
}
# rewards
self.rewards.feet_air_time.params["sensor_cfg"].body_names = ".*_foot"
self.rewards.feet_air_time.weight = 0.01
self.rewards.undesired_contacts = None
self.rewards.dof_torques_l2.weight = -0.0002
self.rewards.track_lin_vel_xy_exp.weight = 1.5
self.rewards.track_ang_vel_z_exp.weight = 0.75
self.rewards.dof_acc_l2.weight = -2.5e-7
# terminations
self.terminations.base_contact.params["sensor_cfg"].body_names = "trunk"
@configclass
class UnitreeGo1RoughEnvCfg_PLAY(UnitreeGo1RoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# spawn the robot randomly in the grid (instead of their terrain levels)
self.scene.terrain.max_init_terrain_level = None
# reduce the number of terrains to save memory
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.num_rows = 5
self.scene.terrain.terrain_generator.num_cols = 5
self.scene.terrain.terrain_generator.curriculum = False
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 3,351 | Python | 38.435294 | 101 | 0.621904 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go1/flat_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from .rough_env_cfg import UnitreeGo1RoughEnvCfg
@configclass
class UnitreeGo1FlatEnvCfg(UnitreeGo1RoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# override rewards
self.rewards.flat_orientation_l2.weight = -2.5
self.rewards.feet_air_time.weight = 0.25
# change terrain to flat
self.scene.terrain.terrain_type = "plane"
self.scene.terrain.terrain_generator = None
# no height scan
self.scene.height_scanner = None
self.observations.policy.height_scan = None
# no terrain curriculum
self.curriculum.terrain_levels = None
class UnitreeGo1FlatEnvCfg_PLAY(UnitreeGo1FlatEnvCfg):
def __post_init__(self) -> None:
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 1,338 | Python | 29.431818 | 58 | 0.658445 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go1/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import gymnasium as gym
from . import agents, flat_env_cfg, rough_env_cfg
##
# Register Gym environments.
##
gym.register(
id="Isaac-Velocity-Flat-Unitree-Go1-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.UnitreeGo1FlatEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo1FlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Flat-Unitree-Go1-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.UnitreeGo1FlatEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo1FlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Unitree-Go1-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.UnitreeGo1RoughEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo1RoughPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Unitree-Go1-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.UnitreeGo1RoughEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo1RoughPPORunnerCfg,
},
)
| 1,498 | Python | 27.283018 | 80 | 0.690921 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go1/agents/rsl_rl_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import (
RslRlOnPolicyRunnerCfg,
RslRlPpoActorCriticCfg,
RslRlPpoAlgorithmCfg,
)
@configclass
class UnitreeGo1RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
num_steps_per_env = 24
max_iterations = 1500
save_interval = 50
experiment_name = "unitree_go1_rough"
empirical_normalization = False
policy = RslRlPpoActorCriticCfg(
init_noise_std=1.0,
actor_hidden_dims=[512, 256, 128],
critic_hidden_dims=[512, 256, 128],
activation="elu",
)
algorithm = RslRlPpoAlgorithmCfg(
value_loss_coef=1.0,
use_clipped_value_loss=True,
clip_param=0.2,
entropy_coef=0.01,
num_learning_epochs=5,
num_mini_batches=4,
learning_rate=1.0e-3,
schedule="adaptive",
gamma=0.99,
lam=0.95,
desired_kl=0.01,
max_grad_norm=1.0,
)
@configclass
class UnitreeGo1FlatPPORunnerCfg(UnitreeGo1RoughPPORunnerCfg):
def __post_init__(self):
super().__post_init__()
self.max_iterations = 300
self.experiment_name = "unitree_go1_flat"
self.policy.actor_hidden_dims = [128, 128, 128]
self.policy.critic_hidden_dims = [128, 128, 128]
| 1,432 | Python | 26.037735 | 62 | 0.648045 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go1/agents/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from . import rsl_rl_cfg # noqa: F401, F403
| 168 | Python | 23.142854 | 56 | 0.720238 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/cassie/rough_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.managers import RewardTermCfg as RewTerm
from omni.isaac.orbit.managers import SceneEntityCfg
from omni.isaac.orbit.utils import configclass
import omni.isaac.orbit_tasks.locomotion.velocity.mdp as mdp
from omni.isaac.orbit_tasks.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg, RewardsCfg
##
# Pre-defined configs
##
from omni.isaac.orbit_assets.cassie import CASSIE_CFG # isort: skip
@configclass
class CassieRewardsCfg(RewardsCfg):
termination_penalty = RewTerm(func=mdp.is_terminated, weight=-200.0)
feet_air_time = RewTerm(
func=mdp.feet_air_time_positive_biped,
weight=2.5,
params={
"sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*toe"),
"command_name": "base_velocity",
"threshold": 0.3,
},
)
joint_deviation_hip = RewTerm(
func=mdp.joint_deviation_l1,
weight=-0.2,
params={"asset_cfg": SceneEntityCfg("robot", joint_names=["hip_abduction_.*", "hip_rotation_.*"])},
)
joint_deviation_toes = RewTerm(
func=mdp.joint_deviation_l1,
weight=-0.2,
params={"asset_cfg": SceneEntityCfg("robot", joint_names=["toe_joint_.*"])},
)
# penalize toe joint limits
dof_pos_limits = RewTerm(
func=mdp.joint_pos_limits,
weight=-1.0,
params={"asset_cfg": SceneEntityCfg("robot", joint_names="toe_joint_.*")},
)
@configclass
class CassieRoughEnvCfg(LocomotionVelocityRoughEnvCfg):
"""Cassie rough environment configuration."""
rewards: CassieRewardsCfg = CassieRewardsCfg()
def __post_init__(self):
super().__post_init__()
# scene
self.scene.robot = CASSIE_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/pelvis"
# actions
self.actions.joint_pos.scale = 0.5
# events
self.events.push_robot = None
self.events.add_base_mass = None
self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0)
self.events.base_external_force_torque.params["asset_cfg"].body_names = [".*pelvis"]
self.events.reset_base.params = {
"pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)},
"velocity_range": {
"x": (0.0, 0.0),
"y": (0.0, 0.0),
"z": (0.0, 0.0),
"roll": (0.0, 0.0),
"pitch": (0.0, 0.0),
"yaw": (0.0, 0.0),
},
}
# terminations
self.terminations.base_contact.params["sensor_cfg"].body_names = [".*pelvis"]
# rewards
self.rewards.undesired_contacts = None
self.rewards.dof_torques_l2.weight = -5.0e-6
self.rewards.track_lin_vel_xy_exp.weight = 2.0
self.rewards.track_ang_vel_z_exp.weight = 1.0
self.rewards.action_rate_l2.weight *= 1.5
self.rewards.dof_acc_l2.weight *= 1.5
@configclass
class CassieRoughEnvCfg_PLAY(CassieRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# spawn the robot randomly in the grid (instead of their terrain levels)
self.scene.terrain.max_init_terrain_level = None
# reduce the number of terrains to save memory
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.num_rows = 5
self.scene.terrain.terrain_generator.num_cols = 5
self.scene.terrain.terrain_generator.curriculum = False
self.commands.base_velocity.ranges.lin_vel_x = (0.7, 1.0)
self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0)
self.commands.base_velocity.ranges.heading = (0.0, 0.0)
# disable randomization for play
self.observations.policy.enable_corruption = False
| 4,139 | Python | 35 | 113 | 0.614641 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/cassie/flat_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from .rough_env_cfg import CassieRoughEnvCfg
@configclass
class CassieFlatEnvCfg(CassieRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# rewards
self.rewards.flat_orientation_l2.weight = -2.5
self.rewards.feet_air_time.weight = 5.0
self.rewards.joint_deviation_hip.params["asset_cfg"].joint_names = ["hip_rotation_.*"]
# change terrain to flat
self.scene.terrain.terrain_type = "plane"
self.scene.terrain.terrain_generator = None
# no height scan
self.scene.height_scanner = None
self.observations.policy.height_scan = None
# no terrain curriculum
self.curriculum.terrain_levels = None
class CassieFlatEnvCfg_PLAY(CassieFlatEnvCfg):
def __post_init__(self) -> None:
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# disable randomization for play
self.observations.policy.enable_corruption = False
| 1,271 | Python | 30.799999 | 94 | 0.653816 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/cassie/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import gymnasium as gym
from . import agents, flat_env_cfg, rough_env_cfg
##
# Register Gym environments.
##
gym.register(
id="Isaac-Velocity-Flat-Cassie-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.CassieFlatEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CassieFlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Flat-Cassie-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.CassieFlatEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CassieFlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Cassie-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.CassieRoughEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CassieRoughPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Cassie-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.CassieRoughEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.CassieRoughPPORunnerCfg,
},
)
| 1,446 | Python | 26.301886 | 76 | 0.682573 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/cassie/agents/rsl_rl_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import (
RslRlOnPolicyRunnerCfg,
RslRlPpoActorCriticCfg,
RslRlPpoAlgorithmCfg,
)
@configclass
class CassieRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
num_steps_per_env = 24
max_iterations = 1500
save_interval = 50
experiment_name = "cassie_rough"
empirical_normalization = False
policy = RslRlPpoActorCriticCfg(
init_noise_std=1.0,
actor_hidden_dims=[512, 256, 128],
critic_hidden_dims=[512, 256, 128],
activation="elu",
)
algorithm = RslRlPpoAlgorithmCfg(
value_loss_coef=1.0,
use_clipped_value_loss=True,
clip_param=0.2,
entropy_coef=0.01,
num_learning_epochs=5,
num_mini_batches=4,
learning_rate=1.0e-3,
schedule="adaptive",
gamma=0.99,
lam=0.95,
desired_kl=0.01,
max_grad_norm=1.0,
)
@configclass
class CassieFlatPPORunnerCfg(CassieRoughPPORunnerCfg):
def __post_init__(self):
super().__post_init__()
self.max_iterations = 1000
self.experiment_name = "cassie_flat"
self.policy.actor_hidden_dims = [128, 128, 128]
self.policy.critic_hidden_dims = [128, 128, 128]
| 1,411 | Python | 25.641509 | 58 | 0.644224 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/cassie/agents/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from . import rsl_rl_cfg # noqa: F401, F403
| 168 | Python | 23.142854 | 56 | 0.720238 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_b/rough_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg
##
# Pre-defined configs
##
from omni.isaac.orbit_assets import ANYMAL_B_CFG # isort: skip
@configclass
class AnymalBRoughEnvCfg(LocomotionVelocityRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# switch robot to anymal-d
self.scene.robot = ANYMAL_B_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
@configclass
class AnymalBRoughEnvCfg_PLAY(AnymalBRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# spawn the robot randomly in the grid (instead of their terrain levels)
self.scene.terrain.max_init_terrain_level = None
# reduce the number of terrains to save memory
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.num_rows = 5
self.scene.terrain.terrain_generator.num_cols = 5
self.scene.terrain.terrain_generator.curriculum = False
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 1,591 | Python | 32.87234 | 101 | 0.683847 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_b/flat_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from .rough_env_cfg import AnymalBRoughEnvCfg
@configclass
class AnymalBFlatEnvCfg(AnymalBRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# override rewards
self.rewards.flat_orientation_l2.weight = -5.0
self.rewards.dof_torques_l2.weight = -2.5e-5
self.rewards.feet_air_time.weight = 0.5
# change terrain to flat
self.scene.terrain.terrain_type = "plane"
self.scene.terrain.terrain_generator = None
# no height scan
self.scene.height_scanner = None
self.observations.policy.height_scan = None
# no terrain curriculum
self.curriculum.terrain_levels = None
class AnymalBFlatEnvCfg_PLAY(AnymalBFlatEnvCfg):
def __post_init__(self) -> None:
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 1,374 | Python | 30.249999 | 58 | 0.653566 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_b/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import gymnasium as gym
from . import agents, flat_env_cfg, rough_env_cfg
##
# Register Gym environments.
##
gym.register(
id="Isaac-Velocity-Flat-Anymal-B-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.AnymalBFlatEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalBFlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Flat-Anymal-B-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.AnymalBFlatEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalBFlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Anymal-B-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.AnymalBRoughEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalBRoughPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Anymal-B-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.AnymalBRoughEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalBRoughPPORunnerCfg,
},
)
| 1,461 | Python | 27.115384 | 77 | 0.683778 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_b/agents/rsl_rl_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import (
RslRlOnPolicyRunnerCfg,
RslRlPpoActorCriticCfg,
RslRlPpoAlgorithmCfg,
)
@configclass
class AnymalBRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
num_steps_per_env = 24
max_iterations = 1500
save_interval = 50
experiment_name = "anymal_b_rough"
empirical_normalization = False
policy = RslRlPpoActorCriticCfg(
init_noise_std=1.0,
actor_hidden_dims=[512, 256, 128],
critic_hidden_dims=[512, 256, 128],
activation="elu",
)
algorithm = RslRlPpoAlgorithmCfg(
value_loss_coef=1.0,
use_clipped_value_loss=True,
clip_param=0.2,
entropy_coef=0.005,
num_learning_epochs=5,
num_mini_batches=4,
learning_rate=1.0e-3,
schedule="adaptive",
gamma=0.99,
lam=0.95,
desired_kl=0.01,
max_grad_norm=1.0,
)
@configclass
class AnymalBFlatPPORunnerCfg(AnymalBRoughPPORunnerCfg):
def __post_init__(self):
super().__post_init__()
self.max_iterations = 300
self.experiment_name = "anymal_b_flat"
self.policy.actor_hidden_dims = [128, 128, 128]
self.policy.critic_hidden_dims = [128, 128, 128]
| 1,418 | Python | 25.773584 | 58 | 0.64457 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_b/agents/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from . import rsl_rl_cfg # noqa: F401, F403
| 168 | Python | 23.142854 | 56 | 0.720238 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_c/rough_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg
##
# Pre-defined configs
##
from omni.isaac.orbit_assets.anymal import ANYMAL_C_CFG # isort: skip
@configclass
class AnymalCRoughEnvCfg(LocomotionVelocityRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# switch robot to anymal-c
self.scene.robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
@configclass
class AnymalCRoughEnvCfg_PLAY(AnymalCRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# spawn the robot randomly in the grid (instead of their terrain levels)
self.scene.terrain.max_init_terrain_level = None
# reduce the number of terrains to save memory
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.num_rows = 5
self.scene.terrain.terrain_generator.num_cols = 5
self.scene.terrain.terrain_generator.curriculum = False
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 1,598 | Python | 33.021276 | 101 | 0.684606 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_c/flat_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from .rough_env_cfg import AnymalCRoughEnvCfg
@configclass
class AnymalCFlatEnvCfg(AnymalCRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# override rewards
self.rewards.flat_orientation_l2.weight = -5.0
self.rewards.dof_torques_l2.weight = -2.5e-5
self.rewards.feet_air_time.weight = 0.5
# change terrain to flat
self.scene.terrain.terrain_type = "plane"
self.scene.terrain.terrain_generator = None
# no height scan
self.scene.height_scanner = None
self.observations.policy.height_scan = None
# no terrain curriculum
self.curriculum.terrain_levels = None
class AnymalCFlatEnvCfg_PLAY(AnymalCFlatEnvCfg):
def __post_init__(self) -> None:
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 1,374 | Python | 30.249999 | 58 | 0.653566 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_c/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import gymnasium as gym
from . import agents, flat_env_cfg, rough_env_cfg
##
# Register Gym environments.
##
gym.register(
id="Isaac-Velocity-Flat-Anymal-C-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.AnymalCFlatEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalCFlatPPORunnerCfg,
"skrl_cfg_entry_point": "omni.isaac.orbit_tasks.locomotion.velocity.anymal_c.agents:skrl_cfg.yaml",
},
)
gym.register(
id="Isaac-Velocity-Flat-Anymal-C-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.AnymalCFlatEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalCFlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Anymal-C-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.AnymalCRoughEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalCRoughPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Anymal-C-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.AnymalCRoughEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalCRoughPPORunnerCfg,
},
)
| 1,570 | Python | 28.092592 | 107 | 0.685987 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_c/agents/skrl_cfg.yaml | seed: 42
# Models are instantiated using skrl's model instantiator utility
# https://skrl.readthedocs.io/en/develop/modules/skrl.utils.model_instantiators.html
models:
separate: False
policy: # see skrl.utils.model_instantiators.gaussian_model for parameter details
clip_actions: False
clip_log_std: True
initial_log_std: 0
min_log_std: -20.0
max_log_std: 2.0
input_shape: "Shape.STATES"
hiddens: [128, 128, 128]
hidden_activation: ["elu", "elu", "elu"]
output_shape: "Shape.ACTIONS"
output_activation: "tanh"
output_scale: 1.0
value: # see skrl.utils.model_instantiators.deterministic_model for parameter details
clip_actions: False
input_shape: "Shape.STATES"
hiddens: [128, 128, 128]
hidden_activation: ["elu", "elu", "elu"]
output_shape: "Shape.ONE"
output_activation: ""
output_scale: 1.0
# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG)
# https://skrl.readthedocs.io/en/latest/modules/skrl.agents.ppo.html
agent:
rollouts: 24
learning_epochs: 5
mini_batches: 4
discount_factor: 0.99
lambda: 0.95
learning_rate: 1.e-3
learning_rate_scheduler: "KLAdaptiveLR"
learning_rate_scheduler_kwargs:
kl_threshold: 0.01
state_preprocessor: "RunningStandardScaler"
state_preprocessor_kwargs: null
value_preprocessor: "RunningStandardScaler"
value_preprocessor_kwargs: null
random_timesteps: 0
learning_starts: 0
grad_norm_clip: 1.0
ratio_clip: 0.2
value_clip: 0.2
clip_predicted_values: True
entropy_loss_scale: 0.0
value_loss_scale: 1.0
kl_threshold: 0
rewards_shaper_scale: 1.0
# logging and checkpoint
experiment:
directory: "anymal"
experiment_name: ""
write_interval: 60
checkpoint_interval: 600
# Sequential trainer
# https://skrl.readthedocs.io/en/latest/modules/skrl.trainers.sequential.html
trainer:
timesteps: 12000
| 1,893 | YAML | 27.268656 | 88 | 0.711569 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_c/agents/rsl_rl_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import (
RslRlOnPolicyRunnerCfg,
RslRlPpoActorCriticCfg,
RslRlPpoAlgorithmCfg,
)
@configclass
class AnymalCRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
num_steps_per_env = 24
max_iterations = 1500
save_interval = 50
experiment_name = "anymal_c_rough"
empirical_normalization = False
policy = RslRlPpoActorCriticCfg(
init_noise_std=1.0,
actor_hidden_dims=[512, 256, 128],
critic_hidden_dims=[512, 256, 128],
activation="elu",
)
algorithm = RslRlPpoAlgorithmCfg(
value_loss_coef=1.0,
use_clipped_value_loss=True,
clip_param=0.2,
entropy_coef=0.005,
num_learning_epochs=5,
num_mini_batches=4,
learning_rate=1.0e-3,
schedule="adaptive",
gamma=0.99,
lam=0.95,
desired_kl=0.01,
max_grad_norm=1.0,
)
@configclass
class AnymalCFlatPPORunnerCfg(AnymalCRoughPPORunnerCfg):
def __post_init__(self):
super().__post_init__()
self.max_iterations = 300
self.experiment_name = "anymal_c_flat"
self.policy.actor_hidden_dims = [128, 128, 128]
self.policy.critic_hidden_dims = [128, 128, 128]
| 1,418 | Python | 25.773584 | 58 | 0.64457 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_c/agents/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from . import rsl_rl_cfg # noqa: F401, F403
| 168 | Python | 23.142854 | 56 | 0.720238 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_d/rough_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg
##
# Pre-defined configs
##
from omni.isaac.orbit_assets.anymal import ANYMAL_D_CFG # isort: skip
@configclass
class AnymalDRoughEnvCfg(LocomotionVelocityRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# switch robot to anymal-d
self.scene.robot = ANYMAL_D_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
@configclass
class AnymalDRoughEnvCfg_PLAY(AnymalDRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# spawn the robot randomly in the grid (instead of their terrain levels)
self.scene.terrain.max_init_terrain_level = None
# reduce the number of terrains to save memory
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.num_rows = 5
self.scene.terrain.terrain_generator.num_cols = 5
self.scene.terrain.terrain_generator.curriculum = False
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 1,598 | Python | 33.021276 | 101 | 0.684606 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_d/flat_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from .rough_env_cfg import AnymalDRoughEnvCfg
@configclass
class AnymalDFlatEnvCfg(AnymalDRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# override rewards
self.rewards.flat_orientation_l2.weight = -5.0
self.rewards.dof_torques_l2.weight = -2.5e-5
self.rewards.feet_air_time.weight = 0.5
# change terrain to flat
self.scene.terrain.terrain_type = "plane"
self.scene.terrain.terrain_generator = None
# no height scan
self.scene.height_scanner = None
self.observations.policy.height_scan = None
# no terrain curriculum
self.curriculum.terrain_levels = None
class AnymalDFlatEnvCfg_PLAY(AnymalDFlatEnvCfg):
def __post_init__(self) -> None:
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 1,374 | Python | 30.249999 | 58 | 0.653566 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_d/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import gymnasium as gym
from . import agents, flat_env_cfg, rough_env_cfg
##
# Register Gym environments.
##
gym.register(
id="Isaac-Velocity-Flat-Anymal-D-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.AnymalDFlatEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalDFlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Flat-Anymal-D-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.AnymalDFlatEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalDFlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Anymal-D-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.AnymalDRoughEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalDRoughPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Anymal-D-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.AnymalDRoughEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.AnymalDRoughPPORunnerCfg,
},
)
| 1,462 | Python | 26.603773 | 77 | 0.683311 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_d/agents/rsl_rl_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import (
RslRlOnPolicyRunnerCfg,
RslRlPpoActorCriticCfg,
RslRlPpoAlgorithmCfg,
)
@configclass
class AnymalDRoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
num_steps_per_env = 24
max_iterations = 1500
save_interval = 50
experiment_name = "anymal_d_rough"
empirical_normalization = False
policy = RslRlPpoActorCriticCfg(
init_noise_std=1.0,
actor_hidden_dims=[512, 256, 128],
critic_hidden_dims=[512, 256, 128],
activation="elu",
)
algorithm = RslRlPpoAlgorithmCfg(
value_loss_coef=1.0,
use_clipped_value_loss=True,
clip_param=0.2,
entropy_coef=0.005,
num_learning_epochs=5,
num_mini_batches=4,
learning_rate=1.0e-3,
schedule="adaptive",
gamma=0.99,
lam=0.95,
desired_kl=0.01,
max_grad_norm=1.0,
)
@configclass
class AnymalDFlatPPORunnerCfg(AnymalDRoughPPORunnerCfg):
def __post_init__(self):
super().__post_init__()
self.max_iterations = 300
self.experiment_name = "anymal_d_flat"
self.policy.actor_hidden_dims = [128, 128, 128]
self.policy.critic_hidden_dims = [128, 128, 128]
| 1,418 | Python | 25.773584 | 58 | 0.64457 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_d/agents/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from . import rsl_rl_cfg # noqa: F401, F403
| 168 | Python | 23.142854 | 56 | 0.720238 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go2/rough_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg
##
# Pre-defined configs
##
from omni.isaac.orbit_assets.unitree import UNITREE_GO2_CFG # isort: skip
@configclass
class UnitreeGo2RoughEnvCfg(LocomotionVelocityRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
self.scene.robot = UNITREE_GO2_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/base"
# scale down the terrains because the robot is small
self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_height_range = (0.025, 0.1)
self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_range = (0.01, 0.06)
self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_step = 0.01
# reduce action scale
self.actions.joint_pos.scale = 0.25
# event
self.events.push_robot = None
self.events.add_base_mass.params["mass_range"] = (-1.0, 3.0)
self.events.add_base_mass.params["asset_cfg"].body_names = "base"
self.events.base_external_force_torque.params["asset_cfg"].body_names = "base"
self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0)
self.events.reset_base.params = {
"pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)},
"velocity_range": {
"x": (0.0, 0.0),
"y": (0.0, 0.0),
"z": (0.0, 0.0),
"roll": (0.0, 0.0),
"pitch": (0.0, 0.0),
"yaw": (0.0, 0.0),
},
}
# rewards
self.rewards.feet_air_time.params["sensor_cfg"].body_names = ".*_foot"
self.rewards.feet_air_time.weight = 0.01
self.rewards.undesired_contacts = None
self.rewards.dof_torques_l2.weight = -0.0002
self.rewards.track_lin_vel_xy_exp.weight = 1.5
self.rewards.track_ang_vel_z_exp.weight = 0.75
self.rewards.dof_acc_l2.weight = -2.5e-7
# terminations
self.terminations.base_contact.params["sensor_cfg"].body_names = "base"
@configclass
class UnitreeGo2RoughEnvCfg_PLAY(UnitreeGo2RoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# spawn the robot randomly in the grid (instead of their terrain levels)
self.scene.terrain.max_init_terrain_level = None
# reduce the number of terrains to save memory
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.num_rows = 5
self.scene.terrain.terrain_generator.num_cols = 5
self.scene.terrain.terrain_generator.curriculum = False
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 3,347 | Python | 38.388235 | 101 | 0.621452 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go2/flat_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from .rough_env_cfg import UnitreeGo2RoughEnvCfg
@configclass
class UnitreeGo2FlatEnvCfg(UnitreeGo2RoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# override rewards
self.rewards.flat_orientation_l2.weight = -2.5
self.rewards.feet_air_time.weight = 0.25
# change terrain to flat
self.scene.terrain.terrain_type = "plane"
self.scene.terrain.terrain_generator = None
# no height scan
self.scene.height_scanner = None
self.observations.policy.height_scan = None
# no terrain curriculum
self.curriculum.terrain_levels = None
class UnitreeGo2FlatEnvCfg_PLAY(UnitreeGo2FlatEnvCfg):
def __post_init__(self) -> None:
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 1,338 | Python | 29.431818 | 58 | 0.658445 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go2/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import gymnasium as gym
from . import agents, flat_env_cfg, rough_env_cfg
##
# Register Gym environments.
##
gym.register(
id="Isaac-Velocity-Flat-Unitree-Go2-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.UnitreeGo2FlatEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo2FlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Flat-Unitree-Go2-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.UnitreeGo2FlatEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo2FlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Unitree-Go2-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.UnitreeGo2RoughEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo2RoughPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Unitree-Go2-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.UnitreeGo2RoughEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeGo2RoughPPORunnerCfg,
},
)
| 1,498 | Python | 27.283018 | 80 | 0.690921 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go2/agents/rsl_rl_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import (
RslRlOnPolicyRunnerCfg,
RslRlPpoActorCriticCfg,
RslRlPpoAlgorithmCfg,
)
@configclass
class UnitreeGo2RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
num_steps_per_env = 24
max_iterations = 1500
save_interval = 50
experiment_name = "unitree_go2_rough"
empirical_normalization = False
policy = RslRlPpoActorCriticCfg(
init_noise_std=1.0,
actor_hidden_dims=[512, 256, 128],
critic_hidden_dims=[512, 256, 128],
activation="elu",
)
algorithm = RslRlPpoAlgorithmCfg(
value_loss_coef=1.0,
use_clipped_value_loss=True,
clip_param=0.2,
entropy_coef=0.01,
num_learning_epochs=5,
num_mini_batches=4,
learning_rate=1.0e-3,
schedule="adaptive",
gamma=0.99,
lam=0.95,
desired_kl=0.01,
max_grad_norm=1.0,
)
@configclass
class UnitreeGo2FlatPPORunnerCfg(UnitreeGo2RoughPPORunnerCfg):
def __post_init__(self):
super().__post_init__()
self.max_iterations = 300
self.experiment_name = "unitree_go2_flat"
self.policy.actor_hidden_dims = [128, 128, 128]
self.policy.critic_hidden_dims = [128, 128, 128]
| 1,432 | Python | 26.037735 | 62 | 0.648045 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_go2/agents/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from . import rsl_rl_cfg # noqa: F401, F403
| 168 | Python | 23.142854 | 56 | 0.720238 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_a1/rough_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg
##
# Pre-defined configs
##
from omni.isaac.orbit_assets.unitree import UNITREE_A1_CFG # isort: skip
@configclass
class UnitreeA1RoughEnvCfg(LocomotionVelocityRoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
self.scene.robot = UNITREE_A1_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/trunk"
# scale down the terrains because the robot is small
self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_height_range = (0.025, 0.1)
self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_range = (0.01, 0.06)
self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_step = 0.01
# reduce action scale
self.actions.joint_pos.scale = 0.25
# event
self.events.push_robot = None
self.events.add_base_mass.params["mass_range"] = (-1.0, 3.0)
self.events.add_base_mass.params["asset_cfg"].body_names = "trunk"
self.events.base_external_force_torque.params["asset_cfg"].body_names = "trunk"
self.events.reset_robot_joints.params["position_range"] = (1.0, 1.0)
self.events.reset_base.params = {
"pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)},
"velocity_range": {
"x": (0.0, 0.0),
"y": (0.0, 0.0),
"z": (0.0, 0.0),
"roll": (0.0, 0.0),
"pitch": (0.0, 0.0),
"yaw": (0.0, 0.0),
},
}
# rewards
self.rewards.feet_air_time.params["sensor_cfg"].body_names = ".*_foot"
self.rewards.feet_air_time.weight = 0.01
self.rewards.undesired_contacts = None
self.rewards.dof_torques_l2.weight = -0.0002
self.rewards.track_lin_vel_xy_exp.weight = 1.5
self.rewards.track_ang_vel_z_exp.weight = 0.75
self.rewards.dof_acc_l2.weight = -2.5e-7
# terminations
self.terminations.base_contact.params["sensor_cfg"].body_names = "trunk"
@configclass
class UnitreeA1RoughEnvCfg_PLAY(UnitreeA1RoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# spawn the robot randomly in the grid (instead of their terrain levels)
self.scene.terrain.max_init_terrain_level = None
# reduce the number of terrains to save memory
if self.scene.terrain.terrain_generator is not None:
self.scene.terrain.terrain_generator.num_rows = 5
self.scene.terrain.terrain_generator.num_cols = 5
self.scene.terrain.terrain_generator.curriculum = False
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 3,346 | Python | 38.37647 | 101 | 0.621339 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_a1/flat_env_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from .rough_env_cfg import UnitreeA1RoughEnvCfg
@configclass
class UnitreeA1FlatEnvCfg(UnitreeA1RoughEnvCfg):
def __post_init__(self):
# post init of parent
super().__post_init__()
# override rewards
self.rewards.flat_orientation_l2.weight = -2.5
self.rewards.feet_air_time.weight = 0.25
# change terrain to flat
self.scene.terrain.terrain_type = "plane"
self.scene.terrain.terrain_generator = None
# no height scan
self.scene.height_scanner = None
self.observations.policy.height_scan = None
# no terrain curriculum
self.curriculum.terrain_levels = None
class UnitreeA1FlatEnvCfg_PLAY(UnitreeA1FlatEnvCfg):
def __post_init__(self) -> None:
# post init of parent
super().__post_init__()
# make a smaller scene for play
self.scene.num_envs = 50
self.scene.env_spacing = 2.5
# disable randomization for play
self.observations.policy.enable_corruption = False
# remove random pushing event
self.events.base_external_force_torque = None
self.events.push_robot = None
| 1,333 | Python | 29.318181 | 58 | 0.657164 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_a1/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import gymnasium as gym
from . import agents, flat_env_cfg, rough_env_cfg
##
# Register Gym environments.
##
gym.register(
id="Isaac-Velocity-Flat-Unitree-A1-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.UnitreeA1FlatEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeA1FlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Flat-Unitree-A1-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": flat_env_cfg.UnitreeA1FlatEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeA1FlatPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Unitree-A1-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.UnitreeA1RoughEnvCfg,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeA1RoughPPORunnerCfg,
},
)
gym.register(
id="Isaac-Velocity-Rough-Unitree-A1-Play-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={
"env_cfg_entry_point": rough_env_cfg.UnitreeA1RoughEnvCfg_PLAY,
"rsl_rl_cfg_entry_point": agents.rsl_rl_cfg.UnitreeA1RoughPPORunnerCfg,
},
)
| 1,486 | Python | 27.056603 | 79 | 0.688425 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_a1/agents/rsl_rl_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import (
RslRlOnPolicyRunnerCfg,
RslRlPpoActorCriticCfg,
RslRlPpoAlgorithmCfg,
)
@configclass
class UnitreeA1RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
num_steps_per_env = 24
max_iterations = 1500
save_interval = 50
experiment_name = "unitree_a1_rough"
empirical_normalization = False
policy = RslRlPpoActorCriticCfg(
init_noise_std=1.0,
actor_hidden_dims=[512, 256, 128],
critic_hidden_dims=[512, 256, 128],
activation="elu",
)
algorithm = RslRlPpoAlgorithmCfg(
value_loss_coef=1.0,
use_clipped_value_loss=True,
clip_param=0.2,
entropy_coef=0.01,
num_learning_epochs=5,
num_mini_batches=4,
learning_rate=1.0e-3,
schedule="adaptive",
gamma=0.99,
lam=0.95,
desired_kl=0.01,
max_grad_norm=1.0,
)
@configclass
class UnitreeA1FlatPPORunnerCfg(UnitreeA1RoughPPORunnerCfg):
def __post_init__(self):
super().__post_init__()
self.max_iterations = 300
self.experiment_name = "unitree_a1_flat"
self.policy.actor_hidden_dims = [128, 128, 128]
self.policy.critic_hidden_dims = [128, 128, 128]
| 1,427 | Python | 25.943396 | 60 | 0.646811 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/locomotion/velocity/config/unitree_a1/agents/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from . import rsl_rl_cfg # noqa: F401, F403
| 168 | Python | 23.142854 | 56 | 0.720238 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/importer.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Sub-module with utility for importing all modules in a package recursively."""
from __future__ import annotations
import importlib
import pkgutil
import sys
def import_packages(package_name: str, blacklist_pkgs: list[str] | None = None):
"""Import all sub-packages in a package recursively.
It is easier to use this function to import all sub-packages in a package recursively
than to manually import each sub-package.
It replaces the need of the following code snippet on the top of each package's ``__init__.py`` file:
.. code-block:: python
import .locomotion.velocity
import .manipulation.reach
import .manipulation.lift
Args:
package_name: The package name.
blacklist_pkgs: The list of blacklisted packages to skip. Defaults to None,
which means no packages are blacklisted.
"""
# Default blacklist
if blacklist_pkgs is None:
blacklist_pkgs = []
# Import the package itself
package = importlib.import_module(package_name)
# Import all Python files
for _ in _walk_packages(package.__path__, package.__name__ + ".", blacklist_pkgs=blacklist_pkgs):
pass
def _walk_packages(
path: str | None = None,
prefix: str = "",
onerror: callable | None = None,
blacklist_pkgs: list[str] | None = None,
):
"""Yields ModuleInfo for all modules recursively on path, or, if path is None, all accessible modules.
Note:
This function is a modified version of the original ``pkgutil.walk_packages`` function. It adds
the `blacklist_pkgs` argument to skip blacklisted packages. Please refer to the original
``pkgutil.walk_packages`` function for more details.
"""
if blacklist_pkgs is None:
blacklist_pkgs = []
def seen(p, m={}):
if p in m:
return True
m[p] = True # noqa: R503
for info in pkgutil.iter_modules(path, prefix):
# check blacklisted
if any([black_pkg_name in info.name for black_pkg_name in blacklist_pkgs]):
continue
# yield the module info
yield info
if info.ispkg:
try:
__import__(info.name)
except Exception:
if onerror is not None:
onerror(info.name)
else:
raise
else:
path = getattr(sys.modules[info.name], "__path__", None) or []
# don't traverse path items we've seen before
path = [p for p in path if not seen(p)]
yield from _walk_packages(path, info.name + ".", onerror, blacklist_pkgs)
| 2,798 | Python | 30.806818 | 106 | 0.617227 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Sub-package with utilities, data collectors and environment wrappers."""
from .importer import import_packages
from .parse_cfg import get_checkpoint_path, load_cfg_from_registry, parse_env_cfg
| 320 | Python | 31.099997 | 81 | 0.771875 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/parse_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Sub-module with utilities for parsing and loading configurations."""
import gymnasium as gym
import importlib
import inspect
import os
import re
import yaml
from omni.isaac.orbit.envs import RLTaskEnvCfg
from omni.isaac.orbit.utils import update_class_from_dict, update_dict
def load_cfg_from_registry(task_name: str, entry_point_key: str) -> dict | RLTaskEnvCfg:
"""Load default configuration given its entry point from the gym registry.
This function loads the configuration object from the gym registry for the given task name.
It supports both YAML and Python configuration files.
It expects the configuration to be registered in the gym registry as:
.. code-block:: python
gym.register(
id="My-Awesome-Task-v0",
...
kwargs={"env_entry_point_cfg": "path.to.config:ConfigClass"},
)
The parsed configuration object for above example can be obtained as:
.. code-block:: python
from omni.isaac.orbit_tasks.utils.parse_cfg import load_cfg_from_registry
cfg = load_cfg_from_registry("My-Awesome-Task-v0", "env_entry_point_cfg")
Args:
task_name: The name of the environment.
entry_point_key: The entry point key to resolve the configuration file.
Returns:
The parsed configuration object. This is either a dictionary or a class object.
Raises:
ValueError: If the entry point key is not available in the gym registry for the task.
"""
# obtain the configuration entry point
cfg_entry_point = gym.spec(task_name).kwargs.get(entry_point_key)
# check if entry point exists
if cfg_entry_point is None:
raise ValueError(
f"Could not find configuration for the environment: '{task_name}'."
f" Please check that the gym registry has the entry point: '{entry_point_key}'."
)
# parse the default config file
if isinstance(cfg_entry_point, str) and cfg_entry_point.endswith(".yaml"):
if os.path.exists(cfg_entry_point):
# absolute path for the config file
config_file = cfg_entry_point
else:
# resolve path to the module location
mod_name, file_name = cfg_entry_point.split(":")
mod_path = os.path.dirname(importlib.import_module(mod_name).__file__)
# obtain the configuration file path
config_file = os.path.join(mod_path, file_name)
# load the configuration
print(f"[INFO]: Parsing configuration from: {config_file}")
with open(config_file, encoding="utf-8") as f:
cfg = yaml.full_load(f)
else:
if callable(cfg_entry_point):
# resolve path to the module location
mod_path = inspect.getfile(cfg_entry_point)
# load the configuration
cfg_cls = cfg_entry_point()
elif isinstance(cfg_entry_point, str):
# resolve path to the module location
mod_name, attr_name = cfg_entry_point.split(":")
mod = importlib.import_module(mod_name)
cfg_cls = getattr(mod, attr_name)
else:
cfg_cls = cfg_entry_point
# load the configuration
print(f"[INFO]: Parsing configuration from: {cfg_entry_point}")
if callable(cfg_cls):
cfg = cfg_cls()
else:
cfg = cfg_cls
return cfg
def parse_env_cfg(
task_name: str, use_gpu: bool | None = None, num_envs: int | None = None, use_fabric: bool | None = None
) -> dict | RLTaskEnvCfg:
"""Parse configuration for an environment and override based on inputs.
Args:
task_name: The name of the environment.
use_gpu: Whether to use GPU/CPU pipeline. Defaults to None, in which case it is left unchanged.
num_envs: Number of environments to create. Defaults to None, in which case it is left unchanged.
use_fabric: Whether to enable/disable fabric interface. If false, all read/write operations go through USD.
This slows down the simulation but allows seeing the changes in the USD through the USD stage.
Defaults to None, in which case it is left unchanged.
Returns:
The parsed configuration object. This is either a dictionary or a class object.
Raises:
ValueError: If the task name is not provided, i.e. None.
"""
# check if a task name is provided
if task_name is None:
raise ValueError("Please provide a valid task name. Hint: Use --task <task_name>.")
# create a dictionary to update from
args_cfg = {"sim": {"physx": dict()}, "scene": dict()}
# resolve pipeline to use (based on input)
if use_gpu is not None:
if not use_gpu:
args_cfg["sim"]["use_gpu_pipeline"] = False
args_cfg["sim"]["physx"]["use_gpu"] = False
args_cfg["sim"]["device"] = "cpu"
else:
args_cfg["sim"]["use_gpu_pipeline"] = True
args_cfg["sim"]["physx"]["use_gpu"] = True
args_cfg["sim"]["device"] = "cuda:0"
# disable fabric to read/write through USD
if use_fabric is not None:
args_cfg["sim"]["use_fabric"] = use_fabric
# number of environments
if num_envs is not None:
args_cfg["scene"]["num_envs"] = num_envs
# load the default configuration
cfg = load_cfg_from_registry(task_name, "env_cfg_entry_point")
# update the main configuration
if isinstance(cfg, dict):
cfg = update_dict(cfg, args_cfg)
else:
update_class_from_dict(cfg, args_cfg)
return cfg
def get_checkpoint_path(
log_path: str, run_dir: str = ".*", checkpoint: str = ".*", other_dirs: list[str] = None, sort_alpha: bool = True
) -> str:
"""Get path to the model checkpoint in input directory.
The checkpoint file is resolved as: ``<log_path>/<run_dir>/<*other_dirs>/<checkpoint>``, where the
:attr:`other_dirs` are intermediate folder names to concatenate. These cannot be regex expressions.
If :attr:`run_dir` and :attr:`checkpoint` are regex expressions then the most recent (highest alphabetical order)
run and checkpoint are selected. To disable this behavior, set the flag :attr:`sort_alpha` to False.
Args:
log_path: The log directory path to find models in.
run_dir: The regex expression for the name of the directory containing the run. Defaults to the most
recent directory created inside :attr:`log_path`.
other_dirs: The intermediate directories between the run directory and the checkpoint file. Defaults to
None, which implies that checkpoint file is directly under the run directory.
checkpoint: The regex expression for the model checkpoint file. Defaults to the most recent
torch-model saved in the :attr:`run_dir` directory.
sort_alpha: Whether to sort the runs by alphabetical order. Defaults to True.
If False, the folders in :attr:`run_dir` are sorted by the last modified time.
Raises:
ValueError: When no runs are found in the input directory.
ValueError: When no checkpoints are found in the input directory.
Returns:
The path to the model checkpoint.
Reference:
https://github.com/leggedrobotics/legged_gym/blob/master/legged_gym/utils/helpers.py#L103
"""
# check if runs present in directory
try:
# find all runs in the directory that math the regex expression
runs = [
os.path.join(log_path, run) for run in os.scandir(log_path) if run.is_dir() and re.match(run_dir, run.name)
]
# sort matched runs by alphabetical order (latest run should be last)
if sort_alpha:
runs.sort()
else:
runs = sorted(runs, key=os.path.getmtime)
# create last run file path
if other_dirs is not None:
run_path = os.path.join(runs[-1], *other_dirs)
else:
run_path = runs[-1]
except IndexError:
raise ValueError(f"No runs present in the directory: '{log_path}' match: '{run_dir}'.")
# list all model checkpoints in the directory
model_checkpoints = [f for f in os.listdir(run_path) if re.match(checkpoint, f)]
# check if any checkpoints are present
if len(model_checkpoints) == 0:
raise ValueError(f"No checkpoints in the directory: '{run_path}' match '{checkpoint}'.")
# sort alphabetically while ensuring that *_10 comes after *_9
model_checkpoints.sort(key=lambda m: f"{m:0>15}")
# get latest matched checkpoint file
checkpoint_file = model_checkpoints[-1]
return os.path.join(run_path, checkpoint_file)
| 8,798 | Python | 39.925581 | 119 | 0.646397 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/wrappers/skrl.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Wrapper to configure an :class:`RLTaskEnv` instance to skrl environment.
The following example shows how to wrap an environment for skrl:
.. code-block:: python
from omni.isaac.orbit_tasks.utils.wrappers.skrl import SkrlVecEnvWrapper
env = SkrlVecEnvWrapper(env)
Or, equivalently, by directly calling the skrl library API as follows:
.. code-block:: python
from skrl.envs.torch.wrappers import wrap_env
env = wrap_env(env, wrapper="isaac-orbit")
"""
# needed to import for type hinting: Agent | list[Agent]
from __future__ import annotations
import copy
import torch
import tqdm
from skrl.agents.torch import Agent
from skrl.envs.wrappers.torch import Wrapper, wrap_env
from skrl.resources.preprocessors.torch import RunningStandardScaler # noqa: F401
from skrl.resources.schedulers.torch import KLAdaptiveLR # noqa: F401
from skrl.trainers.torch import Trainer
from skrl.trainers.torch.sequential import SEQUENTIAL_TRAINER_DEFAULT_CONFIG
from skrl.utils.model_instantiators.torch import Shape # noqa: F401
from omni.isaac.orbit.envs import RLTaskEnv
"""
Configuration Parser.
"""
def process_skrl_cfg(cfg: dict) -> dict:
"""Convert simple YAML types to skrl classes/components.
Args:
cfg: A configuration dictionary.
Returns:
A dictionary containing the converted configuration.
"""
_direct_eval = [
"learning_rate_scheduler",
"state_preprocessor",
"value_preprocessor",
"input_shape",
"output_shape",
]
def reward_shaper_function(scale):
def reward_shaper(rewards, timestep, timesteps):
return rewards * scale
return reward_shaper
def update_dict(d):
for key, value in d.items():
if isinstance(value, dict):
update_dict(value)
else:
if key in _direct_eval:
d[key] = eval(value)
elif key.endswith("_kwargs"):
d[key] = value if value is not None else {}
elif key in ["rewards_shaper_scale"]:
d["rewards_shaper"] = reward_shaper_function(value)
return d
# parse agent configuration and convert to classes
return update_dict(cfg)
"""
Vectorized environment wrapper.
"""
def SkrlVecEnvWrapper(env: RLTaskEnv):
"""Wraps around Orbit environment for skrl.
This function wraps around the Orbit environment. Since the :class:`RLTaskEnv` environment
wrapping functionality is defined within the skrl library itself, this implementation
is maintained for compatibility with the structure of the extension that contains it.
Internally it calls the :func:`wrap_env` from the skrl library API.
Args:
env: The environment to wrap around.
Raises:
ValueError: When the environment is not an instance of :class:`RLTaskEnv`.
Reference:
https://skrl.readthedocs.io/en/latest/modules/skrl.envs.wrapping.html
"""
# check that input is valid
if not isinstance(env.unwrapped, RLTaskEnv):
raise ValueError(f"The environment must be inherited from RLTaskEnv. Environment type: {type(env)}")
# wrap and return the environment
return wrap_env(env, wrapper="isaac-orbit")
"""
Custom trainer for skrl.
"""
class SkrlSequentialLogTrainer(Trainer):
"""Sequential trainer with logging of episode information.
This trainer inherits from the :class:`skrl.trainers.base_class.Trainer` class. It is used to
train agents in a sequential manner (i.e., one after the other in each interaction with the
environment). It is most suitable for on-policy RL agents such as PPO, A2C, etc.
It modifies the :class:`skrl.trainers.torch.sequential.SequentialTrainer` class with the following
differences:
* It also log episode information to the agent's logger.
* It does not close the environment at the end of the training.
Reference:
https://skrl.readthedocs.io/en/latest/modules/skrl.trainers.base_class.html
"""
def __init__(
self,
env: Wrapper,
agents: Agent | list[Agent],
agents_scope: list[int] | None = None,
cfg: dict | None = None,
):
"""Initializes the trainer.
Args:
env: Environment to train on.
agents: Agents to train.
agents_scope: Number of environments for each agent to
train on. Defaults to None.
cfg: Configuration dictionary. Defaults to None.
"""
# update the config
_cfg = copy.deepcopy(SEQUENTIAL_TRAINER_DEFAULT_CONFIG)
_cfg.update(cfg if cfg is not None else {})
# store agents scope
agents_scope = agents_scope if agents_scope is not None else []
# initialize the base class
super().__init__(env=env, agents=agents, agents_scope=agents_scope, cfg=_cfg)
# init agents
if self.env.num_agents > 1:
for agent in self.agents:
agent.init(trainer_cfg=self.cfg)
else:
self.agents.init(trainer_cfg=self.cfg)
def train(self):
"""Train the agents sequentially.
This method executes the training loop for the agents. It performs the following steps:
* Pre-interaction: Perform any pre-interaction operations.
* Compute actions: Compute the actions for the agents.
* Step the environments: Step the environments with the computed actions.
* Record the environments' transitions: Record the transitions from the environments.
* Log custom environment data: Log custom environment data.
* Post-interaction: Perform any post-interaction operations.
* Reset the environments: Reset the environments if they are terminated or truncated.
"""
# init agent
self.agents.init(trainer_cfg=self.cfg)
self.agents.set_running_mode("train")
# reset env
states, infos = self.env.reset()
# training loop
for timestep in tqdm.tqdm(range(self.timesteps), disable=self.disable_progressbar):
# pre-interaction
self.agents.pre_interaction(timestep=timestep, timesteps=self.timesteps)
# compute actions
with torch.no_grad():
actions = self.agents.act(states, timestep=timestep, timesteps=self.timesteps)[0]
# step the environments
next_states, rewards, terminated, truncated, infos = self.env.step(actions)
# note: here we do not call render scene since it is done in the env.step() method
# record the environments' transitions
with torch.no_grad():
self.agents.record_transition(
states=states,
actions=actions,
rewards=rewards,
next_states=next_states,
terminated=terminated,
truncated=truncated,
infos=infos,
timestep=timestep,
timesteps=self.timesteps,
)
# log custom environment data
if "episode" in infos:
for k, v in infos["episode"].items():
if isinstance(v, torch.Tensor) and v.numel() == 1:
self.agents.track_data(f"EpisodeInfo / {k}", v.item())
# post-interaction
self.agents.post_interaction(timestep=timestep, timesteps=self.timesteps)
# reset the environments
# note: here we do not call reset scene since it is done in the env.step() method
# update states
states.copy_(next_states)
def eval(self) -> None:
"""Evaluate the agents sequentially.
This method executes the following steps in loop:
* Compute actions: Compute the actions for the agents.
* Step the environments: Step the environments with the computed actions.
* Record the environments' transitions: Record the transitions from the environments.
* Log custom environment data: Log custom environment data.
"""
# set running mode
if self.num_agents > 1:
for agent in self.agents:
agent.set_running_mode("eval")
else:
self.agents.set_running_mode("eval")
# single agent
if self.num_agents == 1:
self.single_agent_eval()
return
# reset env
states, infos = self.env.reset()
# evaluation loop
for timestep in tqdm.tqdm(range(self.initial_timestep, self.timesteps), disable=self.disable_progressbar):
# compute actions
with torch.no_grad():
actions = torch.vstack([
agent.act(states[scope[0] : scope[1]], timestep=timestep, timesteps=self.timesteps)[0]
for agent, scope in zip(self.agents, self.agents_scope)
])
# step the environments
next_states, rewards, terminated, truncated, infos = self.env.step(actions)
with torch.no_grad():
# write data to TensorBoard
for agent, scope in zip(self.agents, self.agents_scope):
# track data
agent.record_transition(
states=states[scope[0] : scope[1]],
actions=actions[scope[0] : scope[1]],
rewards=rewards[scope[0] : scope[1]],
next_states=next_states[scope[0] : scope[1]],
terminated=terminated[scope[0] : scope[1]],
truncated=truncated[scope[0] : scope[1]],
infos=infos,
timestep=timestep,
timesteps=self.timesteps,
)
# log custom environment data
if "log" in infos:
for k, v in infos["log"].items():
if isinstance(v, torch.Tensor) and v.numel() == 1:
agent.track_data(k, v.item())
# perform post-interaction
super(type(agent), agent).post_interaction(timestep=timestep, timesteps=self.timesteps)
# reset environments
# note: here we do not call reset scene since it is done in the env.step() method
states.copy_(next_states)
| 10,641 | Python | 36.340351 | 114 | 0.608589 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/wrappers/rl_games.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Wrapper to configure an :class:`RLTaskEnv` instance to RL-Games vectorized environment.
The following example shows how to wrap an environment for RL-Games and register the environment construction
for RL-Games :class:`Runner` class:
.. code-block:: python
from rl_games.common import env_configurations, vecenv
from omni.isaac.orbit_tasks.utils.wrappers.rl_games import RlGamesGpuEnv, RlGamesVecEnvWrapper
# configuration parameters
rl_device = "cuda:0"
clip_obs = 10.0
clip_actions = 1.0
# wrap around environment for rl-games
env = RlGamesVecEnvWrapper(env, rl_device, clip_obs, clip_actions)
# register the environment to rl-games registry
# note: in agents configuration: environment name must be "rlgpu"
vecenv.register(
"IsaacRlgWrapper", lambda config_name, num_actors, **kwargs: RlGamesGpuEnv(config_name, num_actors, **kwargs)
)
env_configurations.register("rlgpu", {"vecenv_type": "IsaacRlgWrapper", "env_creator": lambda **kwargs: env})
"""
# needed to import for allowing type-hinting:gym.spaces.Box | None
from __future__ import annotations
import gym.spaces # needed for rl-games incompatibility: https://github.com/Denys88/rl_games/issues/261
import gymnasium
import torch
from rl_games.common import env_configurations
from rl_games.common.vecenv import IVecEnv
from omni.isaac.orbit.envs import RLTaskEnv, VecEnvObs
"""
Vectorized environment wrapper.
"""
class RlGamesVecEnvWrapper(IVecEnv):
"""Wraps around Orbit environment for RL-Games.
This class wraps around the Orbit environment. Since RL-Games works directly on
GPU buffers, the wrapper handles moving of buffers from the simulation environment
to the same device as the learning agent. Additionally, it performs clipping of
observations and actions.
For algorithms like asymmetric actor-critic, RL-Games expects a dictionary for
observations. This dictionary contains "obs" and "states" which typically correspond
to the actor and critic observations respectively.
To use asymmetric actor-critic, the environment observations from :class:`RLTaskEnv`
must have the key or group name "critic". The observation group is used to set the
:attr:`num_states` (int) and :attr:`state_space` (:obj:`gym.spaces.Box`). These are
used by the learning agent in RL-Games to allocate buffers in the trajectory memory.
Since this is optional for some environments, the wrapper checks if these attributes exist.
If they don't then the wrapper defaults to zero as number of privileged observations.
.. caution::
This class must be the last wrapper in the wrapper chain. This is because the wrapper does not follow
the :class:`gym.Wrapper` interface. Any subsequent wrappers will need to be modified to work with this
wrapper.
Reference:
https://github.com/Denys88/rl_games/blob/master/rl_games/common/ivecenv.py
https://github.com/NVIDIA-Omniverse/IsaacGymEnvs
"""
def __init__(self, env: RLTaskEnv, rl_device: str, clip_obs: float, clip_actions: float):
"""Initializes the wrapper instance.
Args:
env: The environment to wrap around.
rl_device: The device on which agent computations are performed.
clip_obs: The clipping value for observations.
clip_actions: The clipping value for actions.
Raises:
ValueError: The environment is not inherited from :class:`RLTaskEnv`.
ValueError: If specified, the privileged observations (critic) are not of type :obj:`gym.spaces.Box`.
"""
# check that input is valid
if not isinstance(env.unwrapped, RLTaskEnv):
raise ValueError(f"The environment must be inherited from RLTaskEnv. Environment type: {type(env)}")
# initialize the wrapper
self.env = env
# store provided arguments
self._rl_device = rl_device
self._clip_obs = clip_obs
self._clip_actions = clip_actions
self._sim_device = env.unwrapped.device
# information for privileged observations
if self.state_space is None:
self.rlg_num_states = 0
else:
self.rlg_num_states = self.state_space.shape[0]
def __str__(self):
"""Returns the wrapper name and the :attr:`env` representation string."""
return (
f"<{type(self).__name__}{self.env}>"
f"\n\tObservations clipping: {self._clip_obs}"
f"\n\tActions clipping : {self._clip_actions}"
f"\n\tAgent device : {self._rl_device}"
f"\n\tAsymmetric-learning : {self.rlg_num_states != 0}"
)
def __repr__(self):
"""Returns the string representation of the wrapper."""
return str(self)
"""
Properties -- Gym.Wrapper
"""
@property
def render_mode(self) -> str | None:
"""Returns the :attr:`Env` :attr:`render_mode`."""
return self.env.render_mode
@property
def observation_space(self) -> gym.spaces.Box:
"""Returns the :attr:`Env` :attr:`observation_space`."""
# note: rl-games only wants single observation space
policy_obs_space = self.unwrapped.single_observation_space["policy"]
if not isinstance(policy_obs_space, gymnasium.spaces.Box):
raise NotImplementedError(
f"The RL-Games wrapper does not currently support observation space: '{type(policy_obs_space)}'."
f" If you need to support this, please modify the wrapper: {self.__class__.__name__},"
" and if you are nice, please send a merge-request."
)
# note: maybe should check if we are a sub-set of the actual space. don't do it right now since
# in RLTaskEnv we are setting action space as (-inf, inf).
return gym.spaces.Box(-self._clip_obs, self._clip_obs, policy_obs_space.shape)
@property
def action_space(self) -> gym.Space:
"""Returns the :attr:`Env` :attr:`action_space`."""
# note: rl-games only wants single action space
action_space = self.unwrapped.single_action_space
if not isinstance(action_space, gymnasium.spaces.Box):
raise NotImplementedError(
f"The RL-Games wrapper does not currently support action space: '{type(action_space)}'."
f" If you need to support this, please modify the wrapper: {self.__class__.__name__},"
" and if you are nice, please send a merge-request."
)
# return casted space in gym.spaces.Box (OpenAI Gym)
# note: maybe should check if we are a sub-set of the actual space. don't do it right now since
# in RLTaskEnv we are setting action space as (-inf, inf).
return gym.spaces.Box(-self._clip_actions, self._clip_actions, action_space.shape)
@classmethod
def class_name(cls) -> str:
"""Returns the class name of the wrapper."""
return cls.__name__
@property
def unwrapped(self) -> RLTaskEnv:
"""Returns the base environment of the wrapper.
This will be the bare :class:`gymnasium.Env` environment, underneath all layers of wrappers.
"""
return self.env.unwrapped
"""
Properties
"""
@property
def num_envs(self) -> int:
"""Returns the number of sub-environment instances."""
return self.unwrapped.num_envs
@property
def device(self) -> str:
"""Returns the base environment simulation device."""
return self.unwrapped.device
@property
def state_space(self) -> gym.spaces.Box | None:
"""Returns the :attr:`Env` :attr:`observation_space`."""
# note: rl-games only wants single observation space
critic_obs_space = self.unwrapped.single_observation_space.get("critic")
# check if we even have a critic obs
if critic_obs_space is None:
return None
elif not isinstance(critic_obs_space, gymnasium.spaces.Box):
raise NotImplementedError(
f"The RL-Games wrapper does not currently support state space: '{type(critic_obs_space)}'."
f" If you need to support this, please modify the wrapper: {self.__class__.__name__},"
" and if you are nice, please send a merge-request."
)
# return casted space in gym.spaces.Box (OpenAI Gym)
# note: maybe should check if we are a sub-set of the actual space. don't do it right now since
# in RLTaskEnv we are setting action space as (-inf, inf).
return gym.spaces.Box(-self._clip_obs, self._clip_obs, critic_obs_space.shape)
def get_number_of_agents(self) -> int:
"""Returns number of actors in the environment."""
return getattr(self, "num_agents", 1)
def get_env_info(self) -> dict:
"""Returns the Gym spaces for the environment."""
return {
"observation_space": self.observation_space,
"action_space": self.action_space,
"state_space": self.state_space,
}
"""
Operations - MDP
"""
def seed(self, seed: int = -1) -> int: # noqa: D102
return self.unwrapped.seed(seed)
def reset(self): # noqa: D102
obs_dict, _ = self.env.reset()
# process observations and states
return self._process_obs(obs_dict)
def step(self, actions): # noqa: D102
# move actions to sim-device
actions = actions.detach().clone().to(device=self._sim_device)
# clip the actions
actions = torch.clamp(actions, -self._clip_actions, self._clip_actions)
# perform environment step
obs_dict, rew, terminated, truncated, extras = self.env.step(actions)
# move time out information to the extras dict
# this is only needed for infinite horizon tasks
# note: only useful when `value_bootstrap` is True in the agent configuration
if not self.unwrapped.cfg.is_finite_horizon:
extras["time_outs"] = truncated.to(device=self._rl_device)
# process observations and states
obs_and_states = self._process_obs(obs_dict)
# move buffers to rl-device
# note: we perform clone to prevent issues when rl-device and sim-device are the same.
rew = rew.to(device=self._rl_device)
dones = (terminated | truncated).to(device=self._rl_device)
extras = {
k: v.to(device=self._rl_device, non_blocking=True) if hasattr(v, "to") else v for k, v in extras.items()
}
# remap extras from "log" to "episode"
if "log" in extras:
extras["episode"] = extras.pop("log")
return obs_and_states, rew, dones, extras
def close(self): # noqa: D102
return self.env.close()
"""
Helper functions
"""
def _process_obs(self, obs_dict: VecEnvObs) -> torch.Tensor | dict[str, torch.Tensor]:
"""Processing of the observations and states from the environment.
Note:
States typically refers to privileged observations for the critic function. It is typically used in
asymmetric actor-critic algorithms.
Args:
obs_dict: The current observations from environment.
Returns:
If environment provides states, then a dictionary containing the observations and states is returned.
Otherwise just the observations tensor is returned.
"""
# process policy obs
obs = obs_dict["policy"]
# clip the observations
obs = torch.clamp(obs, -self._clip_obs, self._clip_obs)
# move the buffer to rl-device
obs = obs.to(device=self._rl_device).clone()
# check if asymmetric actor-critic or not
if self.rlg_num_states > 0:
# acquire states from the environment if it exists
try:
states = obs_dict["critic"]
except AttributeError:
raise NotImplementedError("Environment does not define key 'critic' for privileged observations.")
# clip the states
states = torch.clamp(states, -self._clip_obs, self._clip_obs)
# move buffers to rl-device
states = states.to(self._rl_device).clone()
# convert to dictionary
return {"obs": obs, "states": states}
else:
return obs
"""
Environment Handler.
"""
class RlGamesGpuEnv(IVecEnv):
"""Thin wrapper to create instance of the environment to fit RL-Games runner."""
# TODO: Adding this for now but do we really need this?
def __init__(self, config_name: str, num_actors: int, **kwargs):
"""Initialize the environment.
Args:
config_name: The name of the environment configuration.
num_actors: The number of actors in the environment. This is not used in this wrapper.
"""
self.env: RlGamesVecEnvWrapper = env_configurations.configurations[config_name]["env_creator"](**kwargs)
def step(self, action): # noqa: D102
return self.env.step(action)
def reset(self): # noqa: D102
return self.env.reset()
def get_number_of_agents(self) -> int:
"""Get number of agents in the environment.
Returns:
The number of agents in the environment.
"""
return self.env.get_number_of_agents()
def get_env_info(self) -> dict:
"""Get the Gym spaces for the environment.
Returns:
The Gym spaces for the environment.
"""
return self.env.get_env_info()
| 13,803 | Python | 38.666667 | 117 | 0.638484 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/wrappers/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Sub-module for environment wrappers to different learning frameworks.
Wrappers allow you to modify the behavior of an environment without modifying the environment itself.
This is useful for modifying the observation space, action space, or reward function. Additionally,
they can be used to cast a given environment into the respective environment class definition used by
different learning frameworks. This operation may include handling of asymmetric actor-critic observations,
casting the data between different backends such `numpy` and `pytorch`, or organizing the returned data
into the expected data structure by the learning framework.
All wrappers work similar to the :class:`gymnasium.Wrapper` class. Using a wrapper is as simple as passing
the initialized environment instance to the wrapper constructor. However, since learning frameworks
expect different input and output data structures, their wrapper classes are not compatible with each other.
Thus, they should always be used in conjunction with the respective learning framework.
For instance, to wrap an environment in the `Stable-Baselines3`_ wrapper, you can do the following:
.. code-block:: python
from omni.isaac.orbit_tasks.utils.wrappers.sb3 import Sb3VecEnvWrapper
env = Sb3VecEnvWrapper(env)
.. _RL-Games: https://github.com/Denys88/rl_games
.. _RSL-RL: https://github.com/leggedrobotics/rsl_rl
.. _skrl: https://github.com/Toni-SM/skrl
.. _Stable-Baselines3: https://github.com/DLR-RM/stable-baselines3
"""
| 1,629 | Python | 45.571427 | 108 | 0.794352 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/wrappers/sb3.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Wrapper to configure an :class:`RLTaskEnv` instance to Stable-Baselines3 vectorized environment.
The following example shows how to wrap an environment for Stable-Baselines3:
.. code-block:: python
from omni.isaac.orbit_tasks.utils.wrappers.sb3 import Sb3VecEnvWrapper
env = Sb3VecEnvWrapper(env)
"""
# needed to import for allowing type-hinting: torch.Tensor | dict[str, torch.Tensor]
from __future__ import annotations
import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn # noqa: F401
from typing import Any
from stable_baselines3.common.utils import constant_fn
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs, VecEnvStepReturn
from omni.isaac.orbit.envs import RLTaskEnv
"""
Configuration Parser.
"""
def process_sb3_cfg(cfg: dict) -> dict:
"""Convert simple YAML types to Stable-Baselines classes/components.
Args:
cfg: A configuration dictionary.
Returns:
A dictionary containing the converted configuration.
Reference:
https://github.com/DLR-RM/rl-baselines3-zoo/blob/0e5eb145faefa33e7d79c7f8c179788574b20da5/utils/exp_manager.py#L358
"""
def update_dict(hyperparams: dict[str, Any]) -> dict[str, Any]:
for key, value in hyperparams.items():
if isinstance(value, dict):
update_dict(value)
else:
if key in ["policy_kwargs", "replay_buffer_class", "replay_buffer_kwargs"]:
hyperparams[key] = eval(value)
elif key in ["learning_rate", "clip_range", "clip_range_vf", "delta_std"]:
if isinstance(value, str):
_, initial_value = value.split("_")
initial_value = float(initial_value)
hyperparams[key] = lambda progress_remaining: progress_remaining * initial_value
elif isinstance(value, (float, int)):
# Negative value: ignore (ex: for clipping)
if value < 0:
continue
hyperparams[key] = constant_fn(float(value))
else:
raise ValueError(f"Invalid value for {key}: {hyperparams[key]}")
return hyperparams
# parse agent configuration and convert to classes
return update_dict(cfg)
"""
Vectorized environment wrapper.
"""
class Sb3VecEnvWrapper(VecEnv):
"""Wraps around Orbit environment for Stable Baselines3.
Isaac Sim internally implements a vectorized environment. However, since it is
still considered a single environment instance, Stable Baselines tries to wrap
around it using the :class:`DummyVecEnv`. This is only done if the environment
is not inheriting from their :class:`VecEnv`. Thus, this class thinly wraps
over the environment from :class:`RLTaskEnv`.
Note:
While Stable-Baselines3 supports Gym 0.26+ API, their vectorized environment
still uses the old API (i.e. it is closer to Gym 0.21). Thus, we implement
the old API for the vectorized environment.
We also add monitoring functionality that computes the un-discounted episode
return and length. This information is added to the info dicts under key `episode`.
In contrast to the Orbit environment, stable-baselines expect the following:
1. numpy datatype for MDP signals
2. a list of info dicts for each sub-environment (instead of a dict)
3. when environment has terminated, the observations from the environment should correspond
to the one after reset. The "real" final observation is passed using the info dicts
under the key ``terminal_observation``.
.. warning::
By the nature of physics stepping in Isaac Sim, it is not possible to forward the
simulation buffers without performing a physics step. Thus, reset is performed
inside the :meth:`step()` function after the actual physics step is taken.
Thus, the returned observations for terminated environments is the one after the reset.
.. caution::
This class must be the last wrapper in the wrapper chain. This is because the wrapper does not follow
the :class:`gym.Wrapper` interface. Any subsequent wrappers will need to be modified to work with this
wrapper.
Reference:
1. https://stable-baselines3.readthedocs.io/en/master/guide/vec_envs.html
2. https://stable-baselines3.readthedocs.io/en/master/common/monitor.html
"""
def __init__(self, env: RLTaskEnv):
"""Initialize the wrapper.
Args:
env: The environment to wrap around.
Raises:
ValueError: When the environment is not an instance of :class:`RLTaskEnv`.
"""
# check that input is valid
if not isinstance(env.unwrapped, RLTaskEnv):
raise ValueError(f"The environment must be inherited from RLTaskEnv. Environment type: {type(env)}")
# initialize the wrapper
self.env = env
# collect common information
self.num_envs = self.unwrapped.num_envs
self.sim_device = self.unwrapped.device
self.render_mode = self.unwrapped.render_mode
# obtain gym spaces
# note: stable-baselines3 does not like when we have unbounded action space so
# we set it to some high value here. Maybe this is not general but something to think about.
observation_space = self.unwrapped.single_observation_space["policy"]
action_space = self.unwrapped.single_action_space
if isinstance(action_space, gym.spaces.Box) and action_space.is_bounded() != "both":
action_space = gym.spaces.Box(low=-100, high=100, shape=action_space.shape)
# initialize vec-env
VecEnv.__init__(self, self.num_envs, observation_space, action_space)
# add buffer for logging episodic information
self._ep_rew_buf = torch.zeros(self.num_envs, device=self.sim_device)
self._ep_len_buf = torch.zeros(self.num_envs, device=self.sim_device)
def __str__(self):
"""Returns the wrapper name and the :attr:`env` representation string."""
return f"<{type(self).__name__}{self.env}>"
def __repr__(self):
"""Returns the string representation of the wrapper."""
return str(self)
"""
Properties -- Gym.Wrapper
"""
@classmethod
def class_name(cls) -> str:
"""Returns the class name of the wrapper."""
return cls.__name__
@property
def unwrapped(self) -> RLTaskEnv:
"""Returns the base environment of the wrapper.
This will be the bare :class:`gymnasium.Env` environment, underneath all layers of wrappers.
"""
return self.env.unwrapped
"""
Properties
"""
def get_episode_rewards(self) -> list[float]:
"""Returns the rewards of all the episodes."""
return self._ep_rew_buf.cpu().tolist()
def get_episode_lengths(self) -> list[int]:
"""Returns the number of time-steps of all the episodes."""
return self._ep_len_buf.cpu().tolist()
"""
Operations - MDP
"""
def seed(self, seed: int | None = None) -> list[int | None]: # noqa: D102
return [self.unwrapped.seed(seed)] * self.unwrapped.num_envs
def reset(self) -> VecEnvObs: # noqa: D102
obs_dict, _ = self.env.reset()
# convert data types to numpy depending on backend
return self._process_obs(obs_dict)
def step_async(self, actions): # noqa: D102
# convert input to numpy array
if not isinstance(actions, torch.Tensor):
actions = np.asarray(actions)
actions = torch.from_numpy(actions).to(device=self.sim_device, dtype=torch.float32)
else:
actions = actions.to(device=self.sim_device, dtype=torch.float32)
# convert to tensor
self._async_actions = actions
def step_wait(self) -> VecEnvStepReturn: # noqa: D102
# record step information
obs_dict, rew, terminated, truncated, extras = self.env.step(self._async_actions)
# update episode un-discounted return and length
self._ep_rew_buf += rew
self._ep_len_buf += 1
# compute reset ids
dones = terminated | truncated
reset_ids = (dones > 0).nonzero(as_tuple=False)
# convert data types to numpy depending on backend
# note: RLTaskEnv uses torch backend (by default).
obs = self._process_obs(obs_dict)
rew = rew.detach().cpu().numpy()
terminated = terminated.detach().cpu().numpy()
truncated = truncated.detach().cpu().numpy()
dones = dones.detach().cpu().numpy()
# convert extra information to list of dicts
infos = self._process_extras(obs, terminated, truncated, extras, reset_ids)
# reset info for terminated environments
self._ep_rew_buf[reset_ids] = 0
self._ep_len_buf[reset_ids] = 0
return obs, rew, dones, infos
def close(self): # noqa: D102
self.env.close()
def get_attr(self, attr_name, indices=None): # noqa: D102
# resolve indices
if indices is None:
indices = slice(None)
num_indices = self.num_envs
else:
num_indices = len(indices)
# obtain attribute value
attr_val = getattr(self.env, attr_name)
# return the value
if not isinstance(attr_val, torch.Tensor):
return [attr_val] * num_indices
else:
return attr_val[indices].detach().cpu().numpy()
def set_attr(self, attr_name, value, indices=None): # noqa: D102
raise NotImplementedError("Setting attributes is not supported.")
def env_method(self, method_name: str, *method_args, indices=None, **method_kwargs): # noqa: D102
if method_name == "render":
# gymnasium does not support changing render mode at runtime
return self.env.render()
else:
# this isn't properly implemented but it is not necessary.
# mostly done for completeness.
env_method = getattr(self.env, method_name)
return env_method(*method_args, indices=indices, **method_kwargs)
def env_is_wrapped(self, wrapper_class, indices=None): # noqa: D102
raise NotImplementedError("Checking if environment is wrapped is not supported.")
def get_images(self): # noqa: D102
raise NotImplementedError("Getting images is not supported.")
"""
Helper functions.
"""
def _process_obs(self, obs_dict: torch.Tensor | dict[str, torch.Tensor]) -> np.ndarray | dict[str, np.ndarray]:
"""Convert observations into NumPy data type."""
# Sb3 doesn't support asymmetric observation spaces, so we only use "policy"
obs = obs_dict["policy"]
# note: RLTaskEnv uses torch backend (by default).
if isinstance(obs, dict):
for key, value in obs.items():
obs[key] = value.detach().cpu().numpy()
elif isinstance(obs, torch.Tensor):
obs = obs.detach().cpu().numpy()
else:
raise NotImplementedError(f"Unsupported data type: {type(obs)}")
return obs
def _process_extras(
self, obs: np.ndarray, terminated: np.ndarray, truncated: np.ndarray, extras: dict, reset_ids: np.ndarray
) -> list[dict[str, Any]]:
"""Convert miscellaneous information into dictionary for each sub-environment."""
# create empty list of dictionaries to fill
infos: list[dict[str, Any]] = [dict.fromkeys(extras.keys()) for _ in range(self.num_envs)]
# fill-in information for each sub-environment
# note: This loop becomes slow when number of environments is large.
for idx in range(self.num_envs):
# fill-in episode monitoring info
if idx in reset_ids:
infos[idx]["episode"] = dict()
infos[idx]["episode"]["r"] = float(self._ep_rew_buf[idx])
infos[idx]["episode"]["l"] = float(self._ep_len_buf[idx])
else:
infos[idx]["episode"] = None
# fill-in bootstrap information
infos[idx]["TimeLimit.truncated"] = truncated[idx] and not terminated[idx]
# fill-in information from extras
for key, value in extras.items():
# 1. remap extra episodes information safely
# 2. for others just store their values
if key == "log":
# only log this data for episodes that are terminated
if infos[idx]["episode"] is not None:
for sub_key, sub_value in value.items():
infos[idx]["episode"][sub_key] = sub_value
else:
infos[idx][key] = value[idx]
# add information about terminal observation separately
if idx in reset_ids:
# extract terminal observations
if isinstance(obs, dict):
terminal_obs = dict.fromkeys(obs.keys())
for key, value in obs.items():
terminal_obs[key] = value[idx]
else:
terminal_obs = obs[idx]
# add info to dict
infos[idx]["terminal_observation"] = terminal_obs
else:
infos[idx]["terminal_observation"] = None
# return list of dictionaries
return infos
| 13,706 | Python | 39.196481 | 123 | 0.621844 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/wrappers/rsl_rl/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Wrappers and utilities to configure an :class:`RLTaskEnv` for RSL-RL library."""
from .exporter import export_policy_as_jit, export_policy_as_onnx
from .rl_cfg import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg
from .vecenv_wrapper import RslRlVecEnvWrapper
| 410 | Python | 36.363633 | 88 | 0.795122 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/wrappers/rsl_rl/vecenv_wrapper.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Wrapper to configure an :class:`RLTaskEnv` instance to RSL-RL vectorized environment.
The following example shows how to wrap an environment for RSL-RL:
.. code-block:: python
from omni.isaac.orbit_tasks.utils.wrappers.rsl_rl import RslRlVecEnvWrapper
env = RslRlVecEnvWrapper(env)
"""
import gymnasium as gym
import torch
from rsl_rl.env import VecEnv
from omni.isaac.orbit.envs import RLTaskEnv
class RslRlVecEnvWrapper(VecEnv):
"""Wraps around Orbit environment for RSL-RL library
To use asymmetric actor-critic, the environment instance must have the attributes :attr:`num_privileged_obs` (int).
This is used by the learning agent to allocate buffers in the trajectory memory. Additionally, the returned
observations should have the key "critic" which corresponds to the privileged observations. Since this is
optional for some environments, the wrapper checks if these attributes exist. If they don't then the wrapper
defaults to zero as number of privileged observations.
.. caution::
This class must be the last wrapper in the wrapper chain. This is because the wrapper does not follow
the :class:`gym.Wrapper` interface. Any subsequent wrappers will need to be modified to work with this
wrapper.
Reference:
https://github.com/leggedrobotics/rsl_rl/blob/master/rsl_rl/env/vec_env.py
"""
def __init__(self, env: RLTaskEnv):
"""Initializes the wrapper.
Note:
The wrapper calls :meth:`reset` at the start since the RSL-RL runner does not call reset.
Args:
env: The environment to wrap around.
Raises:
ValueError: When the environment is not an instance of :class:`RLTaskEnv`.
"""
# check that input is valid
if not isinstance(env.unwrapped, RLTaskEnv):
raise ValueError(f"The environment must be inherited from RLTaskEnv. Environment type: {type(env)}")
# initialize the wrapper
self.env = env
# store information required by wrapper
self.num_envs = self.unwrapped.num_envs
self.device = self.unwrapped.device
self.max_episode_length = self.unwrapped.max_episode_length
self.num_actions = self.unwrapped.action_manager.total_action_dim
self.num_obs = self.unwrapped.observation_manager.group_obs_dim["policy"][0]
# -- privileged observations
if "critic" in self.unwrapped.observation_manager.group_obs_dim:
self.num_privileged_obs = self.unwrapped.observation_manager.group_obs_dim["critic"][0]
else:
self.num_privileged_obs = 0
# reset at the start since the RSL-RL runner does not call reset
self.env.reset()
def __str__(self):
"""Returns the wrapper name and the :attr:`env` representation string."""
return f"<{type(self).__name__}{self.env}>"
def __repr__(self):
"""Returns the string representation of the wrapper."""
return str(self)
"""
Properties -- Gym.Wrapper
"""
@property
def cfg(self) -> object:
"""Returns the configuration class instance of the environment."""
return self.unwrapped.cfg
@property
def render_mode(self) -> str | None:
"""Returns the :attr:`Env` :attr:`render_mode`."""
return self.env.render_mode
@property
def observation_space(self) -> gym.Space:
"""Returns the :attr:`Env` :attr:`observation_space`."""
return self.env.observation_space
@property
def action_space(self) -> gym.Space:
"""Returns the :attr:`Env` :attr:`action_space`."""
return self.env.action_space
@classmethod
def class_name(cls) -> str:
"""Returns the class name of the wrapper."""
return cls.__name__
@property
def unwrapped(self) -> RLTaskEnv:
"""Returns the base environment of the wrapper.
This will be the bare :class:`gymnasium.Env` environment, underneath all layers of wrappers.
"""
return self.env.unwrapped
"""
Properties
"""
def get_observations(self) -> tuple[torch.Tensor, dict]:
"""Returns the current observations of the environment."""
obs_dict = self.unwrapped.observation_manager.compute()
return obs_dict["policy"], {"observations": obs_dict}
@property
def episode_length_buf(self) -> torch.Tensor:
"""The episode length buffer."""
return self.unwrapped.episode_length_buf
@episode_length_buf.setter
def episode_length_buf(self, value: torch.Tensor):
"""Set the episode length buffer.
Note:
This is needed to perform random initialization of episode lengths in RSL-RL.
"""
self.unwrapped.episode_length_buf = value
"""
Operations - MDP
"""
def seed(self, seed: int = -1) -> int: # noqa: D102
return self.unwrapped.seed(seed)
def reset(self) -> tuple[torch.Tensor, dict]: # noqa: D102
# reset the environment
obs_dict, _ = self.env.reset()
# return observations
return obs_dict["policy"], {"observations": obs_dict}
def step(self, actions: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, dict]:
# record step information
obs_dict, rew, terminated, truncated, extras = self.env.step(actions)
# compute dones for compatibility with RSL-RL
dones = (terminated | truncated).to(dtype=torch.long)
# move extra observations to the extras dict
obs = obs_dict["policy"]
extras["observations"] = obs_dict
# move time out information to the extras dict
# this is only needed for infinite horizon tasks
if not self.unwrapped.cfg.is_finite_horizon:
extras["time_outs"] = truncated
# return the step information
return obs, rew, dones, extras
def close(self): # noqa: D102
return self.env.close()
| 6,120 | Python | 33.778409 | 119 | 0.650817 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/wrappers/rsl_rl/exporter.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import copy
import os
import torch
def export_policy_as_jit(actor_critic: object, path: str, filename="policy.pt"):
"""Export policy into a Torch JIT file.
Args:
actor_critic: The actor-critic torch module.
path: The path to the saving directory.
filename: The name of exported JIT file. Defaults to "policy.pt".
Reference:
https://github.com/leggedrobotics/legged_gym/blob/master/legged_gym/utils/helpers.py#L180
"""
policy_exporter = _TorchPolicyExporter(actor_critic)
policy_exporter.export(path, filename)
def export_policy_as_onnx(actor_critic: object, path: str, filename="policy.onnx", verbose=False):
"""Export policy into a Torch ONNX file.
Args:
actor_critic: The actor-critic torch module.
path: The path to the saving directory.
filename: The name of exported JIT file. Defaults to "policy.pt".
verbose: Whether to print the model summary. Defaults to False.
"""
if not os.path.exists(path):
os.makedirs(path, exist_ok=True)
policy_exporter = _OnnxPolicyExporter(actor_critic, verbose)
policy_exporter.export(path, filename)
"""
Helper Classes - Private.
"""
class _TorchPolicyExporter(torch.nn.Module):
"""Exporter of actor-critic into JIT file.
Reference:
https://github.com/leggedrobotics/legged_gym/blob/master/legged_gym/utils/helpers.py#L193
"""
def __init__(self, actor_critic):
super().__init__()
self.actor = copy.deepcopy(actor_critic.actor)
self.is_recurrent = actor_critic.is_recurrent
if self.is_recurrent:
self.rnn = copy.deepcopy(actor_critic.memory_a.rnn)
self.rnn.cpu()
self.register_buffer("hidden_state", torch.zeros(self.rnn.num_layers, 1, self.rnn.hidden_size))
self.register_buffer("cell_state", torch.zeros(self.rnn.num_layers, 1, self.rnn.hidden_size))
self.forward = self.forward_lstm
self.reset = self.reset_memory
def forward_lstm(self, x):
x, (h, c) = self.rnn(x.unsqueeze(0), (self.hidden_state, self.cell_state))
self.hidden_state[:] = h
self.cell_state[:] = c
x = x.squeeze(0)
return self.actor(x)
def forward(self, x):
return self.actor(x)
@torch.jit.export
def reset(self):
pass
def reset_memory(self):
self.hidden_state[:] = 0.0
self.cell_state[:] = 0.0
def export(self, path, filename):
os.makedirs(path, exist_ok=True)
path = os.path.join(path, filename)
self.to("cpu")
traced_script_module = torch.jit.script(self)
traced_script_module.save(path)
class _OnnxPolicyExporter(torch.nn.Module):
"""Exporter of actor-critic into ONNX file."""
def __init__(self, actor_critic, verbose=False):
super().__init__()
self.verbose = verbose
self.actor = copy.deepcopy(actor_critic.actor)
self.is_recurrent = actor_critic.is_recurrent
if self.is_recurrent:
self.rnn = copy.deepcopy(actor_critic.memory_a.rnn)
self.rnn.cpu()
self.forward = self.forward_lstm
def forward_lstm(self, x_in, h_in, c_in):
x, (h, c) = self.rnn(x_in.unsqueeze(0), (h_in, c_in))
x = x.squeeze(0)
return self.actor(x), h, c
def forward(self, x):
return self.actor(x)
def export(self, path, filename):
self.to("cpu")
if self.is_recurrent:
obs = torch.zeros(1, self.rnn.input_size)
h_in = torch.zeros(self.rnn.num_layers, 1, self.rnn.hidden_size)
c_in = torch.zeros(self.rnn.num_layers, 1, self.rnn.hidden_size)
actions, h_out, c_out = self(obs, h_in, c_in)
torch.onnx.export(
self,
(obs, h_in, c_in),
os.path.join(path, filename),
export_params=True,
opset_version=11,
verbose=self.verbose,
input_names=["obs", "h_in", "c_in"],
output_names=["actions", "h_out", "c_out"],
dynamic_axes={},
)
else:
obs = torch.zeros(1, self.actor[0].in_features)
torch.onnx.export(
self,
obs,
os.path.join(path, filename),
export_params=True,
opset_version=11,
verbose=self.verbose,
input_names=["obs"],
output_names=["actions"],
dynamic_axes={},
)
| 4,718 | Python | 32 | 107 | 0.583934 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/wrappers/rsl_rl/rl_cfg.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from dataclasses import MISSING
from typing import Literal
from omni.isaac.orbit.utils import configclass
@configclass
class RslRlPpoActorCriticCfg:
"""Configuration for the PPO actor-critic networks."""
class_name: str = "ActorCritic"
"""The policy class name. Default is ActorCritic."""
init_noise_std: float = MISSING
"""The initial noise standard deviation for the policy."""
actor_hidden_dims: list[int] = MISSING
"""The hidden dimensions of the actor network."""
critic_hidden_dims: list[int] = MISSING
"""The hidden dimensions of the critic network."""
activation: str = MISSING
"""The activation function for the actor and critic networks."""
@configclass
class RslRlPpoAlgorithmCfg:
"""Configuration for the PPO algorithm."""
class_name: str = "PPO"
"""The algorithm class name. Default is PPO."""
value_loss_coef: float = MISSING
"""The coefficient for the value loss."""
use_clipped_value_loss: bool = MISSING
"""Whether to use clipped value loss."""
clip_param: float = MISSING
"""The clipping parameter for the policy."""
entropy_coef: float = MISSING
"""The coefficient for the entropy loss."""
num_learning_epochs: int = MISSING
"""The number of learning epochs per update."""
num_mini_batches: int = MISSING
"""The number of mini-batches per update."""
learning_rate: float = MISSING
"""The learning rate for the policy."""
schedule: str = MISSING
"""The learning rate schedule."""
gamma: float = MISSING
"""The discount factor."""
lam: float = MISSING
"""The lambda parameter for Generalized Advantage Estimation (GAE)."""
desired_kl: float = MISSING
"""The desired KL divergence."""
max_grad_norm: float = MISSING
"""The maximum gradient norm."""
@configclass
class RslRlOnPolicyRunnerCfg:
"""Configuration of the runner for on-policy algorithms."""
seed: int = 42
"""The seed for the experiment. Default is 42."""
device: str = "cuda"
"""The device for the rl-agent. Default is cuda."""
num_steps_per_env: int = MISSING
"""The number of steps per environment per update."""
max_iterations: int = MISSING
"""The maximum number of iterations."""
empirical_normalization: bool = MISSING
"""Whether to use empirical normalization."""
policy: RslRlPpoActorCriticCfg = MISSING
"""The policy configuration."""
algorithm: RslRlPpoAlgorithmCfg = MISSING
"""The algorithm configuration."""
##
# Checkpointing parameters
##
save_interval: int = MISSING
"""The number of iterations between saves."""
experiment_name: str = MISSING
"""The experiment name."""
run_name: str = ""
"""The run name. Default is empty string.
The name of the run directory is typically the time-stamp at execution. If the run name is not empty,
then it is appended to the run directory's name, i.e. the logging directory's name will become
``{time-stamp}_{run_name}``.
"""
##
# Logging parameters
##
logger: Literal["tensorboard", "neptune", "wandb"] = "tensorboard"
"""The logger to use. Default is tensorboard."""
neptune_project: str = "orbit"
"""The neptune project name. Default is "orbit"."""
wandb_project: str = "orbit"
"""The wandb project name. Default is "orbit"."""
##
# Loading parameters
##
resume: bool = False
"""Whether to resume. Default is False."""
load_run: str = ".*"
"""The run directory to load. Default is ".*" (all).
If regex expression, the latest (alphabetical order) matching run will be loaded.
"""
load_checkpoint: str = "model_.*.pt"
"""The checkpoint file to load. Default is ``"model_.*.pt"`` (all).
If regex expression, the latest (alphabetical order) matching file will be loaded.
"""
| 4,026 | Python | 25.846666 | 105 | 0.654993 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/data_collector/__init__.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Sub-module for data collection utilities.
All post-processed robomimic compatible datasets share the same data structure.
A single dataset is a single HDF5 file. The stored data follows the structure provided
`here <https://robomimic.github.io/docs/datasets/overview.html#dataset-structure>`_.
The collector takes input data in its batched format and stores them as different
demonstrations, each corresponding to a given environment index. The demonstrations are
flushed to disk when the :meth:`RobomimicDataCollector.flush` is called for the
respective environments. All the data is saved when the
:meth:`RobomimicDataCollector.close()` is called.
The following sample shows how to use the :class:`RobomimicDataCollector` to store
random data in a dataset.
.. code-block:: python
import os
import torch
from omni.isaac.orbit_tasks.utils.data_collector import RobomimicDataCollector
# name of the environment (needed by robomimic)
task_name = "Isaac-Franka-Lift-v0"
# specify directory for logging experiments
test_dir = os.path.dirname(os.path.abspath(__file__))
log_dir = os.path.join(test_dir, "logs", "demos")
# name of the file to save data
filename = "hdf_dataset.hdf5"
# number of episodes to collect
num_demos = 10
# number of environments to simulate
num_envs = 4
# create data-collector
collector_interface = RobomimicDataCollector(task_name, log_dir, filename, num_demos)
# reset the collector
collector_interface.reset()
while not collector_interface.is_stopped():
# generate random data to store
# -- obs
obs = {
"joint_pos": torch.randn(num_envs, 10),
"joint_vel": torch.randn(num_envs, 10)
}
# -- actions
actions = torch.randn(num_envs, 10)
# -- rewards
rewards = torch.randn(num_envs)
# -- dones
dones = torch.rand(num_envs) > 0.5
# store signals
# -- obs
for key, value in obs.items():
collector_interface.add(f"obs/{key}", value)
# -- actions
collector_interface.add("actions", actions)
# -- next_obs
for key, value in obs.items():
collector_interface.add(f"next_obs/{key}", value.cpu().numpy())
# -- rewards
collector_interface.add("rewards", rewards)
# -- dones
collector_interface.add("dones", dones)
# flush data from collector for successful environments
# note: in this case we flush all the time
reset_env_ids = dones.nonzero(as_tuple=False).squeeze(-1)
collector_interface.flush(reset_env_ids)
# close collector
collector_interface.close()
"""
from .robomimic_data_collector import RobomimicDataCollector
| 2,834 | Python | 32.352941 | 88 | 0.688073 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/omni/isaac/orbit_tasks/utils/data_collector/robomimic_data_collector.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Interface to collect and store data from the environment using format from `robomimic`."""
# needed to import for allowing type-hinting: np.ndarray | torch.Tensor
from __future__ import annotations
import h5py
import json
import numpy as np
import os
import torch
from collections.abc import Iterable
import carb
class RobomimicDataCollector:
"""Data collection interface for robomimic.
This class implements a data collector interface for saving simulation states to disk.
The data is stored in `HDF5`_ binary data format. The class is useful for collecting
demonstrations. The collected data follows the `structure`_ from robomimic.
All datasets in `robomimic` require the observations and next observations obtained
from before and after the environment step. These are stored as a dictionary of
observations in the keys "obs" and "next_obs" respectively.
For certain agents in `robomimic`, the episode data should have the following
additional keys: "actions", "rewards", "dones". This behavior can be altered by changing
the dataset keys required in the training configuration for the respective learning agent.
For reference on datasets, please check the robomimic `documentation`.
.. _HDF5: https://www.h5py.org/
.. _structure: https://robomimic.github.io/docs/datasets/overview.html#dataset-structure
.. _documentation: https://github.com/ARISE-Initiative/robomimic/blob/master/robomimic/config/base_config.py#L167-L173
"""
def __init__(
self,
env_name: str,
directory_path: str,
filename: str = "test",
num_demos: int = 1,
flush_freq: int = 1,
env_config: dict | None = None,
):
"""Initializes the data collection wrapper.
Args:
env_name: The name of the environment.
directory_path: The path to store collected data.
filename: The basename of the saved file. Defaults to "test".
num_demos: Number of demonstrations to record until stopping. Defaults to 1.
flush_freq: Frequency to dump data to disk. Defaults to 1.
env_config: The configuration for the environment. Defaults to None.
"""
# save input arguments
self._env_name = env_name
self._env_config = env_config
self._directory = os.path.abspath(directory_path)
self._filename = filename
self._num_demos = num_demos
self._flush_freq = flush_freq
# print info
print(self.__str__())
# create directory it doesn't exist
if not os.path.isdir(self._directory):
os.makedirs(self._directory)
# placeholder for current hdf5 file object
self._h5_file_stream = None
self._h5_data_group = None
self._h5_episode_group = None
# store count of demos within episode
self._demo_count = 0
# flags for setting up
self._is_first_interaction = True
self._is_stop = False
# create buffers to store data
self._dataset = dict()
def __del__(self):
"""Destructor for data collector."""
if not self._is_stop:
self.close()
def __str__(self) -> str:
"""Represents the data collector as a string."""
msg = "Dataset collector <class RobomimicDataCollector> object"
msg += f"\tStoring trajectories in directory: {self._directory}\n"
msg += f"\tNumber of demos for collection : {self._num_demos}\n"
msg += f"\tFrequency for saving data to disk: {self._flush_freq}\n"
return msg
"""
Properties
"""
@property
def demo_count(self) -> int:
"""The number of demos collected so far."""
return self._demo_count
"""
Operations.
"""
def is_stopped(self) -> bool:
"""Whether data collection is stopped or not.
Returns:
True if data collection has stopped.
"""
return self._is_stop
def reset(self):
"""Reset the internals of data logger."""
# setup the file to store data in
if self._is_first_interaction:
self._demo_count = 0
self._create_new_file(self._filename)
self._is_first_interaction = False
# clear out existing buffers
self._dataset = dict()
def add(self, key: str, value: np.ndarray | torch.Tensor):
"""Add a key-value pair to the dataset.
The key can be nested by using the "/" character. For example:
"obs/joint_pos". Currently only two-level nesting is supported.
Args:
key: The key name.
value: The corresponding value
of shape (N, ...), where `N` is number of environments.
Raises:
ValueError: When provided key has sub-keys more than 2. Example: "obs/joints/pos", instead
of "obs/joint_pos".
"""
# check if data should be recorded
if self._is_first_interaction:
carb.log_warn("Please call reset before adding new data. Calling reset...")
self.reset()
if self._is_stop:
carb.log_warn(f"Desired number of demonstrations collected: {self._demo_count} >= {self._num_demos}.")
return
# check datatype
if isinstance(value, torch.Tensor):
value = value.cpu().numpy()
else:
value = np.asarray(value)
# check if there are sub-keys
sub_keys = key.split("/")
num_sub_keys = len(sub_keys)
if len(sub_keys) > 2:
raise ValueError(f"Input key '{key}' has elements {num_sub_keys} which is more than two.")
# add key to dictionary if it doesn't exist
for i in range(value.shape[0]):
# demo index
if f"env_{i}" not in self._dataset:
self._dataset[f"env_{i}"] = dict()
# key index
if num_sub_keys == 2:
# create keys
if sub_keys[0] not in self._dataset[f"env_{i}"]:
self._dataset[f"env_{i}"][sub_keys[0]] = dict()
if sub_keys[1] not in self._dataset[f"env_{i}"][sub_keys[0]]:
self._dataset[f"env_{i}"][sub_keys[0]][sub_keys[1]] = list()
# add data to key
self._dataset[f"env_{i}"][sub_keys[0]][sub_keys[1]].append(value[i])
else:
# create keys
if sub_keys[0] not in self._dataset[f"env_{i}"]:
self._dataset[f"env_{i}"][sub_keys[0]] = list()
# add data to key
self._dataset[f"env_{i}"][sub_keys[0]].append(value[i])
def flush(self, env_ids: Iterable[int] = (0,)):
"""Flush the episode data based on environment indices.
Args:
env_ids: Environment indices to write data for. Defaults to (0).
"""
# check that data is being recorded
if self._h5_file_stream is None or self._h5_data_group is None:
carb.log_error("No file stream has been opened. Please call reset before flushing data.")
return
# iterate over each environment and add their data
for index in env_ids:
# data corresponding to demo
env_dataset = self._dataset[f"env_{index}"]
# create episode group based on demo count
h5_episode_group = self._h5_data_group.create_group(f"demo_{self._demo_count}")
# store number of steps taken
h5_episode_group.attrs["num_samples"] = len(env_dataset["actions"])
# store other data from dictionary
for key, value in env_dataset.items():
if isinstance(value, dict):
# create group
key_group = h5_episode_group.create_group(key)
# add sub-keys values
for sub_key, sub_value in value.items():
key_group.create_dataset(sub_key, data=np.array(sub_value))
else:
h5_episode_group.create_dataset(key, data=np.array(value))
# increment total step counts
self._h5_data_group.attrs["total"] += h5_episode_group.attrs["num_samples"]
# increment total demo counts
self._demo_count += 1
# reset buffer for environment
self._dataset[f"env_{index}"] = dict()
# dump at desired frequency
if self._demo_count % self._flush_freq == 0:
self._h5_file_stream.flush()
print(f">>> Flushing data to disk. Collected demos: {self._demo_count} / {self._num_demos}")
# if demos collected then stop
if self._demo_count >= self._num_demos:
print(f">>> Desired number of demonstrations collected: {self._demo_count} >= {self._num_demos}.")
self.close()
# break out of loop
break
def close(self):
"""Stop recording and save the file at its current state."""
if not self._is_stop:
print(f">>> Closing recording of data. Collected demos: {self._demo_count} / {self._num_demos}")
# close the file safely
if self._h5_file_stream is not None:
self._h5_file_stream.close()
# mark that data collection is stopped
self._is_stop = True
"""
Helper functions.
"""
def _create_new_file(self, fname: str):
"""Create a new HDF5 file for writing episode info into.
Reference:
https://robomimic.github.io/docs/datasets/overview.html
Args:
fname: The base name of the file.
"""
if not fname.endswith(".hdf5"):
fname += ".hdf5"
# define path to file
hdf5_path = os.path.join(self._directory, fname)
# construct the stream object
self._h5_file_stream = h5py.File(hdf5_path, "w")
# create group to store data
self._h5_data_group = self._h5_file_stream.create_group("data")
# stores total number of samples accumulated across demonstrations
self._h5_data_group.attrs["total"] = 0
# store the environment meta-info
# -- we use gym environment type
# Ref: https://github.com/ARISE-Initiative/robomimic/blob/master/robomimic/envs/env_base.py#L15
env_type = 2
# -- check if env config provided
if self._env_config is None:
self._env_config = dict()
# -- add info
self._h5_data_group.attrs["env_args"] = json.dumps({
"env_name": self._env_name,
"type": env_type,
"env_kwargs": self._env_config,
})
| 10,907 | Python | 37.544169 | 122 | 0.581095 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/docs/CHANGELOG.rst | Changelog
---------
0.6.1 (2024-04-16)
~~~~~~~~~~~~~~~~~~
Added
^^^^^
* Added a new environment ``Isaac-Repose-Cube-Allegro-v0`` and ``Isaac-Repose-Allegro-Cube-NoVelObs-v0``
for the Allegro hand to reorient a cube. It is based on the IsaacGymEnvs Allegro hand environment.
0.6.0 (2024-03-10)
~~~~~~~~~~~~~~~~~~
Added
^^^^^
* Added a new environment ``Isaac-Open-Drawer-Franka-v0`` for the Franka arm to open a drawer. It is
based on the IsaacGymEnvs cabinet environment.
Fixed
^^^^^
* Fixed logging of extra information for RL-Games wrapper. It expected the extra information to be under the
key ``"episode"``, but Orbit used the key ``"log"``. The wrapper now remaps the key to ``"episode"``.
0.5.7 (2024-02-28)
~~~~~~~~~~~~~~~~~~
Fixed
^^^^^
* Updated the RL wrapper for the skrl library to the latest release (>= 1.1.0)
0.5.6 (2024-02-21)
~~~~~~~~~~~~~~~~~~
Fixed
^^^^^
* Fixed the configuration parsing to support a pre-initialized configuration object.
0.5.5 (2024-02-05)
~~~~~~~~~~~~~~~~~~
Fixed
^^^^^
* Pinned :mod:`torch` version to 2.0.1 in the setup.py to keep parity version of :mod:`torch` supplied by
Isaac 2023.1.1, and prevent version incompatibility between :mod:`torch` ==2.2 and
:mod:`typing-extensions` ==3.7.4.3
0.5.4 (2024-02-06)
~~~~~~~~~~~~~~~~~~
Added
^^^^^
* Added a check for the flag :attr:`omni.isaac.orbit.envs.RLTaskEnvCfg.is_finite_horizon`
in the RSL-RL and RL-Games wrappers to handle the finite horizon tasks properly. Earlier,
the wrappers were always assuming the tasks to be infinite horizon tasks and returning a
time-out signals when the episode length was reached.
0.5.3 (2023-11-16)
~~~~~~~~~~~~~~~~~~
Fixed
^^^^^
* Added raising of error in the :meth:`omni.isaac.orbit_tasks.utils.importer.import_all` method to make sure
all the packages are imported properly. Previously, error was being caught and ignored.
0.5.2 (2023-11-08)
~~~~~~~~~~~~~~~~~~
Fixed
^^^^^
* Fixed the RL wrappers for Stable-Baselines3 and RL-Games. It now works with their most recent versions.
* Fixed the :meth:`get_checkpoint_path` to allow any in-between sub-folders between the run directory and the
checkpoint directory.
0.5.1 (2023-11-04)
~~~~~~~~~~~~~~~~~~
Fixed
^^^^^
* Fixed the wrappers to different learning frameworks to use the new :class:`omni.isaac.orbit_tasks.RLTaskEnv` class.
The :class:`RLTaskEnv` class inherits from the :class:`gymnasium.Env` class (Gym 0.29.0).
* Fixed the registration of tasks in the Gym registry based on Gym 0.29.0 API.
Changed
^^^^^^^
* Removed the inheritance of all the RL-framework specific wrappers from the :class:`gymnasium.Wrapper` class.
This is because the wrappers don't comply with the new Gym 0.29.0 API. The wrappers are now only inherit
from their respective RL-framework specific base classes.
0.5.0 (2023-10-30)
~~~~~~~~~~~~~~~~~~
Changed
^^^^^^^
* Changed the way agent configs are handled for environments and learning agents. Switched from yaml to configclasses.
Fixed
^^^^^
* Fixed the way package import automation is handled in the :mod:`omni.isaac.orbit_tasks` module. Earlier it was
not skipping the blacklisted packages properly.
0.4.3 (2023-09-25)
~~~~~~~~~~~~~~~~~~
Changed
^^^^^^^
* Added future import of ``annotations`` to have a consistent behavior across Python versions.
* Removed the type-hinting from docstrings to simplify maintenance of the documentation. All type-hints are
now in the code itself.
0.4.2 (2023-08-29)
~~~~~~~~~~~~~~~~~~
Changed
^^^^^^^
* Moved the base environment definition to the :class:`omni.isaac.orbit.envs.RLEnv` class. The :class:`RLEnv`
contains RL-specific managers such as the reward, termination, randomization and curriculum managers. These
are all configured using the :class:`omni.isaac.orbit.envs.RLEnvConfig` class. The :class:`RLEnv` class
inherits from the :class:`omni.isaac.orbit.envs.BaseEnv` and ``gym.Env`` classes.
Fixed
^^^^^
* Adapted the wrappers to use the new :class:`omni.isaac.orbit.envs.RLEnv` class.
0.4.1 (2023-08-02)
~~~~~~~~~~~~~~~~~~
Changed
^^^^^^^
* Adapted the base :class:`IsaacEnv` class to use the :class:`SimulationContext` class from the
:mod:`omni.isaac.orbit.sim` module. This simplifies setting of simulation parameters.
0.4.0 (2023-07-26)
~~~~~~~~~~~~~~~~~~
Changed
^^^^^^^
* Removed the resetting of environment indices in the step call of the :class:`IsaacEnv` class.
This must be handled in the :math:`_step_impl`` function by the inherited classes.
* Adapted the wrapper for RSL-RL library its new API.
Fixed
^^^^^
* Added handling of no checkpoint available error in the :meth:`get_checkpoint_path`.
* Fixed the locomotion environment for rough terrain locomotion training.
0.3.2 (2023-07-22)
~~~~~~~~~~~~~~~~~~
Added
^^^^^^^
* Added a UI to the :class:`IsaacEnv` class to enable/disable rendering of the viewport when not running in
headless mode.
Fixed
^^^^^
* Fixed the the issue with environment returning transition tuples even when the simulation is paused.
* Fixed the shutdown of the simulation when the environment is closed.
0.3.1 (2023-06-23)
~~~~~~~~~~~~~~~~~~
Changed
^^^^^^^
* Changed the argument ``headless`` in :class:`IsaacEnv` class to ``render``, in order to cause less confusion
about rendering and headless-ness, i.e. that you can render while headless.
0.3.0 (2023-04-14)
~~~~~~~~~~~~~~~~~~
Added
^^^^^
* Added a new flag ``viewport`` to the :class:`IsaacEnv` class to enable/disable rendering of the viewport.
If the flag is set to ``True``, the viewport is enabled and the environment is rendered in the background.
* Updated the training scripts in the ``source/standalone/workflows`` directory to use the new flag ``viewport``.
If the CLI argument ``--video`` is passed, videos are recorded in the ``videos`` directory using the
:class:`gym.wrappers.RecordVideo` wrapper.
Changed
^^^^^^^
* The :class:`IsaacEnv` class supports different rendering mode as referenced in OpenAI Gym's ``render`` method.
These modes are:
* ``rgb_array``: Renders the environment in the background and returns the rendered image as a numpy array.
* ``human``: Renders the environment in the background and displays the rendered image in a window.
* Changed the constructor in the classes inheriting from :class:`IsaacEnv` to pass all the keyword arguments to the
constructor of :class:`IsaacEnv` class.
Fixed
^^^^^
* Clarified the documentation of ``headless`` flag in the :class:`IsaacEnv` class. It refers to whether or not
to render at every sim step, not whether to render the viewport or not.
* Fixed the unit tests for running random agent on included environments.
0.2.3 (2023-03-06)
~~~~~~~~~~~~~~~~~~
Fixed
^^^^^
* Tuned the observations and rewards for ``Isaac-Lift-Franka-v0`` environment.
0.2.2 (2023-03-04)
~~~~~~~~~~~~~~~~~~
Fixed
^^^^^
* Fixed the issue with rigid object not working in the ``Isaac-Lift-Franka-v0`` environment.
0.2.1 (2023-03-01)
~~~~~~~~~~~~~~~~~~
Added
^^^^^
* Added a flag ``disable_contact_processing`` to the :class:`SimCfg` class to handle
contact processing effectively when using TensorAPIs for contact reporting.
* Added verbosity flag to :meth:`export_policy_as_onnx` to print model summary.
Fixed
^^^^^
* Clarified the documentation of flags in the :class:`SimCfg` class.
* Added enabling of ``omni.kit.viewport`` and ``omni.replicator.isaac`` extensions
dynamically to maintain order in the startup of extensions.
* Corrected the experiment names in the configuration files for training environments with ``rsl_rl``.
Changed
^^^^^^^
* Changed the default value of ``enable_scene_query_support`` in :class:`SimCfg` class to False.
The flag is overridden to True inside :class:`IsaacEnv` class when running the simulation in
non-headless mode.
0.2.0 (2023-01-25)
~~~~~~~~~~~~~~~~~~
Added
^^^^^
* Added environment wrapper and sequential trainer for the skrl RL library
* Added training/evaluation configuration files for the skrl RL library
0.1.2 (2023-01-19)
~~~~~~~~~~~~~~~~~~
Fixed
^^^^^
* Added the flag ``replicate_physics`` to the :class:`SimCfg` class.
* Increased the default value of ``gpu_found_lost_pairs_capacity`` in :class:`PhysxCfg` class
0.1.1 (2023-01-18)
~~~~~~~~~~~~~~~~~~
Fixed
^^^^^
* Fixed a bug in ``Isaac-Velocity-Anymal-C-v0`` where the domain randomization is
not applicable if cloning the environments with ``replicate_physics=True``.
0.1.0 (2023-01-17)
~~~~~~~~~~~~~~~~~~
Added
^^^^^
* Initial release of the extension.
* Includes the following environments:
* ``Isaac-Cartpole-v0``: A cartpole environment with a continuous action space.
* ``Isaac-Ant-v0``: A 3D ant environment with a continuous action space.
* ``Isaac-Humanoid-v0``: A 3D humanoid environment with a continuous action space.
* ``Isaac-Reach-Franka-v0``: A end-effector pose tracking task for the Franka arm.
* ``Isaac-Lift-Franka-v0``: A 3D object lift and reposing task for the Franka arm.
* ``Isaac-Velocity-Anymal-C-v0``: An SE(2) velocity tracking task for legged robot on flat terrain.
| 9,099 | reStructuredText | 27.4375 | 118 | 0.693813 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit_tasks/docs/README.md | # Orbit: Environment Suite
Using the core framework developed as part of Orbit, we provide various learning environments for robotics research.
These environments follow the `gym.Env` API from OpenAI Gym version `0.21.0`. The environments are registered using
the Gym registry.
Each environment's name is composed of `Isaac-<Task>-<Robot>-v<X>`, where `<Task>` indicates the skill to learn
in the environment, `<Robot>` indicates the embodiment of the acting agent, and `<X>` represents the version of
the environment (which can be used to suggest different observation or action spaces).
The environments are configured using either Python classes (wrapped using `configclass` decorator) or through
YAML files. The template structure of the environment is always put at the same level as the environment file
itself. However, its various instances are included in directories within the environment directory itself.
This looks like as follows:
```tree
omni/isaac/orbit_tasks/locomotion/
├── __init__.py
└── velocity
├── config
│ └── anymal_c
│ ├── agent # <- this is where we store the learning agent configurations
│ ├── __init__.py # <- this is where we register the environment and configurations to gym registry
│ ├── flat_env_cfg.py
│ └── rough_env_cfg.py
├── __init__.py
└── velocity_env_cfg.py # <- this is the base task configuration
```
The environments are then registered in the `omni/isaac/orbit_tasks/locomotion/velocity/config/anymal_c/__init__.py`:
```python
gym.register(
id="Isaac-Velocity-Rough-Anymal-C-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={"env_cfg_entry_point": f"{__name__}.rough_env_cfg:AnymalCRoughEnvCfg"},
)
gym.register(
id="Isaac-Velocity-Flat-Anymal-C-v0",
entry_point="omni.isaac.orbit.envs:RLTaskEnv",
disable_env_checker=True,
kwargs={"env_cfg_entry_point": f"{__name__}.flat_env_cfg:AnymalCFlatEnvCfg"},
)
```
> **Note:** As a practice, we specify all the environments in a single file to avoid name conflicts between different
> tasks or environments. However, this practice is debatable and we are open to suggestions to deal with a large
> scaling in the number of tasks or environments.
| 2,275 | Markdown | 43.62745 | 117 | 0.722198 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/setup.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Installation script for the 'omni.isaac.orbit' python package."""
import os
import toml
from setuptools import setup
# Obtain the extension data from the extension.toml file
EXTENSION_PATH = os.path.dirname(os.path.realpath(__file__))
# Read the extension.toml file
EXTENSION_TOML_DATA = toml.load(os.path.join(EXTENSION_PATH, "config", "extension.toml"))
# Minimum dependencies required prior to installation
INSTALL_REQUIRES = [
# generic
"numpy",
"torch==2.0.1",
"prettytable==3.3.0",
"tensordict",
# devices
"hidapi",
# gym
"gymnasium==0.29.0",
# procedural-generation
"trimesh",
"pyglet<2",
]
# Installation operation
setup(
name="omni-isaac-orbit",
author="ORBIT Project Developers",
maintainer="Mayank Mittal",
maintainer_email="[email protected]",
url=EXTENSION_TOML_DATA["package"]["repository"],
version=EXTENSION_TOML_DATA["package"]["version"],
description=EXTENSION_TOML_DATA["package"]["description"],
keywords=EXTENSION_TOML_DATA["package"]["keywords"],
license="BSD-3-Clause",
include_package_data=True,
python_requires=">=3.10",
install_requires=INSTALL_REQUIRES,
packages=["omni.isaac.orbit"],
classifiers=[
"Natural Language :: English",
"Programming Language :: Python :: 3.10",
"Isaac Sim :: 2023.1.0-hotfix.1",
"Isaac Sim :: 2023.1.1",
],
zip_safe=False,
)
| 1,553 | Python | 26.263157 | 89 | 0.665808 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/devices/check_keyboard.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script shows how to use a teleoperation device with Isaac Sim.
The teleoperation device is a keyboard device that allows the user to control the robot.
It is possible to add additional callbacks to it for user-defined operations.
"""
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher
# launch omniverse app
app_launcher = AppLauncher()
simulation_app = app_launcher.app
"""Rest everything follows."""
import ctypes
from omni.isaac.core.simulation_context import SimulationContext
from omni.isaac.orbit.devices import Se3Keyboard
def print_cb():
"""Dummy callback function executed when the key 'L' is pressed."""
print("Print callback")
def quit_cb():
"""Dummy callback function executed when the key 'ESC' is pressed."""
print("Quit callback")
simulation_app.close()
def main():
# Load kit helper
sim = SimulationContext(physics_dt=0.01, rendering_dt=0.01)
# Create teleoperation interface
teleop_interface = Se3Keyboard(pos_sensitivity=0.1, rot_sensitivity=0.1)
# Add teleoperation callbacks
# available key buttons: https://docs.omniverse.nvidia.com/kit/docs/carbonite/latest/docs/python/carb.html?highlight=keyboardeventtype#carb.input.KeyboardInput
teleop_interface.add_callback("L", print_cb)
teleop_interface.add_callback("ESCAPE", quit_cb)
print("Press 'L' to print a message. Press 'ESC' to quit.")
# Check that boundedness of articulation is correct
if ctypes.c_long.from_address(id(teleop_interface)).value != 1:
raise RuntimeError("Teleoperation interface is not bounded to a single instance.")
# Reset interface internals
teleop_interface.reset()
# Play simulation
sim.reset()
# Simulate
while simulation_app.is_running():
# If simulation is stopped, then exit.
if sim.is_stopped():
break
# If simulation is paused, then skip.
if not sim.is_playing():
sim.step()
continue
# get keyboard command
delta_pose, gripper_command = teleop_interface.advance()
# print command
if gripper_command:
print(f"Gripper command: {gripper_command}")
# step simulation
sim.step()
# check if simulator is stopped
if sim.is_stopped():
break
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 2,589 | Python | 27.461538 | 163 | 0.679799 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sensors/check_contact_sensor.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script demonstrates how to use the contact sensor sensor in Orbit.
.. code-block:: bash
./orbit.sh -p source/extensions/omni.isaac.orbit/test/sensors/test_contact_sensor.py --num_robots 2
"""
"""Launch Isaac Sim Simulator first."""
import argparse
from omni.isaac.orbit.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(description="Contact Sensor Test Script")
parser.add_argument("--num_robots", type=int, default=64, help="Number of robots to spawn.")
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
"""Rest everything follows."""
import torch
import omni.isaac.core.utils.prims as prim_utils
from omni.isaac.cloner import GridCloner
from omni.isaac.core.simulation_context import SimulationContext
from omni.isaac.core.utils.carb import set_carb_setting
from omni.isaac.core.utils.viewports import set_camera_view
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.assets import Articulation
from omni.isaac.orbit.sensors.contact_sensor import ContactSensor, ContactSensorCfg
##
# Pre-defined configs
##
from omni.isaac.orbit_assets.anymal import ANYMAL_C_CFG # isort:skip
"""
Helpers
"""
def design_scene():
"""Add prims to the scene."""
# Ground-plane
cfg = sim_utils.GroundPlaneCfg()
cfg.func("/World/defaultGroundPlane", cfg)
# Lights
cfg = sim_utils.SphereLightCfg()
cfg.func("/World/Light/GreySphere", cfg, translation=(4.5, 3.5, 10.0))
cfg.func("/World/Light/WhiteSphere", cfg, translation=(-4.5, 3.5, 10.0))
"""
Main
"""
def main():
"""Spawns the ANYmal robot and clones it using Isaac Sim Cloner API."""
# Load kit helper
sim = SimulationContext(physics_dt=0.005, rendering_dt=0.005, backend="torch", device="cuda:0")
# Set main camera
set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0])
# Enable hydra scene-graph instancing
# this is needed to visualize the scene when flatcache is enabled
set_carb_setting(sim._settings, "/persistent/omnihydra/useSceneGraphInstancing", True)
# Create interface to clone the scene
cloner = GridCloner(spacing=2.0)
cloner.define_base_env("/World/envs")
# Everything under the namespace "/World/envs/env_0" will be cloned
prim_utils.define_prim("/World/envs/env_0")
# Clone the scene
num_envs = args_cli.num_robots
cloner.define_base_env("/World/envs")
envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_envs)
_ = cloner.clone(source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True)
# Design props
design_scene()
# Spawn things into the scene
robot_cfg = ANYMAL_C_CFG.replace(prim_path="/World/envs/env_.*/Robot")
robot_cfg.spawn.activate_contact_sensors = True
robot = Articulation(cfg=robot_cfg)
# Contact sensor
contact_sensor_cfg = ContactSensorCfg(
prim_path="/World/envs/env_.*/Robot/.*_SHANK", track_air_time=True, debug_vis=not args_cli.headless
)
contact_sensor = ContactSensor(cfg=contact_sensor_cfg)
# filter collisions within each environment instance
physics_scene_path = sim.get_physics_context().prim_path
cloner.filter_collisions(
physics_scene_path, "/World/collisions", envs_prim_paths, global_paths=["/World/defaultGroundPlane"]
)
# Play the simulator
sim.reset()
# print info
print(contact_sensor)
# Now we are ready!
print("[INFO]: Setup complete...")
# Define simulation stepping
decimation = 4
physics_dt = sim.get_physics_dt()
sim_dt = decimation * physics_dt
sim_time = 0.0
count = 0
# Simulate physics
while simulation_app.is_running():
# If simulation is stopped, then exit.
if sim.is_stopped():
break
# If simulation is paused, then skip.
if not sim.is_playing():
sim.step(render=False)
continue
# reset
if count % 1000 == 0:
# reset counters
sim_time = 0.0
count = 0
# reset dof state
joint_pos, joint_vel = robot.data.default_joint_pos, robot.data.default_joint_vel
robot.write_joint_state_to_sim(joint_pos, joint_vel)
robot.reset()
# perform 4 steps
for _ in range(decimation):
# apply actions
robot.set_joint_position_target(robot.data.default_joint_pos)
# write commands to sim
robot.write_data_to_sim()
# perform step
sim.step()
# fetch data
robot.update(physics_dt)
# update sim-time
sim_time += sim_dt
count += 1
# update the buffers
if sim.is_playing():
contact_sensor.update(sim_dt, force_recompute=True)
if count % 100 == 0:
print("Sim-time: ", sim_time)
print("Number of contacts: ", torch.count_nonzero(contact_sensor.data.current_air_time == 0.0).item())
print("-" * 80)
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 5,433 | Python | 30.229885 | 118 | 0.654519 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sensors/check_ray_caster.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script shows how to use the ray caster from the Orbit framework.
.. code-block:: bash
# Usage
./orbit.sh -p source/extensions/omni.isaac.orbit/test/sensors/test_ray_caster.py --headless
"""
"""Launch Isaac Sim Simulator first."""
import argparse
from omni.isaac.orbit.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(description="Ray Caster Test Script")
parser.add_argument("--num_envs", type=int, default=128, help="Number of environments to clone.")
parser.add_argument(
"--terrain_type",
type=str,
default="generator",
help="Type of terrain to import. Can be 'generator' or 'usd' or 'plane'.",
)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
"""Rest everything follows."""
import torch
import omni.isaac.core.utils.prims as prim_utils
from omni.isaac.cloner import GridCloner
from omni.isaac.core.prims import RigidPrimView
from omni.isaac.core.simulation_context import SimulationContext
from omni.isaac.core.utils.viewports import set_camera_view
import omni.isaac.orbit.sim as sim_utils
import omni.isaac.orbit.terrains as terrain_gen
from omni.isaac.orbit.sensors.ray_caster import RayCaster, RayCasterCfg, patterns
from omni.isaac.orbit.terrains.config.rough import ROUGH_TERRAINS_CFG
from omni.isaac.orbit.terrains.terrain_importer import TerrainImporter
from omni.isaac.orbit.utils.assets import ISAAC_NUCLEUS_DIR
from omni.isaac.orbit.utils.timer import Timer
def design_scene(sim: SimulationContext, num_envs: int = 2048):
"""Design the scene."""
# Create interface to clone the scene
cloner = GridCloner(spacing=2.0)
cloner.define_base_env("/World/envs")
# Everything under the namespace "/World/envs/env_0" will be cloned
prim_utils.define_prim("/World/envs/env_0")
# Define the scene
# -- Light
cfg = sim_utils.DistantLightCfg(intensity=2000)
cfg.func("/World/light", cfg)
# -- Balls
cfg = sim_utils.SphereCfg(
radius=0.25,
rigid_props=sim_utils.RigidBodyPropertiesCfg(),
mass_props=sim_utils.MassPropertiesCfg(mass=0.5),
collision_props=sim_utils.CollisionPropertiesCfg(),
visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)),
)
cfg.func("/World/envs/env_0/ball", cfg, translation=(0.0, 0.0, 5.0))
# Clone the scene
cloner.define_base_env("/World/envs")
envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_envs)
cloner.clone(source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True)
physics_scene_path = sim.get_physics_context().prim_path
cloner.filter_collisions(
physics_scene_path, "/World/collisions", prim_paths=envs_prim_paths, global_paths=["/World/ground"]
)
def main():
"""Main function."""
# Load kit helper
sim_params = {
"use_gpu": True,
"use_gpu_pipeline": True,
"use_flatcache": True, # deprecated from Isaac Sim 2023.1 onwards
"use_fabric": True, # used from Isaac Sim 2023.1 onwards
"enable_scene_query_support": True,
}
sim = SimulationContext(
physics_dt=1.0 / 60.0, rendering_dt=1.0 / 60.0, sim_params=sim_params, backend="torch", device="cuda:0"
)
# Set main camera
set_camera_view([0.0, 30.0, 25.0], [0.0, 0.0, -2.5])
# Parameters
num_envs = args_cli.num_envs
# Design the scene
design_scene(sim=sim, num_envs=num_envs)
# Handler for terrains importing
terrain_importer_cfg = terrain_gen.TerrainImporterCfg(
prim_path="/World/ground",
terrain_type=args_cli.terrain_type,
terrain_generator=ROUGH_TERRAINS_CFG,
usd_path=f"{ISAAC_NUCLEUS_DIR}/Environments/Terrains/rough_plane.usd",
max_init_terrain_level=None,
num_envs=1,
)
_ = TerrainImporter(terrain_importer_cfg)
# Create a ray-caster sensor
ray_caster_cfg = RayCasterCfg(
prim_path="/World/envs/env_.*/ball",
mesh_prim_paths=["/World/ground"],
pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=(1.6, 1.0)),
attach_yaw_only=True,
debug_vis=not args_cli.headless,
)
ray_caster = RayCaster(cfg=ray_caster_cfg)
# Create a view over all the balls
ball_view = RigidPrimView("/World/envs/env_.*/ball", reset_xform_properties=False)
# Play simulator
sim.reset()
# Initialize the views
# -- balls
ball_view.initialize()
# Print the sensor information
print(ray_caster)
# Get the initial positions of the balls
ball_initial_positions, ball_initial_orientations = ball_view.get_world_poses()
ball_initial_velocities = ball_view.get_velocities()
# Create a counter for resetting the scene
step_count = 0
# Simulate physics
while simulation_app.is_running():
# If simulation is stopped, then exit.
if sim.is_stopped():
break
# If simulation is paused, then skip.
if not sim.is_playing():
sim.step(render=False)
continue
# Reset the scene
if step_count % 500 == 0:
# sample random indices to reset
reset_indices = torch.randint(0, num_envs, (num_envs // 2,))
# reset the balls
ball_view.set_world_poses(
ball_initial_positions[reset_indices], ball_initial_orientations[reset_indices], indices=reset_indices
)
ball_view.set_velocities(ball_initial_velocities[reset_indices], indices=reset_indices)
# reset the sensor
ray_caster.reset(reset_indices)
# reset the counter
step_count = 0
# Step simulation
sim.step()
# Update the ray-caster
with Timer(f"Ray-caster update with {num_envs} x {ray_caster.num_rays} rays"):
ray_caster.update(dt=sim.get_physics_dt(), force_recompute=True)
# Update counter
step_count += 1
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 6,366 | Python | 33.603261 | 118 | 0.665724 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sensors/test_camera.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
# ignore private usage of variables warning
# pyright: reportPrivateUsage=none
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
app_launcher = AppLauncher(headless=True, offscreen_render=True)
simulation_app = app_launcher.app
"""Rest everything follows."""
import copy
import numpy as np
import os
import random
import scipy.spatial.transform as tf
import torch
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
import omni.replicator.core as rep
from omni.isaac.core.prims import GeometryPrim, RigidPrim
from pxr import Gf, Usd, UsdGeom
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.sensors.camera import Camera, CameraCfg
from omni.isaac.orbit.utils import convert_dict_to_backend
from omni.isaac.orbit.utils.math import convert_quat
from omni.isaac.orbit.utils.timer import Timer
# sample camera poses
POSITION = [2.5, 2.5, 2.5]
QUAT_ROS = [-0.17591989, 0.33985114, 0.82047325, -0.42470819]
QUAT_OPENGL = [0.33985113, 0.17591988, 0.42470818, 0.82047324]
QUAT_WORLD = [-0.3647052, -0.27984815, -0.1159169, 0.88047623]
class TestCamera(unittest.TestCase):
"""Test for USD Camera sensor."""
def setUp(self):
"""Create a blank new stage for each test."""
self.camera_cfg = CameraCfg(
height=128,
width=128,
prim_path="/World/Camera",
update_period=0,
data_types=["distance_to_image_plane"],
spawn=sim_utils.PinholeCameraCfg(
focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5)
),
)
# Create a new stage
stage_utils.create_new_stage()
# Simulation time-step
self.dt = 0.01
# Load kit helper
sim_cfg = sim_utils.SimulationCfg(dt=self.dt)
self.sim: sim_utils.SimulationContext = sim_utils.SimulationContext(sim_cfg)
# populate scene
self._populate_scene()
# load stage
stage_utils.update_stage()
def tearDown(self):
"""Stops simulator after each test."""
# close all the opened viewport from before.
rep.vp_manager.destroy_hydra_textures("Replicator")
# stop simulation
# note: cannot use self.sim.stop() since it does one render step after stopping!! This doesn't make sense :(
self.sim._timeline.stop()
# clear the stage
self.sim.clear_all_callbacks()
self.sim.clear_instance()
"""
Tests
"""
def test_camera_init(self):
"""Test camera initialization."""
# Create camera
camera = Camera(self.camera_cfg)
# Check simulation parameter is set correctly
self.assertTrue(self.sim.has_rtx_sensors())
# Play sim
self.sim.reset()
# Check if camera is initialized
self.assertTrue(camera._is_initialized)
# Check if camera prim is set correctly and that it is a camera prim
self.assertEqual(camera._sensor_prims[0].GetPath().pathString, self.camera_cfg.prim_path)
self.assertIsInstance(camera._sensor_prims[0], UsdGeom.Camera)
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# Check buffers that exists and have correct shapes
self.assertEqual(camera.data.pos_w.shape, (1, 3))
self.assertEqual(camera.data.quat_w_ros.shape, (1, 4))
self.assertEqual(camera.data.quat_w_world.shape, (1, 4))
self.assertEqual(camera.data.quat_w_opengl.shape, (1, 4))
self.assertEqual(camera.data.intrinsic_matrices.shape, (1, 3, 3))
self.assertEqual(camera.data.image_shape, (self.camera_cfg.height, self.camera_cfg.width))
self.assertEqual(camera.data.info, [{self.camera_cfg.data_types[0]: None}])
# Simulate physics
for _ in range(10):
# perform rendering
self.sim.step()
# update camera
camera.update(self.dt)
# check image data
for im_data in camera.data.output.to_dict().values():
self.assertEqual(im_data.shape, (1, self.camera_cfg.height, self.camera_cfg.width))
def test_camera_init_offset(self):
"""Test camera initialization with offset using different conventions."""
# define the same offset in all conventions
# -- ROS convention
cam_cfg_offset_ros = copy.deepcopy(self.camera_cfg)
cam_cfg_offset_ros.offset = CameraCfg.OffsetCfg(
pos=POSITION,
rot=QUAT_ROS,
convention="ros",
)
cam_cfg_offset_ros.prim_path = "/World/CameraOffsetRos"
camera_ros = Camera(cam_cfg_offset_ros)
# -- OpenGL convention
cam_cfg_offset_opengl = copy.deepcopy(self.camera_cfg)
cam_cfg_offset_opengl.offset = CameraCfg.OffsetCfg(
pos=POSITION,
rot=QUAT_OPENGL,
convention="opengl",
)
cam_cfg_offset_opengl.prim_path = "/World/CameraOffsetOpengl"
camera_opengl = Camera(cam_cfg_offset_opengl)
# -- World convention
cam_cfg_offset_world = copy.deepcopy(self.camera_cfg)
cam_cfg_offset_world.offset = CameraCfg.OffsetCfg(
pos=POSITION,
rot=QUAT_WORLD,
convention="world",
)
cam_cfg_offset_world.prim_path = "/World/CameraOffsetWorld"
camera_world = Camera(cam_cfg_offset_world)
# play sim
self.sim.reset()
# retrieve camera pose using USD API
prim_tf_ros = camera_ros._sensor_prims[0].ComputeLocalToWorldTransform(Usd.TimeCode.Default())
prim_tf_opengl = camera_opengl._sensor_prims[0].ComputeLocalToWorldTransform(Usd.TimeCode.Default())
prim_tf_world = camera_world._sensor_prims[0].ComputeLocalToWorldTransform(Usd.TimeCode.Default())
# convert them from column-major to row-major
prim_tf_ros = np.transpose(prim_tf_ros)
prim_tf_opengl = np.transpose(prim_tf_opengl)
prim_tf_world = np.transpose(prim_tf_world)
# check that all transforms are set correctly
np.testing.assert_allclose(prim_tf_ros[0:3, 3], cam_cfg_offset_ros.offset.pos)
np.testing.assert_allclose(prim_tf_opengl[0:3, 3], cam_cfg_offset_opengl.offset.pos)
np.testing.assert_allclose(prim_tf_world[0:3, 3], cam_cfg_offset_world.offset.pos)
np.testing.assert_allclose(
convert_quat(tf.Rotation.from_matrix(prim_tf_ros[:3, :3]).as_quat(), "wxyz"),
cam_cfg_offset_opengl.offset.rot,
rtol=1e-5,
)
np.testing.assert_allclose(
convert_quat(tf.Rotation.from_matrix(prim_tf_opengl[:3, :3]).as_quat(), "wxyz"),
cam_cfg_offset_opengl.offset.rot,
rtol=1e-5,
)
np.testing.assert_allclose(
convert_quat(tf.Rotation.from_matrix(prim_tf_world[:3, :3]).as_quat(), "wxyz"),
cam_cfg_offset_opengl.offset.rot,
rtol=1e-5,
)
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# check if transform correctly set in output
np.testing.assert_allclose(camera_ros.data.pos_w[0].cpu().numpy(), cam_cfg_offset_ros.offset.pos, rtol=1e-5)
np.testing.assert_allclose(camera_ros.data.quat_w_ros[0].cpu().numpy(), QUAT_ROS, rtol=1e-5)
np.testing.assert_allclose(camera_ros.data.quat_w_opengl[0].cpu().numpy(), QUAT_OPENGL, rtol=1e-5)
np.testing.assert_allclose(camera_ros.data.quat_w_world[0].cpu().numpy(), QUAT_WORLD, rtol=1e-5)
def test_multi_camera_init(self):
"""Test multi-camera initialization."""
# create two cameras with different prim paths
# -- camera 1
cam_cfg_1 = copy.deepcopy(self.camera_cfg)
cam_cfg_1.prim_path = "/World/Camera_1"
cam_1 = Camera(cam_cfg_1)
# -- camera 2
cam_cfg_2 = copy.deepcopy(self.camera_cfg)
cam_cfg_2.prim_path = "/World/Camera_2"
cam_2 = Camera(cam_cfg_2)
# play sim
self.sim.reset()
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# Simulate physics
for _ in range(10):
# perform rendering
self.sim.step()
# update camera
cam_1.update(self.dt)
cam_2.update(self.dt)
# check image data
for cam in [cam_1, cam_2]:
for im_data in cam.data.output.to_dict().values():
self.assertEqual(im_data.shape, (1, self.camera_cfg.height, self.camera_cfg.width))
def test_camera_set_world_poses(self):
"""Test camera function to set specific world pose."""
camera = Camera(self.camera_cfg)
# play sim
self.sim.reset()
# convert to torch tensors
position = torch.tensor([POSITION], dtype=torch.float32, device=camera.device)
orientation = torch.tensor([QUAT_WORLD], dtype=torch.float32, device=camera.device)
# set new pose
camera.set_world_poses(position.clone(), orientation.clone(), convention="world")
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# check if transform correctly set in output
torch.testing.assert_close(camera.data.pos_w, position)
torch.testing.assert_close(camera.data.quat_w_world, orientation)
def test_camera_set_world_poses_from_view(self):
"""Test camera function to set specific world pose from view."""
camera = Camera(self.camera_cfg)
# play sim
self.sim.reset()
# convert to torch tensors
eyes = torch.tensor([POSITION], dtype=torch.float32, device=camera.device)
targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera.device)
quat_ros_gt = torch.tensor([QUAT_ROS], dtype=torch.float32, device=camera.device)
# set new pose
camera.set_world_poses_from_view(eyes.clone(), targets.clone())
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# check if transform correctly set in output
torch.testing.assert_close(camera.data.pos_w, eyes)
torch.testing.assert_close(camera.data.quat_w_ros, quat_ros_gt)
def test_intrinsic_matrix(self):
"""Checks that the camera's set and retrieve methods work for intrinsic matrix."""
camera_cfg = copy.deepcopy(self.camera_cfg)
camera_cfg.height = 240
camera_cfg.width = 320
camera = Camera(camera_cfg)
# play sim
self.sim.reset()
# Desired properties (obtained from realsense camera at 320x240 resolution)
rs_intrinsic_matrix = [229.31640625, 0.0, 164.810546875, 0.0, 229.826171875, 122.1650390625, 0.0, 0.0, 1.0]
rs_intrinsic_matrix = torch.tensor(rs_intrinsic_matrix, device=camera.device).reshape(3, 3).unsqueeze(0)
# Set matrix into simulator
camera.set_intrinsic_matrices(rs_intrinsic_matrix.clone())
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# Simulate physics
for _ in range(10):
# perform rendering
self.sim.step()
# update camera
camera.update(self.dt)
# Check that matrix is correct
# TODO: This is not correctly setting all values in the matrix since the
# vertical aperture and aperture offsets are not being set correctly
# This is a bug in the simulator.
torch.testing.assert_close(rs_intrinsic_matrix[0, 0, 0], camera.data.intrinsic_matrices[0, 0, 0])
# torch.testing.assert_close(rs_intrinsic_matrix[0, 1, 1], camera.data.intrinsic_matrices[0, 1, 1])
def test_camera_resolution_all_colorize(self):
"""Test camera resolution is correctly set for all types with colorization enabled."""
# Add all types
camera_cfg = copy.deepcopy(self.camera_cfg)
camera_cfg.data_types = [
"rgb",
"distance_to_image_plane",
"normals",
"semantic_segmentation",
"instance_segmentation_fast",
"instance_id_segmentation_fast",
]
camera_cfg.colorize_instance_id_segmentation = True
camera_cfg.colorize_instance_segmentation = True
camera_cfg.colorize_semantic_segmentation = True
# Create camera
camera = Camera(camera_cfg)
# Play sim
self.sim.reset()
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
camera.update(self.dt)
# expected sizes
hw_3c_shape = (1, camera_cfg.height, camera_cfg.width, 4)
hw_1c_shape = (1, camera_cfg.height, camera_cfg.width)
# access image data and compare shapes
output = camera.data.output
self.assertEqual(output["rgb"].shape, hw_3c_shape)
self.assertEqual(output["distance_to_image_plane"].shape, hw_1c_shape)
self.assertEqual(output["normals"].shape, hw_3c_shape)
self.assertEqual(output["semantic_segmentation"].shape, hw_3c_shape)
self.assertEqual(output["instance_segmentation_fast"].shape, hw_3c_shape)
self.assertEqual(output["instance_id_segmentation_fast"].shape, hw_3c_shape)
# access image data and compare dtype
output = camera.data.output
self.assertEqual(output["rgb"].dtype, torch.uint8)
self.assertEqual(output["distance_to_image_plane"].dtype, torch.float)
self.assertEqual(output["normals"].dtype, torch.float)
self.assertEqual(output["semantic_segmentation"].dtype, torch.uint8)
self.assertEqual(output["instance_segmentation_fast"].dtype, torch.uint8)
self.assertEqual(output["instance_id_segmentation_fast"].dtype, torch.uint8)
def test_camera_resolution_no_colorize(self):
"""Test camera resolution is correctly set for all types with no colorization enabled."""
# Add all types
camera_cfg = copy.deepcopy(self.camera_cfg)
camera_cfg.data_types = [
"rgb",
"distance_to_image_plane",
"normals",
"semantic_segmentation",
"instance_segmentation_fast",
"instance_id_segmentation_fast",
]
camera_cfg.colorize_instance_id_segmentation = False
camera_cfg.colorize_instance_segmentation = False
camera_cfg.colorize_semantic_segmentation = False
# Create camera
camera = Camera(camera_cfg)
# Play sim
self.sim.reset()
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(12):
self.sim.step()
camera.update(self.dt)
# expected sizes
hw_3c_shape = (1, camera_cfg.height, camera_cfg.width, 4)
hw_1c_shape = (1, camera_cfg.height, camera_cfg.width)
# access image data and compare shapes
output = camera.data.output
self.assertEqual(output["rgb"].shape, hw_3c_shape)
self.assertEqual(output["distance_to_image_plane"].shape, hw_1c_shape)
self.assertEqual(output["normals"].shape, hw_3c_shape)
self.assertEqual(output["semantic_segmentation"].shape, hw_1c_shape)
self.assertEqual(output["instance_segmentation_fast"].shape, hw_1c_shape)
self.assertEqual(output["instance_id_segmentation_fast"].shape, hw_1c_shape)
# access image data and compare dtype
output = camera.data.output
self.assertEqual(output["rgb"].dtype, torch.uint8)
self.assertEqual(output["distance_to_image_plane"].dtype, torch.float)
self.assertEqual(output["normals"].dtype, torch.float)
self.assertEqual(output["semantic_segmentation"].dtype, torch.int32)
self.assertEqual(output["instance_segmentation_fast"].dtype, torch.int32)
self.assertEqual(output["instance_id_segmentation_fast"].dtype, torch.int32)
def test_throughput(self):
"""Checks that the single camera gets created properly with a rig."""
# Create directory temp dir to dump the results
file_dir = os.path.dirname(os.path.realpath(__file__))
temp_dir = os.path.join(file_dir, "output", "camera", "throughput")
os.makedirs(temp_dir, exist_ok=True)
# Create replicator writer
rep_writer = rep.BasicWriter(output_dir=temp_dir, frame_padding=3)
# create camera
camera_cfg = copy.deepcopy(self.camera_cfg)
camera_cfg.height = 480
camera_cfg.width = 640
camera = Camera(camera_cfg)
# Play simulator
self.sim.reset()
# Set camera pose
eyes = torch.tensor([[2.5, 2.5, 2.5]], dtype=torch.float32, device=camera.device)
targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera.device)
camera.set_world_poses_from_view(eyes, targets)
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# Simulate physics
for _ in range(5):
# perform rendering
self.sim.step()
# update camera
with Timer(f"Time taken for updating camera with shape {camera.image_shape}"):
camera.update(self.dt)
# Save images
with Timer(f"Time taken for writing data with shape {camera.image_shape} "):
# Pack data back into replicator format to save them using its writer
rep_output = dict()
camera_data = convert_dict_to_backend(camera.data.output[0].to_dict(), backend="numpy")
for key, data, info in zip(camera_data.keys(), camera_data.values(), camera.data.info[0].values()):
if info is not None:
rep_output[key] = {"data": data, "info": info}
else:
rep_output[key] = data
# Save images
rep_output["trigger_outputs"] = {"on_time": camera.frame[0]}
rep_writer.write(rep_output)
print("----------------------------------------")
# Check image data
for im_data in camera.data.output.values():
self.assertEqual(im_data.shape, (1, camera_cfg.height, camera_cfg.width))
"""
Helper functions.
"""
@staticmethod
def _populate_scene():
"""Add prims to the scene."""
# Ground-plane
cfg = sim_utils.GroundPlaneCfg()
cfg.func("/World/defaultGroundPlane", cfg)
# Lights
cfg = sim_utils.SphereLightCfg()
cfg.func("/World/Light/GreySphere", cfg, translation=(4.5, 3.5, 10.0))
cfg.func("/World/Light/WhiteSphere", cfg, translation=(-4.5, 3.5, 10.0))
# Random objects
random.seed(0)
for i in range(10):
# sample random position
position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0])
position *= np.asarray([1.5, 1.5, 0.5])
# create prim
prim_type = random.choice(["Cube", "Sphere", "Cylinder"])
prim = prim_utils.create_prim(
f"/World/Objects/Obj_{i:02d}",
prim_type,
translation=position,
scale=(0.25, 0.25, 0.25),
semantic_label=prim_type,
)
# cast to geom prim
geom_prim = getattr(UsdGeom, prim_type)(prim)
# set random color
color = Gf.Vec3f(random.random(), random.random(), random.random())
geom_prim.CreateDisplayColorAttr()
geom_prim.GetDisplayColorAttr().Set([color])
# add rigid properties
GeometryPrim(f"/World/Objects/Obj_{i:02d}", collision=True)
RigidPrim(f"/World/Objects/Obj_{i:02d}", mass=5.0)
if __name__ == "__main__":
run_tests()
| 21,527 | Python | 41.799205 | 116 | 0.616389 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sensors/test_frame_transformer.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script checks the FrameTransformer sensor by visualizing the frames that it creates.
"""
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
app_launcher = AppLauncher(headless=True)
simulation_app = app_launcher.app
"""Rest everything follows."""
import math
import scipy.spatial.transform as tf
import torch
import unittest
import omni.isaac.core.utils.stage as stage_utils
import omni.isaac.orbit.sim as sim_utils
import omni.isaac.orbit.utils.math as math_utils
from omni.isaac.orbit.scene import InteractiveScene, InteractiveSceneCfg
from omni.isaac.orbit.sensors import FrameTransformerCfg, OffsetCfg
from omni.isaac.orbit.terrains import TerrainImporterCfg
from omni.isaac.orbit.utils import configclass
##
# Pre-defined configs
##
from omni.isaac.orbit_assets.anymal import ANYMAL_C_CFG # isort:skip
def quat_from_euler_rpy(roll, pitch, yaw, degrees=False):
"""Converts Euler XYZ to Quaternion (w, x, y, z)."""
quat = tf.Rotation.from_euler("xyz", (roll, pitch, yaw), degrees=degrees).as_quat()
return tuple(quat[[3, 0, 1, 2]].tolist())
def euler_rpy_apply(rpy, xyz, degrees=False):
"""Applies rotation from Euler XYZ on position vector."""
rot = tf.Rotation.from_euler("xyz", rpy, degrees=degrees)
return tuple(rot.apply(xyz).tolist())
@configclass
class MySceneCfg(InteractiveSceneCfg):
"""Example scene configuration."""
# terrain - flat terrain plane
terrain = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane")
# articulation - robot
robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
# sensors - frame transformer (filled inside unit test)
frame_transformer: FrameTransformerCfg = None
class TestFrameTransformer(unittest.TestCase):
"""Test for frame transformer sensor."""
def setUp(self):
"""Create a blank new stage for each test."""
# Create a new stage
stage_utils.create_new_stage()
# Load kit helper
self.sim = sim_utils.SimulationContext(sim_utils.SimulationCfg(dt=0.005))
# Set main camera
self.sim.set_camera_view(eye=[5, 5, 5], target=[0.0, 0.0, 0.0])
def tearDown(self):
"""Stops simulator after each test."""
# stop simulation
# self.sim.stop()
# clear the stage
self.sim.clear_all_callbacks()
self.sim.clear_instance()
"""
Tests
"""
def test_frame_transformer_feet_wrt_base(self):
"""Test feet transformations w.r.t. base source frame.
In this test, the source frame is the robot base. This frame is at index 0, when
the frame bodies are sorted in the order of the regex matching in the frame transformer.
"""
# Spawn things into stage
scene_cfg = MySceneCfg(num_envs=32, env_spacing=5.0, lazy_sensor_update=False)
scene_cfg.frame_transformer = FrameTransformerCfg(
prim_path="{ENV_REGEX_NS}/Robot/base",
target_frames=[
FrameTransformerCfg.FrameCfg(
name="LF_FOOT_USER",
prim_path="{ENV_REGEX_NS}/Robot/LF_SHANK",
offset=OffsetCfg(
pos=euler_rpy_apply(rpy=(0, 0, -math.pi / 2), xyz=(0.08795, 0.01305, -0.33797)),
rot=quat_from_euler_rpy(0, 0, -math.pi / 2),
),
),
FrameTransformerCfg.FrameCfg(
name="RF_FOOT_USER",
prim_path="{ENV_REGEX_NS}/Robot/RF_SHANK",
offset=OffsetCfg(
pos=euler_rpy_apply(rpy=(0, 0, math.pi / 2), xyz=(0.08795, -0.01305, -0.33797)),
rot=quat_from_euler_rpy(0, 0, math.pi / 2),
),
),
FrameTransformerCfg.FrameCfg(
name="LH_FOOT_USER",
prim_path="{ENV_REGEX_NS}/Robot/LH_SHANK",
offset=OffsetCfg(
pos=euler_rpy_apply(rpy=(0, 0, -math.pi / 2), xyz=(-0.08795, 0.01305, -0.33797)),
rot=quat_from_euler_rpy(0, 0, -math.pi / 2),
),
),
FrameTransformerCfg.FrameCfg(
name="RH_FOOT_USER",
prim_path="{ENV_REGEX_NS}/Robot/RH_SHANK",
offset=OffsetCfg(
pos=euler_rpy_apply(rpy=(0, 0, math.pi / 2), xyz=(-0.08795, -0.01305, -0.33797)),
rot=quat_from_euler_rpy(0, 0, math.pi / 2),
),
),
],
)
scene = InteractiveScene(scene_cfg)
# Play the simulator
self.sim.reset()
# Acquire the index of ground truth bodies
feet_indices, feet_names = scene.articulations["robot"].find_bodies(
["LF_FOOT", "RF_FOOT", "LH_FOOT", "RH_FOOT"]
)
# Check names are parsed the same order
user_feet_names = [f"{name}_USER" for name in feet_names]
self.assertListEqual(scene.sensors["frame_transformer"].data.target_frame_names, user_feet_names)
# default joint targets
default_actions = scene.articulations["robot"].data.default_joint_pos.clone()
# Define simulation stepping
sim_dt = self.sim.get_physics_dt()
# Simulate physics
for count in range(100):
# # reset
if count % 25 == 0:
# reset root state
root_state = scene.articulations["robot"].data.default_root_state.clone()
root_state[:, :3] += scene.env_origins
joint_pos = scene.articulations["robot"].data.default_joint_pos
joint_vel = scene.articulations["robot"].data.default_joint_vel
# -- set root state
# -- robot
scene.articulations["robot"].write_root_state_to_sim(root_state)
scene.articulations["robot"].write_joint_state_to_sim(joint_pos, joint_vel)
# reset buffers
scene.reset()
# set joint targets
robot_actions = default_actions + 0.5 * torch.randn_like(default_actions)
scene.articulations["robot"].set_joint_position_target(robot_actions)
# write data to sim
scene.write_data_to_sim()
# perform step
self.sim.step()
# read data from sim
scene.update(sim_dt)
# check absolute frame transforms in world frame
# -- ground-truth
root_pose_w = scene.articulations["robot"].data.root_state_w[:, :7]
feet_pos_w_gt = scene.articulations["robot"].data.body_pos_w[:, feet_indices]
feet_quat_w_gt = scene.articulations["robot"].data.body_quat_w[:, feet_indices]
# -- frame transformer
source_pos_w_tf = scene.sensors["frame_transformer"].data.source_pos_w
source_quat_w_tf = scene.sensors["frame_transformer"].data.source_quat_w
feet_pos_w_tf = scene.sensors["frame_transformer"].data.target_pos_w
feet_quat_w_tf = scene.sensors["frame_transformer"].data.target_quat_w
# check if they are same
torch.testing.assert_close(root_pose_w[:, :3], source_pos_w_tf, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(root_pose_w[:, 3:], source_quat_w_tf, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(feet_pos_w_gt, feet_pos_w_tf, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(feet_quat_w_gt, feet_quat_w_tf, rtol=1e-3, atol=1e-3)
# check if relative transforms are same
feet_pos_source_tf = scene.sensors["frame_transformer"].data.target_pos_source
feet_quat_source_tf = scene.sensors["frame_transformer"].data.target_quat_source
for index in range(len(feet_indices)):
# ground-truth
foot_pos_b, foot_quat_b = math_utils.subtract_frame_transforms(
root_pose_w[:, :3], root_pose_w[:, 3:], feet_pos_w_tf[:, index], feet_quat_w_tf[:, index]
)
# check if they are same
torch.testing.assert_close(feet_pos_source_tf[:, index], foot_pos_b, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(feet_quat_source_tf[:, index], foot_quat_b, rtol=1e-3, atol=1e-3)
def test_frame_transformer_feet_wrt_thigh(self):
"""Test feet transformation w.r.t. thigh source frame.
In this test, the source frame is the LF leg's thigh frame. This frame is not at index 0,
when the frame bodies are sorted in the order of the regex matching in the frame transformer.
"""
# Spawn things into stage
scene_cfg = MySceneCfg(num_envs=32, env_spacing=5.0, lazy_sensor_update=False)
scene_cfg.frame_transformer = FrameTransformerCfg(
prim_path="{ENV_REGEX_NS}/Robot/LF_THIGH",
target_frames=[
FrameTransformerCfg.FrameCfg(
name="LF_FOOT_USER",
prim_path="{ENV_REGEX_NS}/Robot/LF_SHANK",
offset=OffsetCfg(
pos=euler_rpy_apply(rpy=(0, 0, -math.pi / 2), xyz=(0.08795, 0.01305, -0.33797)),
rot=quat_from_euler_rpy(0, 0, -math.pi / 2),
),
),
FrameTransformerCfg.FrameCfg(
name="RF_FOOT_USER",
prim_path="{ENV_REGEX_NS}/Robot/RF_SHANK",
offset=OffsetCfg(
pos=euler_rpy_apply(rpy=(0, 0, math.pi / 2), xyz=(0.08795, -0.01305, -0.33797)),
rot=quat_from_euler_rpy(0, 0, math.pi / 2),
),
),
],
)
scene = InteractiveScene(scene_cfg)
# Play the simulator
self.sim.reset()
# Acquire the index of ground truth bodies
source_frame_index = scene.articulations["robot"].find_bodies("LF_THIGH")[0][0]
feet_indices, feet_names = scene.articulations["robot"].find_bodies(["LF_FOOT", "RF_FOOT"])
# Check names are parsed the same order
user_feet_names = [f"{name}_USER" for name in feet_names]
self.assertListEqual(scene.sensors["frame_transformer"].data.target_frame_names, user_feet_names)
# default joint targets
default_actions = scene.articulations["robot"].data.default_joint_pos.clone()
# Define simulation stepping
sim_dt = self.sim.get_physics_dt()
# Simulate physics
for count in range(100):
# # reset
if count % 25 == 0:
# reset root state
root_state = scene.articulations["robot"].data.default_root_state.clone()
root_state[:, :3] += scene.env_origins
joint_pos = scene.articulations["robot"].data.default_joint_pos
joint_vel = scene.articulations["robot"].data.default_joint_vel
# -- set root state
# -- robot
scene.articulations["robot"].write_root_state_to_sim(root_state)
scene.articulations["robot"].write_joint_state_to_sim(joint_pos, joint_vel)
# reset buffers
scene.reset()
# set joint targets
robot_actions = default_actions + 0.5 * torch.randn_like(default_actions)
scene.articulations["robot"].set_joint_position_target(robot_actions)
# write data to sim
scene.write_data_to_sim()
# perform step
self.sim.step()
# read data from sim
scene.update(sim_dt)
# check absolute frame transforms in world frame
# -- ground-truth
source_pose_w_gt = scene.articulations["robot"].data.body_state_w[:, source_frame_index, :7]
feet_pos_w_gt = scene.articulations["robot"].data.body_pos_w[:, feet_indices]
feet_quat_w_gt = scene.articulations["robot"].data.body_quat_w[:, feet_indices]
# -- frame transformer
source_pos_w_tf = scene.sensors["frame_transformer"].data.source_pos_w
source_quat_w_tf = scene.sensors["frame_transformer"].data.source_quat_w
feet_pos_w_tf = scene.sensors["frame_transformer"].data.target_pos_w
feet_quat_w_tf = scene.sensors["frame_transformer"].data.target_quat_w
# check if they are same
torch.testing.assert_close(source_pose_w_gt[:, :3], source_pos_w_tf, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(source_pose_w_gt[:, 3:], source_quat_w_tf, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(feet_pos_w_gt, feet_pos_w_tf, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(feet_quat_w_gt, feet_quat_w_tf, rtol=1e-3, atol=1e-3)
# check if relative transforms are same
feet_pos_source_tf = scene.sensors["frame_transformer"].data.target_pos_source
feet_quat_source_tf = scene.sensors["frame_transformer"].data.target_quat_source
for index in range(len(feet_indices)):
# ground-truth
foot_pos_b, foot_quat_b = math_utils.subtract_frame_transforms(
source_pose_w_gt[:, :3], source_pose_w_gt[:, 3:], feet_pos_w_tf[:, index], feet_quat_w_tf[:, index]
)
# check if they are same
torch.testing.assert_close(feet_pos_source_tf[:, index], foot_pos_b, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(feet_quat_source_tf[:, index], foot_quat_b, rtol=1e-3, atol=1e-3)
if __name__ == "__main__":
run_tests()
| 13,928 | Python | 44.224026 | 119 | 0.577972 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sensors/test_contact_sensor.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Tests to verify contact sensor functionality on rigid object prims."""
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
HEADLESS = True
# launch omniverse app
app_launcher = AppLauncher(headless=HEADLESS)
simulation_app = app_launcher.app
"""Rest everything follows."""
import torch
import unittest
from dataclasses import MISSING
from enum import Enum
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.assets import RigidObject, RigidObjectCfg
from omni.isaac.orbit.scene import InteractiveScene, InteractiveSceneCfg
from omni.isaac.orbit.sensors import ContactSensor, ContactSensorCfg
from omni.isaac.orbit.sim import build_simulation_context
from omni.isaac.orbit.terrains import HfRandomUniformTerrainCfg, TerrainGeneratorCfg, TerrainImporterCfg
from omni.isaac.orbit.utils import configclass
##
# Custom helper classes.
##
class ContactTestMode(Enum):
"""Enum to declare the type of contact sensor test to execute."""
IN_CONTACT = 0
"""Enum to test the condition where the test object is in contact with the ground plane."""
NON_CONTACT = 1
"""Enum to test the condition where the test object is not in contact with the ground plane (air time)."""
@configclass
class TestContactSensorRigidObjectCfg(RigidObjectCfg):
"""Configuration for rigid objects used for the contact sensor test.
This contains the expected values in the configuration to simplify test fixtures.
"""
contact_pose: torch.Tensor = MISSING
"""6D pose of the rigid object under test when it is in contact with the ground surface."""
non_contact_pose: torch.Tensor = MISSING
"""6D pose of the rigid object under test when it is not in contact."""
@configclass
class ContactSensorSceneCfg(InteractiveSceneCfg):
"""Configuration of the scene used by the contact sensor test."""
terrain: TerrainImporterCfg = MISSING
"""Terrain configuration within the scene."""
shape: TestContactSensorRigidObjectCfg = MISSING
"""RigidObject contact prim configuration."""
contact_sensor: ContactSensorCfg = MISSING
"""Contact sensor configuration."""
shape_2: TestContactSensorRigidObjectCfg = None
"""RigidObject contact prim configuration. Defaults to None, i.e. not included in the scene.
This is a second prim used for testing contact filtering.
"""
contact_sensor_2: ContactSensorCfg = None
"""Contact sensor configuration. Defaults to None, i.e. not included in the scene.
This is a second contact sensor used for testing contact filtering.
"""
##
# Scene entity configurations.
##
CUBE_CFG = TestContactSensorRigidObjectCfg(
prim_path="/World/Objects/Cube",
spawn=sim_utils.CuboidCfg(
size=(0.5, 0.5, 0.5),
rigid_props=sim_utils.RigidBodyPropertiesCfg(
disable_gravity=False,
),
collision_props=sim_utils.CollisionPropertiesCfg(
collision_enabled=True,
),
activate_contact_sensors=True,
visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.4, 0.6, 0.4)),
),
init_state=RigidObjectCfg.InitialStateCfg(pos=(0, -1.0, 1.0)),
contact_pose=torch.tensor([0, -1.0, 0, 1, 0, 0, 0]),
non_contact_pose=torch.tensor([0, -1.0, 1.0, 1, 0, 0, 0]),
)
"""Configuration of the cube prim."""
SPHERE_CFG = TestContactSensorRigidObjectCfg(
prim_path="/World/Objects/Sphere",
spawn=sim_utils.SphereCfg(
radius=0.25,
rigid_props=sim_utils.RigidBodyPropertiesCfg(
disable_gravity=False,
),
collision_props=sim_utils.CollisionPropertiesCfg(
collision_enabled=True,
),
activate_contact_sensors=True,
visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.4, 0.4, 0.6)),
),
init_state=RigidObjectCfg.InitialStateCfg(pos=(0, 1.0, 1.0)),
contact_pose=torch.tensor([0, 1.0, 0.0, 1, 0, 0, 0]),
non_contact_pose=torch.tensor([0, 1.0, 1.0, 1, 0, 0, 0]),
)
"""Configuration of the sphere prim."""
CYLINDER_CFG = TestContactSensorRigidObjectCfg(
prim_path="/World/Objects/Cylinder",
spawn=sim_utils.CylinderCfg(
radius=0.5,
height=0.01,
axis="Y",
rigid_props=sim_utils.RigidBodyPropertiesCfg(
disable_gravity=False,
),
collision_props=sim_utils.CollisionPropertiesCfg(
collision_enabled=True,
),
activate_contact_sensors=True,
visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.6, 0.4, 0.4)),
),
init_state=RigidObjectCfg.InitialStateCfg(pos=(0, 0.0, 1.0)),
contact_pose=torch.tensor([0, 0, 0.0, 1, 0, 0, 0]),
non_contact_pose=torch.tensor([0, 0, 1.0, 1, 0, 0, 0]),
)
"""Configuration of the cylinder prim."""
CAPSULE_CFG = TestContactSensorRigidObjectCfg(
prim_path="/World/Objects/Capsule",
spawn=sim_utils.CapsuleCfg(
radius=0.25,
height=0.5,
axis="Z",
rigid_props=sim_utils.RigidBodyPropertiesCfg(
disable_gravity=False,
),
collision_props=sim_utils.CollisionPropertiesCfg(
collision_enabled=True,
),
activate_contact_sensors=True,
visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.2, 0.4, 0.4)),
),
init_state=RigidObjectCfg.InitialStateCfg(pos=(1.0, 0.0, 1.5)),
contact_pose=torch.tensor([1.0, 0.0, 0.0, 1, 0, 0, 0]),
non_contact_pose=torch.tensor([1.0, 0.0, 1.5, 1, 0, 0, 0]),
)
"""Configuration of the capsule prim."""
CONE_CFG = TestContactSensorRigidObjectCfg(
prim_path="/World/Objects/Cone",
spawn=sim_utils.ConeCfg(
radius=0.5,
height=0.5,
axis="Z",
rigid_props=sim_utils.RigidBodyPropertiesCfg(
disable_gravity=False,
),
collision_props=sim_utils.CollisionPropertiesCfg(
collision_enabled=True,
),
activate_contact_sensors=True,
visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.4, 0.2, 0.4)),
),
init_state=RigidObjectCfg.InitialStateCfg(pos=(-1.0, 0.0, 1.0)),
contact_pose=torch.tensor([-1.0, 0.0, 0.0, 1, 0, 0, 0]),
non_contact_pose=torch.tensor([-1.0, 0.0, 1.0, 1, 0, 0, 0]),
)
"""Configuration of the cone prim."""
FLAT_TERRAIN_CFG = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane")
"""Configuration of the flat ground plane."""
COBBLESTONE_TERRAIN_CFG = TerrainImporterCfg(
prim_path="/World/ground",
terrain_type="generator",
terrain_generator=TerrainGeneratorCfg(
seed=0,
size=(3.0, 3.0),
border_width=0.0,
num_rows=1,
num_cols=1,
sub_terrains={
"random_rough": HfRandomUniformTerrainCfg(
proportion=1.0, noise_range=(0.0, 0.05), noise_step=0.01, border_width=0.25
),
},
),
)
"""Configuration of the generated mesh terrain."""
class TestContactSensor(unittest.TestCase):
"""Unittest class for testing the contact sensor.
This class includes test cases for the available rigid object primitives, and tests that the
the contact sensor is reporting correct results for various contact durations, terrain types, and
evaluation devices.
"""
@classmethod
def setUpClass(cls):
"""Contact sensor test suite init."""
cls.sim_dt = 0.0025
cls.durations = [cls.sim_dt, cls.sim_dt * 2, cls.sim_dt * 32, cls.sim_dt * 128]
cls.terrains = [FLAT_TERRAIN_CFG, COBBLESTONE_TERRAIN_CFG]
cls.devices = ["cuda:0", "cpu"]
def test_cube_contact_time(self):
"""Checks contact sensor values for contact time and air time for a cube collision primitive."""
self._run_contact_sensor_test(shape_cfg=CUBE_CFG)
def test_sphere_contact_time(self):
"""Checks contact sensor values for contact time and air time for a sphere collision primitive."""
self._run_contact_sensor_test(shape_cfg=SPHERE_CFG)
def test_cube_stack_contact_filtering(self):
"""Checks contact sensor reporting for filtering stacked cube prims."""
for device in self.devices:
for num_envs in [1, 6, 24]:
with self.subTest(device=device, num_envs=num_envs):
with build_simulation_context(device=device, dt=self.sim_dt, add_lighting=True) as sim:
# Instance new scene for the current terrain and contact prim.
scene_cfg = ContactSensorSceneCfg(num_envs=num_envs, env_spacing=1.0, lazy_sensor_update=False)
scene_cfg.terrain = FLAT_TERRAIN_CFG.replace(prim_path="/World/ground")
# -- cube 1
scene_cfg.shape = CUBE_CFG.replace(prim_path="{ENV_REGEX_NS}/Cube_1")
scene_cfg.shape.init_state.pos = (0, -1.0, 1.0)
# -- cube 2 (on top of cube 1)
scene_cfg.shape_2 = CUBE_CFG.replace(prim_path="{ENV_REGEX_NS}/Cube_2")
scene_cfg.shape_2.init_state.pos = (0, -1.0, 1.525)
# -- contact sensor 1
scene_cfg.contact_sensor = ContactSensorCfg(
prim_path="{ENV_REGEX_NS}/Cube_1",
track_pose=True,
debug_vis=False,
update_period=0.0,
filter_prim_paths_expr=["{ENV_REGEX_NS}/Cube_2"],
)
# -- contact sensor 2
scene_cfg.contact_sensor_2 = ContactSensorCfg(
prim_path="{ENV_REGEX_NS}/Cube_2",
track_pose=True,
debug_vis=False,
update_period=0.0,
filter_prim_paths_expr=["{ENV_REGEX_NS}/Cube_1"],
)
scene = InteractiveScene(scene_cfg)
# Set variables internally for reference
self.sim = sim
self.scene = scene
# Play the simulation
self.sim.reset()
# Extract from scene for type hinting
contact_sensor: ContactSensor = self.scene["contact_sensor"]
contact_sensor_2: ContactSensor = self.scene["contact_sensor_2"]
# Check buffers have the right size
self.assertEqual(contact_sensor.contact_physx_view.filter_count, 1)
self.assertEqual(contact_sensor_2.contact_physx_view.filter_count, 1)
# Reset the contact sensors
self.scene.reset()
# Let the scene come to a rest
for _ in range(20):
self._perform_sim_step()
# Check values for cube 2
torch.testing.assert_close(
contact_sensor_2.data.force_matrix_w[:, :, 0], contact_sensor_2.data.net_forces_w
)
torch.testing.assert_close(
contact_sensor_2.data.force_matrix_w[:, :, 0], contact_sensor.data.force_matrix_w[:, :, 0]
)
"""
Internal helpers.
"""
def _run_contact_sensor_test(self, shape_cfg: TestContactSensorRigidObjectCfg):
"""Runs a rigid body test for a given contact primitive configuration.
This method iterates through each device and terrain combination in the simulation environment,
running tests for contact sensors.
Args:
shape_cfg: The configuration parameters for the shape to be tested.
"""
for device in self.devices:
for terrain in self.terrains:
with self.subTest(device=device, terrain=terrain):
with build_simulation_context(device=device, dt=self.sim_dt, add_lighting=True) as sim:
# Instance new scene for the current terrain and contact prim.
scene_cfg = ContactSensorSceneCfg(num_envs=1, env_spacing=1.0, lazy_sensor_update=False)
scene_cfg.terrain = terrain
scene_cfg.shape = shape_cfg
scene_cfg.contact_sensor = ContactSensorCfg(
prim_path=shape_cfg.prim_path,
track_pose=True,
debug_vis=False,
update_period=0.0,
track_air_time=True,
history_length=3,
)
scene = InteractiveScene(scene_cfg)
# Set variables internally for reference
self.sim = sim
self.scene = scene
# Play the simulation
self.sim.reset()
# Run contact time and air time tests.
self._test_sensor_contact(
shape=self.scene["shape"],
sensor=self.scene["contact_sensor"],
mode=ContactTestMode.IN_CONTACT,
)
self._test_sensor_contact(
shape=self.scene["shape"],
sensor=self.scene["contact_sensor"],
mode=ContactTestMode.NON_CONTACT,
)
def _test_sensor_contact(self, shape: RigidObject, sensor: ContactSensor, mode: ContactTestMode):
"""Test for the contact sensor.
This test sets the contact prim to a pose either in contact or out of contact with the ground plane for
a known duration. Once the contact duration has elapsed, the data stored inside the contact sensor
associated with the contact prim is checked against the expected values.
This process is repeated for all elements in :attr:`TestContactSensor.durations`, where each successive
contact timing test is punctuated by setting the contact prim to the complement of the desired contact mode
for 1 sim time-step.
Args:
shape: The contact prim used for the contact sensor test.
sensor: The sensor reporting data to be verified by the contact sensor test.
mode: The contact test mode: either contact with ground plane or air time.
"""
# reset the test state
sensor.reset()
expected_last_test_contact_time = 0
expected_last_reset_contact_time = 0
# set poses for shape for a given contact sensor test mode.
# desired contact mode to set for a given duration.
test_pose = None
# complement of the desired contact mode used to reset the contact sensor.
reset_pose = None
if mode == ContactTestMode.IN_CONTACT:
test_pose = shape.cfg.contact_pose
reset_pose = shape.cfg.non_contact_pose
elif mode == ContactTestMode.NON_CONTACT:
test_pose = shape.cfg.non_contact_pose
reset_pose = shape.cfg.contact_pose
else:
raise ValueError("Received incompatible contact sensor test mode")
for idx in range(len(self.durations)):
current_test_time = 0
duration = self.durations[idx]
while current_test_time < duration:
# set object states to contact the ground plane
shape.write_root_pose_to_sim(root_pose=test_pose)
# perform simulation step
self._perform_sim_step()
# increment contact time
current_test_time += self.sim_dt
# set last contact time to the previous desired contact duration plus the extra dt allowance.
expected_last_test_contact_time = self.durations[idx - 1] + self.sim_dt if idx > 0 else 0
# Check the data inside the contact sensor
if mode == ContactTestMode.IN_CONTACT:
self._check_prim_contact_state_times(
sensor=sensor,
expected_air_time=0.0,
expected_contact_time=self.durations[idx],
expected_last_contact_time=expected_last_test_contact_time,
expected_last_air_time=expected_last_reset_contact_time,
dt=duration + self.sim_dt,
)
elif mode == ContactTestMode.NON_CONTACT:
self._check_prim_contact_state_times(
sensor=sensor,
expected_air_time=self.durations[idx],
expected_contact_time=0.0,
expected_last_contact_time=expected_last_reset_contact_time,
expected_last_air_time=expected_last_test_contact_time,
dt=duration + self.sim_dt,
)
# switch the contact mode for 1 dt step before the next contact test begins.
shape.write_root_pose_to_sim(root_pose=reset_pose)
# perform simulation step
self._perform_sim_step()
# set the last air time to 2 sim_dt steps, because last_air_time and last_contact_time
# adds an additional sim_dt to the total time spent in the previous contact mode for uncertainty in
# when the contact switch happened in between a dt step.
expected_last_reset_contact_time = 2 * self.sim_dt
def _check_prim_contact_state_times(
self,
sensor: ContactSensor,
expected_air_time: float,
expected_contact_time: float,
expected_last_air_time: float,
expected_last_contact_time: float,
dt: float,
) -> None:
"""Checks contact sensor data matches expected values.
Args:
sensor: Instance of ContactSensor containing data to be tested.
expected_air_time: Air time ground truth.
expected_contact_time: Contact time ground truth.
expected_last_air_time: Last air time ground truth.
expected_last_contact_time: Last contact time ground truth.
dt: Time since previous contact mode switch. If the contact prim left contact 0.1 seconds ago,
dt should be 0.1 + simulation dt seconds.
"""
# store current state of the contact prim
in_air = False
in_contact = False
if expected_air_time > 0.0:
in_air = True
if expected_contact_time > 0.0:
in_contact = True
measured_contact_time = sensor.data.current_contact_time
measured_air_time = sensor.data.current_air_time
measured_last_contact_time = sensor.data.last_contact_time
measured_last_air_time = sensor.data.last_air_time
# check current contact state
self.assertAlmostEqual(measured_contact_time.item(), expected_contact_time, places=2)
self.assertAlmostEqual(measured_air_time.item(), expected_air_time, places=2)
# check last contact state
self.assertAlmostEqual(measured_last_contact_time.item(), expected_last_contact_time, places=2)
self.assertAlmostEqual(measured_last_air_time.item(), expected_last_air_time, places=2)
# check current contact mode
self.assertEqual(sensor.compute_first_contact(dt=dt).item(), in_contact)
self.assertEqual(sensor.compute_first_air(dt=dt).item(), in_air)
def _perform_sim_step(self) -> None:
"""Updates sensors and steps the contact sensor test scene."""
# write data to simulation
self.scene.write_data_to_sim()
# simulate
self.sim.step(render=not HEADLESS)
# update buffers at sim dt
self.scene.update(dt=self.sim_dt)
if __name__ == "__main__":
run_tests()
| 20,168 | Python | 41.283019 | 119 | 0.597779 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sensors/test_ray_caster_camera.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
# ignore private usage of variables warning
# pyright: reportPrivateUsage=none
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
app_launcher = AppLauncher(headless=True, offscreen_render=True)
simulation_app = app_launcher.app
"""Rest everything follows."""
import copy
import numpy as np
import os
import torch
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
import omni.replicator.core as rep
from pxr import Gf
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.sensors.camera import Camera, CameraCfg
from omni.isaac.orbit.sensors.ray_caster import RayCasterCamera, RayCasterCameraCfg, patterns
from omni.isaac.orbit.sim import PinholeCameraCfg
from omni.isaac.orbit.terrains.trimesh.utils import make_plane
from omni.isaac.orbit.terrains.utils import create_prim_from_mesh
from omni.isaac.orbit.utils import convert_dict_to_backend
from omni.isaac.orbit.utils.timer import Timer
# sample camera poses
POSITION = [2.5, 2.5, 2.5]
QUAT_ROS = [-0.17591989, 0.33985114, 0.82047325, -0.42470819]
QUAT_OPENGL = [0.33985113, 0.17591988, 0.42470818, 0.82047324]
QUAT_WORLD = [-0.3647052, -0.27984815, -0.1159169, 0.88047623]
class TestWarpCamera(unittest.TestCase):
"""Test for orbit camera sensor"""
"""
Test Setup and Teardown
"""
def setUp(self):
"""Create a blank new stage for each test."""
camera_pattern_cfg = patterns.PinholeCameraPatternCfg(
focal_length=24.0,
horizontal_aperture=20.955,
height=480,
width=640,
)
self.camera_cfg = RayCasterCameraCfg(
prim_path="/World/Camera",
mesh_prim_paths=["/World/defaultGroundPlane"],
update_period=0,
offset=RayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0), convention="world"),
debug_vis=False,
pattern_cfg=camera_pattern_cfg,
data_types=[
"distance_to_image_plane",
],
)
# Create a new stage
stage_utils.create_new_stage()
# create xform because placement of camera directly under world is not supported
prim_utils.create_prim("/World/Camera", "Xform")
# Simulation time-step
self.dt = 0.01
# Load kit helper
sim_cfg = sim_utils.SimulationCfg(dt=self.dt)
self.sim: sim_utils.SimulationContext = sim_utils.SimulationContext(sim_cfg)
# Ground-plane
mesh = make_plane(size=(2e1, 2e1), height=0.0, center_zero=True)
create_prim_from_mesh("/World/defaultGroundPlane", mesh)
# load stage
stage_utils.update_stage()
def tearDown(self):
"""Stops simulator after each test."""
# close all the opened viewport from before.
rep.vp_manager.destroy_hydra_textures("Replicator")
# stop simulation
# note: cannot use self.sim.stop() since it does one render step after stopping!! This doesn't make sense :(
self.sim._timeline.stop()
# clear the stage
self.sim.clear_all_callbacks()
self.sim.clear_instance()
"""
Tests
"""
def test_camera_init(self):
"""Test camera initialization."""
# Create camera
camera = RayCasterCamera(cfg=self.camera_cfg)
# Play sim
self.sim.reset()
# Check if camera is initialized
self.assertTrue(camera._is_initialized)
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# Check buffers that exists and have correct shapes
self.assertEqual(camera.data.pos_w.shape, (1, 3))
self.assertEqual(camera.data.quat_w_ros.shape, (1, 4))
self.assertEqual(camera.data.quat_w_world.shape, (1, 4))
self.assertEqual(camera.data.quat_w_opengl.shape, (1, 4))
self.assertEqual(camera.data.intrinsic_matrices.shape, (1, 3, 3))
self.assertEqual(
camera.data.image_shape, (self.camera_cfg.pattern_cfg.height, self.camera_cfg.pattern_cfg.width)
)
self.assertEqual(camera.data.info, [{self.camera_cfg.data_types[0]: None}])
# Simulate physics
for _ in range(10):
# perform rendering
self.sim.step()
# update camera
camera.update(self.dt)
# check image data
for im_data in camera.data.output.to_dict().values():
self.assertEqual(
im_data.shape, (1, self.camera_cfg.pattern_cfg.height, self.camera_cfg.pattern_cfg.width)
)
def test_camera_resolution(self):
"""Test camera resolution is correctly set."""
# Create camera
camera = RayCasterCamera(cfg=self.camera_cfg)
# Play sim
self.sim.reset()
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
camera.update(self.dt)
# access image data and compare shapes
for im_data in camera.data.output.to_dict().values():
self.assertTrue(im_data.shape == (1, self.camera_cfg.pattern_cfg.height, self.camera_cfg.pattern_cfg.width))
def test_camera_init_offset(self):
"""Test camera initialization with offset using different conventions."""
# define the same offset in all conventions
# -- ROS convention
cam_cfg_offset_ros = copy.deepcopy(self.camera_cfg)
cam_cfg_offset_ros.offset = RayCasterCameraCfg.OffsetCfg(
pos=POSITION,
rot=QUAT_ROS,
convention="ros",
)
prim_utils.create_prim("/World/CameraOffsetRos", "Xform")
cam_cfg_offset_ros.prim_path = "/World/CameraOffsetRos"
camera_ros = RayCasterCamera(cam_cfg_offset_ros)
# -- OpenGL convention
cam_cfg_offset_opengl = copy.deepcopy(self.camera_cfg)
cam_cfg_offset_opengl.offset = RayCasterCameraCfg.OffsetCfg(
pos=POSITION,
rot=QUAT_OPENGL,
convention="opengl",
)
prim_utils.create_prim("/World/CameraOffsetOpengl", "Xform")
cam_cfg_offset_opengl.prim_path = "/World/CameraOffsetOpengl"
camera_opengl = RayCasterCamera(cam_cfg_offset_opengl)
# -- World convention
cam_cfg_offset_world = copy.deepcopy(self.camera_cfg)
cam_cfg_offset_world.offset = RayCasterCameraCfg.OffsetCfg(
pos=POSITION,
rot=QUAT_WORLD,
convention="world",
)
prim_utils.create_prim("/World/CameraOffsetWorld", "Xform")
cam_cfg_offset_world.prim_path = "/World/CameraOffsetWorld"
camera_world = RayCasterCamera(cam_cfg_offset_world)
# play sim
self.sim.reset()
# update cameras
camera_world.update(self.dt)
camera_opengl.update(self.dt)
camera_ros.update(self.dt)
# check that all transforms are set correctly
np.testing.assert_allclose(camera_ros.data.pos_w[0].cpu().numpy(), cam_cfg_offset_ros.offset.pos)
np.testing.assert_allclose(camera_opengl.data.pos_w[0].cpu().numpy(), cam_cfg_offset_opengl.offset.pos)
np.testing.assert_allclose(camera_world.data.pos_w[0].cpu().numpy(), cam_cfg_offset_world.offset.pos)
# check if transform correctly set in output
np.testing.assert_allclose(camera_ros.data.pos_w[0].cpu().numpy(), cam_cfg_offset_ros.offset.pos, rtol=1e-5)
np.testing.assert_allclose(camera_ros.data.quat_w_ros[0].cpu().numpy(), QUAT_ROS, rtol=1e-5)
np.testing.assert_allclose(camera_ros.data.quat_w_opengl[0].cpu().numpy(), QUAT_OPENGL, rtol=1e-5)
np.testing.assert_allclose(camera_ros.data.quat_w_world[0].cpu().numpy(), QUAT_WORLD, rtol=1e-5)
def test_multi_camera_init(self):
"""Test multi-camera initialization."""
# create two cameras with different prim paths
# -- camera 1
cam_cfg_1 = copy.deepcopy(self.camera_cfg)
cam_cfg_1.prim_path = "/World/Camera_1"
prim_utils.create_prim("/World/Camera_1", "Xform")
# Create camera
cam_1 = RayCasterCamera(cam_cfg_1)
# -- camera 2
cam_cfg_2 = copy.deepcopy(self.camera_cfg)
cam_cfg_2.prim_path = "/World/Camera_2"
prim_utils.create_prim("/World/Camera_2", "Xform")
cam_2 = RayCasterCamera(cam_cfg_2)
# check that the loaded meshes are equal
self.assertTrue(cam_1.meshes == cam_2.meshes)
# play sim
self.sim.reset()
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# Simulate physics
for _ in range(10):
# perform rendering
self.sim.step()
# update camera
cam_1.update(self.dt)
cam_2.update(self.dt)
# check image data
for cam in [cam_1, cam_2]:
for im_data in cam.data.output.to_dict().values():
self.assertEqual(
im_data.shape, (1, self.camera_cfg.pattern_cfg.height, self.camera_cfg.pattern_cfg.width)
)
def test_camera_set_world_poses(self):
"""Test camera function to set specific world pose."""
camera = RayCasterCamera(self.camera_cfg)
# play sim
self.sim.reset()
# convert to torch tensors
position = torch.tensor([POSITION], dtype=torch.float32, device=camera.device)
orientation = torch.tensor([QUAT_WORLD], dtype=torch.float32, device=camera.device)
# set new pose
camera.set_world_poses(position.clone(), orientation.clone(), convention="world")
# check if transform correctly set in output
torch.testing.assert_close(camera.data.pos_w, position)
torch.testing.assert_close(camera.data.quat_w_world, orientation)
def test_camera_set_world_poses_from_view(self):
"""Test camera function to set specific world pose from view."""
camera = RayCasterCamera(self.camera_cfg)
# play sim
self.sim.reset()
# convert to torch tensors
eyes = torch.tensor([POSITION], dtype=torch.float32, device=camera.device)
targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera.device)
quat_ros_gt = torch.tensor([QUAT_ROS], dtype=torch.float32, device=camera.device)
# set new pose
camera.set_world_poses_from_view(eyes.clone(), targets.clone())
# check if transform correctly set in output
torch.testing.assert_close(camera.data.pos_w, eyes)
torch.testing.assert_close(camera.data.quat_w_ros, quat_ros_gt)
def test_intrinsic_matrix(self):
"""Checks that the camera's set and retrieve methods work for intrinsic matrix."""
camera_cfg = copy.deepcopy(self.camera_cfg)
camera_cfg.pattern_cfg.height = 240
camera_cfg.pattern_cfg.width = 320
camera = RayCasterCamera(camera_cfg)
# play sim
self.sim.reset()
# Desired properties (obtained from realsense camera at 320x240 resolution)
rs_intrinsic_matrix = [229.31640625, 0.0, 164.810546875, 0.0, 229.826171875, 122.1650390625, 0.0, 0.0, 1.0]
rs_intrinsic_matrix = torch.tensor(rs_intrinsic_matrix, device=camera.device).reshape(3, 3).unsqueeze(0)
# Set matrix into simulator
camera.set_intrinsic_matrices(rs_intrinsic_matrix.clone())
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# Simulate physics
for _ in range(10):
# perform rendering
self.sim.step()
# update camera
camera.update(self.dt)
# Check that matrix is correct
# TODO: This is not correctly setting all values in the matrix since the
# vertical aperture and aperture offsets are not being set correctly
# This is a bug in the simulator.
torch.testing.assert_close(rs_intrinsic_matrix[0, 0, 0], camera.data.intrinsic_matrices[0, 0, 0])
# torch.testing.assert_close(rs_intrinsic_matrix[0, 1, 1], camera.data.intrinsic_matrices[0, 1, 1])
def test_throughput(self):
"""Checks that the single camera gets created properly with a rig."""
# Create directory temp dir to dump the results
file_dir = os.path.dirname(os.path.realpath(__file__))
temp_dir = os.path.join(file_dir, "output", "camera", "throughput")
os.makedirs(temp_dir, exist_ok=True)
# Create replicator writer
rep_writer = rep.BasicWriter(output_dir=temp_dir, frame_padding=3)
# create camera
camera_cfg = copy.deepcopy(self.camera_cfg)
camera_cfg.pattern_cfg.height = 480
camera_cfg.pattern_cfg.width = 640
camera = RayCasterCamera(camera_cfg)
# Play simulator
self.sim.reset()
# Set camera pose
eyes = torch.tensor([[2.5, 2.5, 2.5]], dtype=torch.float32, device=camera.device)
targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera.device)
camera.set_world_poses_from_view(eyes, targets)
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
self.sim.step()
# Simulate physics
for _ in range(5):
# perform rendering
self.sim.step()
# update camera
with Timer(f"Time taken for updating camera with shape {camera.image_shape}"):
camera.update(self.dt)
# Save images
with Timer(f"Time taken for writing data with shape {camera.image_shape} "):
# Pack data back into replicator format to save them using its writer
rep_output = dict()
camera_data = convert_dict_to_backend(camera.data.output[0].to_dict(), backend="numpy")
for key, data, info in zip(camera_data.keys(), camera_data.values(), camera.data.info[0].values()):
if info is not None:
rep_output[key] = {"data": data, "info": info}
else:
rep_output[key] = data
# Save images
rep_output["trigger_outputs"] = {"on_time": camera.frame[0]}
rep_writer.write(rep_output)
print("----------------------------------------")
# Check image data
for im_data in camera.data.output.values():
self.assertEqual(im_data.shape, (1, camera_cfg.pattern_cfg.height, camera_cfg.pattern_cfg.width))
def test_output_equal_to_usdcamera(self):
camera_pattern_cfg = patterns.PinholeCameraPatternCfg(
focal_length=24.0,
horizontal_aperture=20.955,
height=240,
width=320,
)
prim_utils.create_prim("/World/Camera_warp", "Xform")
camera_cfg_warp = RayCasterCameraCfg(
prim_path="/World/Camera",
mesh_prim_paths=["/World/defaultGroundPlane"],
update_period=0,
offset=RayCasterCameraCfg.OffsetCfg(pos=(0.0, 0.0, 0.0), rot=(1.0, 0.0, 0.0, 0.0)),
debug_vis=False,
pattern_cfg=camera_pattern_cfg,
data_types=["distance_to_image_plane", "distance_to_camera", "normals"],
)
camera_warp = RayCasterCamera(camera_cfg_warp)
# create usd camera
camera_cfg_usd = CameraCfg(
height=240,
width=320,
prim_path="/World/Camera_usd",
update_period=0,
data_types=["distance_to_image_plane", "distance_to_camera", "normals"],
spawn=PinholeCameraCfg(
focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1e-4, 1.0e5)
),
)
camera_usd = Camera(camera_cfg_usd)
# play sim
self.sim.reset()
self.sim.play()
# convert to torch tensors
eyes = torch.tensor([[2.5, 2.5, 4.5]], dtype=torch.float32, device=camera_warp.device)
targets = torch.tensor([[0.0, 0.0, 0.0]], dtype=torch.float32, device=camera_warp.device)
# set views
camera_warp.set_world_poses_from_view(eyes, targets)
camera_usd.set_world_poses_from_view(eyes, targets)
# perform steps
for _ in range(5):
self.sim.step()
# update camera
camera_usd.update(self.dt)
camera_warp.update(self.dt)
# check image data
torch.testing.assert_close(
camera_usd.data.output["distance_to_image_plane"],
camera_warp.data.output["distance_to_image_plane"],
rtol=5e-3,
atol=1e-4,
)
torch.testing.assert_close(
camera_usd.data.output["distance_to_camera"],
camera_warp.data.output["distance_to_camera"],
rtol=5e-3,
atol=1e-4,
)
torch.testing.assert_close(
camera_usd.data.output["normals"][..., :3],
camera_warp.data.output["normals"],
rtol=1e-5,
atol=1e-4,
)
def test_output_equal_to_usdcamera_offset(self):
offset_rot = [-0.1251, 0.3617, 0.8731, -0.3020]
camera_pattern_cfg = patterns.PinholeCameraPatternCfg(
focal_length=24.0,
horizontal_aperture=20.955,
height=240,
width=320,
)
prim_utils.create_prim("/World/Camera_warp", "Xform")
camera_cfg_warp = RayCasterCameraCfg(
prim_path="/World/Camera",
mesh_prim_paths=["/World/defaultGroundPlane"],
update_period=0,
offset=RayCasterCameraCfg.OffsetCfg(pos=(2.5, 2.5, 4.0), rot=offset_rot, convention="ros"),
debug_vis=False,
pattern_cfg=camera_pattern_cfg,
data_types=["distance_to_image_plane", "distance_to_camera", "normals"],
)
camera_warp = RayCasterCamera(camera_cfg_warp)
# create usd camera
camera_cfg_usd = CameraCfg(
height=240,
width=320,
prim_path="/World/Camera_usd",
update_period=0,
data_types=["distance_to_image_plane", "distance_to_camera", "normals"],
spawn=PinholeCameraCfg(
focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1e-6, 1.0e5)
),
offset=CameraCfg.OffsetCfg(pos=(2.5, 2.5, 4.0), rot=offset_rot, convention="ros"),
)
camera_usd = Camera(camera_cfg_usd)
# play sim
self.sim.reset()
self.sim.play()
# perform steps
for _ in range(5):
self.sim.step()
# update camera
camera_usd.update(self.dt)
camera_warp.update(self.dt)
# check image data
torch.testing.assert_close(
camera_usd.data.output["distance_to_image_plane"],
camera_warp.data.output["distance_to_image_plane"],
rtol=5e-3,
atol=1e-4,
)
torch.testing.assert_close(
camera_usd.data.output["distance_to_camera"],
camera_warp.data.output["distance_to_camera"],
rtol=5e-3,
atol=1e-4,
)
torch.testing.assert_close(
camera_usd.data.output["normals"][..., :3],
camera_warp.data.output["normals"],
rtol=1e-5,
atol=1e-4,
)
def test_output_equal_to_usdcamera_prim_offset(self):
"""Test that the output of the ray caster camera is equal to the output of the usd camera when both are placed
under an XForm prim that is translated and rotated from the world origin
."""
offset_rot = [-0.1251, 0.3617, 0.8731, -0.3020]
# gf quat
gf_quatf = Gf.Quatd()
gf_quatf.SetReal(QUAT_OPENGL[0])
gf_quatf.SetImaginary(tuple(QUAT_OPENGL[1:]))
camera_pattern_cfg = patterns.PinholeCameraPatternCfg(
focal_length=24.0,
horizontal_aperture=20.955,
height=240,
width=320,
)
prim_raycast_cam = prim_utils.create_prim("/World/Camera_warp", "Xform")
prim_raycast_cam.GetAttribute("xformOp:translate").Set(tuple(POSITION))
prim_raycast_cam.GetAttribute("xformOp:orient").Set(gf_quatf)
camera_cfg_warp = RayCasterCameraCfg(
prim_path="/World/Camera_warp",
mesh_prim_paths=["/World/defaultGroundPlane"],
update_period=0,
offset=RayCasterCameraCfg.OffsetCfg(pos=(0, 0, 2.0), rot=offset_rot, convention="ros"),
debug_vis=False,
pattern_cfg=camera_pattern_cfg,
data_types=["distance_to_image_plane", "distance_to_camera", "normals"],
)
camera_warp = RayCasterCamera(camera_cfg_warp)
# create usd camera
camera_cfg_usd = CameraCfg(
height=240,
width=320,
prim_path="/World/Camera_usd/camera",
update_period=0,
data_types=["distance_to_image_plane", "distance_to_camera", "normals"],
spawn=PinholeCameraCfg(
focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(1e-6, 1.0e5)
),
offset=CameraCfg.OffsetCfg(pos=(0, 0, 2.0), rot=offset_rot, convention="ros"),
)
prim_usd = prim_utils.create_prim("/World/Camera_usd", "Xform")
prim_usd.GetAttribute("xformOp:translate").Set(tuple(POSITION))
prim_usd.GetAttribute("xformOp:orient").Set(gf_quatf)
camera_usd = Camera(camera_cfg_usd)
# play sim
self.sim.reset()
self.sim.play()
# perform steps
for _ in range(5):
self.sim.step()
# update camera
camera_usd.update(self.dt)
camera_warp.update(self.dt)
# check if pos and orientation are correct
torch.testing.assert_close(camera_warp.data.pos_w[0], camera_usd.data.pos_w[0])
torch.testing.assert_close(camera_warp.data.quat_w_ros[0], camera_usd.data.quat_w_ros[0])
# check image data
torch.testing.assert_close(
camera_usd.data.output["distance_to_image_plane"],
camera_warp.data.output["distance_to_image_plane"],
rtol=5e-3,
atol=1e-4,
)
torch.testing.assert_close(
camera_usd.data.output["distance_to_camera"],
camera_warp.data.output["distance_to_camera"],
rtol=5e-3,
atol=1e-4,
)
torch.testing.assert_close(
camera_usd.data.output["normals"][..., :3],
camera_warp.data.output["normals"],
rtol=1e-5,
atol=1e-4,
)
if __name__ == "__main__":
run_tests()
| 23,839 | Python | 39.27027 | 120 | 0.598851 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/envs/check_base_env_floating_cube.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script demonstrates the base environment concept that combines a scene with an action,
observation and event manager for a floating cube.
"""
"""Launch Isaac Sim Simulator first."""
import argparse
from omni.isaac.orbit.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(description="This script demonstrates how to use the concept of an Environment.")
parser.add_argument("--num_envs", type=int, default=64, help="Number of environments to spawn.")
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
"""Rest everything follows."""
import torch
import omni.isaac.orbit.envs.mdp as mdp
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.assets import AssetBaseCfg, RigidObject, RigidObjectCfg
from omni.isaac.orbit.envs import BaseEnv, BaseEnvCfg
from omni.isaac.orbit.managers import EventTermCfg as EventTerm
from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup
from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm
from omni.isaac.orbit.managers import SceneEntityCfg
from omni.isaac.orbit.managers.action_manager import ActionTerm, ActionTermCfg
from omni.isaac.orbit.scene import InteractiveSceneCfg
from omni.isaac.orbit.terrains import TerrainImporterCfg
from omni.isaac.orbit.utils import configclass
##
# Scene definition
##
@configclass
class MySceneCfg(InteractiveSceneCfg):
"""Example scene configuration."""
# add terrain
terrain = TerrainImporterCfg(prim_path="/World/ground", terrain_type="plane", debug_vis=False)
# add cube
cube: RigidObjectCfg = RigidObjectCfg(
prim_path="{ENV_REGEX_NS}/cube",
spawn=sim_utils.CuboidCfg(
size=(0.2, 0.2, 0.2),
rigid_props=sim_utils.RigidBodyPropertiesCfg(max_depenetration_velocity=1.0),
mass_props=sim_utils.MassPropertiesCfg(mass=1.0),
physics_material=sim_utils.RigidBodyMaterialCfg(),
visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.5, 0.0, 0.0)),
),
init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 0.0, 5)),
)
# lights
light = AssetBaseCfg(
prim_path="/World/light",
spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0),
)
##
# Action Term
##
class CubeActionTerm(ActionTerm):
"""Simple action term that implements a PD controller to track a target position."""
_asset: RigidObject
"""The articulation asset on which the action term is applied."""
def __init__(self, cfg: ActionTermCfg, env: BaseEnv):
# call super constructor
super().__init__(cfg, env)
# create buffers
self._raw_actions = torch.zeros(env.num_envs, 3, device=self.device)
self._processed_actions = torch.zeros(env.num_envs, 3, device=self.device)
self._vel_command = torch.zeros(self.num_envs, 6, device=self.device)
# gains of controller
self.p_gain = 5.0
self.d_gain = 0.5
"""
Properties.
"""
@property
def action_dim(self) -> int:
return self._raw_actions.shape[1]
@property
def raw_actions(self) -> torch.Tensor:
# desired: (x, y, z)
return self._raw_actions
@property
def processed_actions(self) -> torch.Tensor:
return self._processed_actions
"""
Operations
"""
def process_actions(self, actions: torch.Tensor):
# store the raw actions
self._raw_actions[:] = actions
# no-processing of actions
self._processed_actions[:] = self._raw_actions[:]
def apply_actions(self):
# implement a PD controller to track the target position
pos_error = self._processed_actions - (self._asset.data.root_pos_w - self._env.scene.env_origins)
vel_error = -self._asset.data.root_lin_vel_w
# set velocity targets
self._vel_command[:, :3] = self.p_gain * pos_error + self.d_gain * vel_error
self._asset.write_root_velocity_to_sim(self._vel_command)
@configclass
class CubeActionTermCfg(ActionTermCfg):
"""Configuration for the cube action term."""
class_type: type = CubeActionTerm
##
# Observation Term
##
def base_position(env: BaseEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor:
"""Root linear velocity in the asset's root frame."""
# extract the used quantities (to enable type-hinting)
asset: RigidObject = env.scene[asset_cfg.name]
return asset.data.root_pos_w - env.scene.env_origins
##
# Environment settings
##
@configclass
class ActionsCfg:
"""Action specifications for the MDP."""
joint_pos = CubeActionTermCfg(asset_name="cube")
@configclass
class ObservationsCfg:
"""Observation specifications for the MDP."""
@configclass
class PolicyCfg(ObsGroup):
"""Observations for policy group."""
# cube velocity
position = ObsTerm(func=base_position, params={"asset_cfg": SceneEntityCfg("cube")})
def __post_init__(self):
self.enable_corruption = True
self.concatenate_terms = True
# observation groups
policy: PolicyCfg = PolicyCfg()
@configclass
class EventCfg:
"""Configuration for events."""
reset_base = EventTerm(
func=mdp.reset_root_state_uniform,
mode="reset",
params={
"pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)},
"velocity_range": {
"x": (-0.5, 0.5),
"y": (-0.5, 0.5),
"z": (-0.5, 0.5),
},
"asset_cfg": SceneEntityCfg("cube"),
},
)
##
# Environment configuration
##
@configclass
class CubeEnvCfg(BaseEnvCfg):
"""Configuration for the locomotion velocity-tracking environment."""
# Scene settings
scene: MySceneCfg = MySceneCfg(num_envs=args_cli.num_envs, env_spacing=2.5, replicate_physics=True)
# Basic settings
observations: ObservationsCfg = ObservationsCfg()
actions: ActionsCfg = ActionsCfg()
events: EventCfg = EventCfg()
def __post_init__(self):
"""Post initialization."""
# general settings
self.decimation = 2
# simulation settings
self.sim.dt = 0.01
self.sim.physics_material = self.scene.terrain.physics_material
def main():
"""Main function."""
# setup base environment
env = BaseEnv(cfg=CubeEnvCfg())
# setup target position commands
target_position = torch.rand(env.num_envs, 3, device=env.device) * 2
target_position[:, 2] += 2.0
# offset all targets so that they move to the world origin
target_position -= env.scene.env_origins
# simulate physics
count = 0
while simulation_app.is_running():
with torch.inference_mode():
# reset
if count % 300 == 0:
env.reset()
count = 0
# step env
obs, _ = env.step(target_position)
# print mean squared position error between target and current position
error = torch.norm(obs["policy"] - target_position).mean().item()
print(f"[Step: {count:04d}]: Mean position error: {error:.4f}")
# update counter
count += 1
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 7,627 | Python | 27.569288 | 114 | 0.649797 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/envs/test_base_env.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
# ignore private usage of variables warning
# pyright: reportPrivateUsage=none
from __future__ import annotations
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher
# Can set this to False to see the GUI for debugging
HEADLESS = True
# launch omniverse app
app_launcher = AppLauncher(headless=HEADLESS)
simulation_app = app_launcher.app
"""Rest everything follows."""
import torch
import unittest
import omni.usd
from omni.isaac.orbit.envs import BaseEnv, BaseEnvCfg
from omni.isaac.orbit.scene import InteractiveSceneCfg
from omni.isaac.orbit.utils import configclass
@configclass
class EmptyActionsCfg:
"""Action specifications for the environment."""
pass
@configclass
class EmptySceneCfg(InteractiveSceneCfg):
"""Configuration for an empty scene."""
pass
def get_empty_base_env_cfg(device: str = "cuda:0", num_envs: int = 1, env_spacing: float = 1.0):
"""Generate base environment config based on device"""
@configclass
class EmptyEnvCfg(BaseEnvCfg):
"""Configuration for the empty test environment."""
# Scene settings
scene: EmptySceneCfg = EmptySceneCfg(num_envs=num_envs, env_spacing=env_spacing)
# Basic settings
actions: EmptyActionsCfg = EmptyActionsCfg()
def __post_init__(self):
"""Post initialization."""
# step settings
self.decimation = 4 # env step every 4 sim steps: 200Hz / 4 = 50Hz
# simulation settings
self.sim.dt = 0.005 # sim step every 5ms: 200Hz
# pass device down from test
self.sim.device = device
return EmptyEnvCfg()
class TestBaseEnv(unittest.TestCase):
"""Test for base env class"""
"""
Tests
"""
def test_initialization(self):
for device in ("cuda:0", "cpu"):
with self.subTest(device=device):
# create a new stage
omni.usd.get_context().new_stage()
# create environment
env = BaseEnv(cfg=get_empty_base_env_cfg(device=device))
# check size of action manager terms
self.assertEqual(env.action_manager.total_action_dim, 0)
self.assertEqual(len(env.action_manager.active_terms), 0)
self.assertEqual(len(env.action_manager.action_term_dim), 0)
# check size of observation manager terms
self.assertEqual(len(env.observation_manager.active_terms), 0)
self.assertEqual(len(env.observation_manager.group_obs_dim), 0)
self.assertEqual(len(env.observation_manager.group_obs_term_dim), 0)
self.assertEqual(len(env.observation_manager.group_obs_concatenate), 0)
# create actions of correct size (1,0)
act = torch.randn_like(env.action_manager.action)
# step environment to verify setup
for _ in range(2):
obs, ext = env.step(action=act)
# close the environment
env.close()
| 3,207 | Python | 30.45098 | 96 | 0.635485 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/envs/test_null_command_term.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
app_launcher = AppLauncher(headless=True)
simulation_app = app_launcher.app
"""Rest everything follows."""
import unittest
from collections import namedtuple
from omni.isaac.orbit.envs.mdp import NullCommandCfg
class TestNullCommandTerm(unittest.TestCase):
"""Test cases for null command generator."""
def setUp(self) -> None:
self.env = namedtuple("RLTaskEnv", ["num_envs", "dt", "device"])(20, 0.1, "cpu")
def test_str(self):
"""Test the string representation of the command manager."""
cfg = NullCommandCfg()
command_term = cfg.class_type(cfg, self.env)
# print the expected string
print()
print(command_term)
def test_compute(self):
"""Test the compute function. For null command generator, it does nothing."""
cfg = NullCommandCfg()
command_term = cfg.class_type(cfg, self.env)
# test the reset function
command_term.reset()
# test the compute function
command_term.compute(dt=self.env.dt)
# expect error
with self.assertRaises(RuntimeError):
command_term.command
if __name__ == "__main__":
run_tests()
| 1,423 | Python | 26.384615 | 88 | 0.657765 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/envs/check_base_env_anymal_locomotion.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script demonstrates the environment concept that combines a scene with an action,
observation and event manager for a quadruped robot.
A locomotion policy is loaded and used to control the robot. This shows how to use the
environment with a policy.
"""
"""Launch Isaac Sim Simulator first."""
import argparse
from omni.isaac.orbit.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(description="This script demonstrates how to use the concept of an Environment.")
parser.add_argument("--num_envs", type=int, default=64, help="Number of environments to spawn.")
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
"""Rest everything follows."""
import os
import torch
import omni.isaac.orbit.envs.mdp as mdp
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg
from omni.isaac.orbit.envs import BaseEnv, BaseEnvCfg
from omni.isaac.orbit.managers import EventTermCfg as EventTerm
from omni.isaac.orbit.managers import ObservationGroupCfg as ObsGroup
from omni.isaac.orbit.managers import ObservationTermCfg as ObsTerm
from omni.isaac.orbit.managers import SceneEntityCfg
from omni.isaac.orbit.scene import InteractiveSceneCfg
from omni.isaac.orbit.sensors import RayCasterCfg, patterns
from omni.isaac.orbit.terrains import TerrainImporterCfg
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit.utils.assets import ISAAC_ORBIT_NUCLEUS_DIR, check_file_path, read_file
from omni.isaac.orbit.utils.noise import AdditiveUniformNoiseCfg as Unoise
##
# Pre-defined configs
##
from omni.isaac.orbit.terrains.config.rough import ROUGH_TERRAINS_CFG # isort: skip
from omni.isaac.orbit_assets.anymal import ANYMAL_C_CFG # isort: skip
##
# Scene definition
##
@configclass
class MySceneCfg(InteractiveSceneCfg):
"""Example scene configuration."""
# add terrain
terrain = TerrainImporterCfg(
prim_path="/World/ground",
terrain_type="generator",
terrain_generator=ROUGH_TERRAINS_CFG,
physics_material=sim_utils.RigidBodyMaterialCfg(
friction_combine_mode="multiply",
restitution_combine_mode="multiply",
static_friction=1.0,
dynamic_friction=1.0,
),
debug_vis=False,
)
# add robot
robot: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
# sensors
height_scanner = RayCasterCfg(
prim_path="{ENV_REGEX_NS}/Robot/base",
offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)),
attach_yaw_only=True,
pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]),
debug_vis=True,
mesh_prim_paths=["/World/ground"],
)
# lights
light = AssetBaseCfg(
prim_path="/World/light",
spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0),
)
##
# MDP settings
##
def constant_commands(env: BaseEnv) -> torch.Tensor:
"""The generated command from the command generator."""
return torch.tensor([[1, 0, 0]], device=env.device).repeat(env.num_envs, 1)
@configclass
class ActionsCfg:
"""Action specifications for the MDP."""
joint_pos = mdp.JointPositionActionCfg(asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True)
@configclass
class ObservationsCfg:
"""Observation specifications for the MDP."""
@configclass
class PolicyCfg(ObsGroup):
"""Observations for policy group."""
# observation terms (order preserved)
base_lin_vel = ObsTerm(func=mdp.base_lin_vel, noise=Unoise(n_min=-0.1, n_max=0.1))
base_ang_vel = ObsTerm(func=mdp.base_ang_vel, noise=Unoise(n_min=-0.2, n_max=0.2))
projected_gravity = ObsTerm(
func=mdp.projected_gravity,
noise=Unoise(n_min=-0.05, n_max=0.05),
)
velocity_commands = ObsTerm(func=constant_commands)
joint_pos = ObsTerm(func=mdp.joint_pos_rel, noise=Unoise(n_min=-0.01, n_max=0.01))
joint_vel = ObsTerm(func=mdp.joint_vel_rel, noise=Unoise(n_min=-1.5, n_max=1.5))
actions = ObsTerm(func=mdp.last_action)
height_scan = ObsTerm(
func=mdp.height_scan,
params={"sensor_cfg": SceneEntityCfg("height_scanner")},
noise=Unoise(n_min=-0.1, n_max=0.1),
clip=(-1.0, 1.0),
)
def __post_init__(self):
self.enable_corruption = True
self.concatenate_terms = True
# observation groups
policy: PolicyCfg = PolicyCfg()
@configclass
class EventCfg:
"""Configuration for events."""
reset_base = EventTerm(
func=mdp.reset_root_state_uniform,
mode="reset",
params={
"pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)},
"velocity_range": {
"x": (-0.5, 0.5),
"y": (-0.5, 0.5),
"z": (-0.5, 0.5),
"roll": (-0.5, 0.5),
"pitch": (-0.5, 0.5),
"yaw": (-0.5, 0.5),
},
},
)
##
# Environment configuration
##
@configclass
class QuadrupedEnvCfg(BaseEnvCfg):
"""Configuration for the locomotion velocity-tracking environment."""
# Scene settings
scene: MySceneCfg = MySceneCfg(num_envs=args_cli.num_envs, env_spacing=2.5, replicate_physics=True)
# Basic settings
observations: ObservationsCfg = ObservationsCfg()
actions: ActionsCfg = ActionsCfg()
events: EventCfg = EventCfg()
def __post_init__(self):
"""Post initialization."""
# general settings
self.decimation = 4
self.episode_length_s = 20.0
# simulation settings
self.sim.dt = 0.005
# update sensor update periods
# we tick all the sensors based on the smallest update period (physics update period)
if self.scene.height_scanner is not None:
self.scene.height_scanner.update_period = self.decimation * self.sim.dt
def main():
"""Main function."""
# setup base environment
env = BaseEnv(cfg=QuadrupedEnvCfg())
obs, _ = env.reset()
# load level policy
policy_path = os.path.join(ISAAC_ORBIT_NUCLEUS_DIR, "Policies", "ANYmal-C", "policy.pt")
# check if policy file exists
if not check_file_path(policy_path):
raise FileNotFoundError(f"Policy file '{policy_path}' does not exist.")
file_bytes = read_file(policy_path)
# jit load the policy
locomotion_policy = torch.jit.load(file_bytes)
locomotion_policy.to(env.device)
locomotion_policy.eval()
# simulate physics
count = 0
while simulation_app.is_running():
with torch.inference_mode():
# reset
if count % 1000 == 0:
obs, _ = env.reset()
count = 0
print("[INFO]: Resetting robots state...")
# infer action
action = locomotion_policy(obs["policy"])
# step env
obs, _ = env.step(action)
# update counter
count += 1
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 7,495 | Python | 29.348178 | 118 | 0.644029 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/managers/test_observation_manager.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
# needed to import for allowing type-hinting: torch.Tensor | None
from __future__ import annotations
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import torch
import unittest
from collections import namedtuple
from omni.isaac.orbit.managers import ManagerTermBase, ObservationGroupCfg, ObservationManager, ObservationTermCfg
from omni.isaac.orbit.utils import configclass
def grilled_chicken(env):
return torch.ones(env.num_envs, 4, device=env.device)
def grilled_chicken_with_bbq(env, bbq: bool):
return bbq * torch.ones(env.num_envs, 1, device=env.device)
def grilled_chicken_with_curry(env, hot: bool):
return hot * 2 * torch.ones(env.num_envs, 1, device=env.device)
def grilled_chicken_with_yoghurt(env, hot: bool, bland: float):
return hot * bland * torch.ones(env.num_envs, 5, device=env.device)
def grilled_chicken_with_yoghurt_and_bbq(env, hot: bool, bland: float, bbq: bool = False):
return hot * bland * bbq * torch.ones(env.num_envs, 3, device=env.device)
class complex_function_class(ManagerTermBase):
def __init__(self, cfg: ObservationTermCfg, env: object):
self.cfg = cfg
self.env = env
# define some variables
self._time_passed = torch.zeros(env.num_envs, device=env.device)
def reset(self, env_ids: torch.Tensor | None = None):
if env_ids is None:
env_ids = slice(None)
self._time_passed[env_ids] = 0.0
def __call__(self, env: object, interval: float) -> torch.Tensor:
self._time_passed += interval
return self._time_passed.clone().unsqueeze(-1)
class non_callable_complex_function_class(ManagerTermBase):
def __init__(self, cfg: ObservationTermCfg, env: object):
self.cfg = cfg
self.env = env
# define some variables
self._cost = 2 * self.env.num_envs
def call_me(self, env: object) -> torch.Tensor:
return torch.ones(env.num_envs, 2, device=env.device) * self._cost
class MyDataClass:
def __init__(self, num_envs: int, device: str):
self.pos_w = torch.rand((num_envs, 3), device=device)
self.lin_vel_w = torch.rand((num_envs, 3), device=device)
def pos_w_data(env) -> torch.Tensor:
return env.data.pos_w
def lin_vel_w_data(env) -> torch.Tensor:
return env.data.lin_vel_w
class TestObservationManager(unittest.TestCase):
"""Test cases for various situations with observation manager."""
def setUp(self) -> None:
# set up the environment
self.num_envs = 20
self.device = "cuda:0"
# create dummy environment
self.env = namedtuple("BaseEnv", ["num_envs", "device", "data"])(
self.num_envs, self.device, MyDataClass(self.num_envs, self.device)
)
def test_str(self):
"""Test the string representation of the observation manager."""
@configclass
class MyObservationManagerCfg:
"""Test config class for observation manager."""
@configclass
class SampleGroupCfg(ObservationGroupCfg):
"""Test config class for policy observation group."""
term_1 = ObservationTermCfg(func="__main__:grilled_chicken", scale=10)
term_2 = ObservationTermCfg(func=grilled_chicken, scale=2)
term_3 = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=5, params={"bbq": True})
term_4 = ObservationTermCfg(
func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0}
)
term_5 = ObservationTermCfg(
func=grilled_chicken_with_yoghurt_and_bbq, scale=1.0, params={"hot": False, "bland": 2.0}
)
policy: ObservationGroupCfg = SampleGroupCfg()
# create observation manager
cfg = MyObservationManagerCfg()
self.obs_man = ObservationManager(cfg, self.env)
self.assertEqual(len(self.obs_man.active_terms["policy"]), 5)
# print the expected string
print()
print(self.obs_man)
def test_config_equivalence(self):
"""Test the equivalence of observation manager created from different config types."""
# create from config class
@configclass
class MyObservationManagerCfg:
"""Test config class for observation manager."""
@configclass
class SampleGroupCfg(ObservationGroupCfg):
"""Test config class for policy observation group."""
your_term = ObservationTermCfg(func="__main__:grilled_chicken", scale=10)
his_term = ObservationTermCfg(func=grilled_chicken, scale=2)
my_term = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=5, params={"bbq": True})
her_term = ObservationTermCfg(
func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0}
)
policy = SampleGroupCfg()
critic = SampleGroupCfg(concatenate_terms=False, her_term=None)
cfg = MyObservationManagerCfg()
obs_man_from_cfg = ObservationManager(cfg, self.env)
# create from config class
@configclass
class MyObservationManagerAnnotatedCfg:
"""Test config class for observation manager with annotations on terms."""
@configclass
class SampleGroupCfg(ObservationGroupCfg):
"""Test config class for policy observation group."""
your_term: ObservationTermCfg = ObservationTermCfg(func="__main__:grilled_chicken", scale=10)
his_term: ObservationTermCfg = ObservationTermCfg(func=grilled_chicken, scale=2)
my_term: ObservationTermCfg = ObservationTermCfg(
func=grilled_chicken_with_bbq, scale=5, params={"bbq": True}
)
her_term: ObservationTermCfg = ObservationTermCfg(
func=grilled_chicken_with_yoghurt, scale=1.0, params={"hot": False, "bland": 2.0}
)
policy: ObservationGroupCfg = SampleGroupCfg()
critic: ObservationGroupCfg = SampleGroupCfg(concatenate_terms=False, her_term=None)
cfg = MyObservationManagerAnnotatedCfg()
obs_man_from_annotated_cfg = ObservationManager(cfg, self.env)
# check equivalence
# parsed terms
self.assertEqual(obs_man_from_cfg.active_terms, obs_man_from_annotated_cfg.active_terms)
self.assertEqual(obs_man_from_cfg.group_obs_term_dim, obs_man_from_annotated_cfg.group_obs_term_dim)
self.assertEqual(obs_man_from_cfg.group_obs_dim, obs_man_from_annotated_cfg.group_obs_dim)
# parsed term configs
self.assertEqual(obs_man_from_cfg._group_obs_term_cfgs, obs_man_from_annotated_cfg._group_obs_term_cfgs)
self.assertEqual(obs_man_from_cfg._group_obs_concatenate, obs_man_from_annotated_cfg._group_obs_concatenate)
def test_config_terms(self):
"""Test the number of terms in the observation manager."""
@configclass
class MyObservationManagerCfg:
"""Test config class for observation manager."""
@configclass
class SampleGroupCfg(ObservationGroupCfg):
"""Test config class for policy observation group."""
term_1 = ObservationTermCfg(func=grilled_chicken, scale=10)
term_2 = ObservationTermCfg(func=grilled_chicken_with_curry, scale=0.0, params={"hot": False})
policy: ObservationGroupCfg = SampleGroupCfg()
critic: ObservationGroupCfg = SampleGroupCfg(term_2=None)
# create observation manager
cfg = MyObservationManagerCfg()
self.obs_man = ObservationManager(cfg, self.env)
self.assertEqual(len(self.obs_man.active_terms["policy"]), 2)
self.assertEqual(len(self.obs_man.active_terms["critic"]), 1)
def test_compute(self):
"""Test the observation computation."""
@configclass
class MyObservationManagerCfg:
"""Test config class for observation manager."""
@configclass
class PolicyCfg(ObservationGroupCfg):
"""Test config class for policy observation group."""
term_1 = ObservationTermCfg(func=grilled_chicken, scale=10)
term_2 = ObservationTermCfg(func=grilled_chicken_with_curry, scale=0.0, params={"hot": False})
term_3 = ObservationTermCfg(func=pos_w_data, scale=2.0)
term_4 = ObservationTermCfg(func=lin_vel_w_data, scale=1.5)
@configclass
class CriticCfg(ObservationGroupCfg):
term_1 = ObservationTermCfg(func=pos_w_data, scale=2.0)
term_2 = ObservationTermCfg(func=lin_vel_w_data, scale=1.5)
term_3 = ObservationTermCfg(func=pos_w_data, scale=2.0)
term_4 = ObservationTermCfg(func=lin_vel_w_data, scale=1.5)
policy: ObservationGroupCfg = PolicyCfg()
critic: ObservationGroupCfg = CriticCfg()
# create observation manager
cfg = MyObservationManagerCfg()
self.obs_man = ObservationManager(cfg, self.env)
# compute observation using manager
observations = self.obs_man.compute()
# obtain the group observations
obs_policy: torch.Tensor = observations["policy"]
obs_critic: torch.Tensor = observations["critic"]
# check the observation shape
self.assertEqual((self.env.num_envs, 11), obs_policy.shape)
self.assertEqual((self.env.num_envs, 12), obs_critic.shape)
# make sure that the data are the same for same terms
# -- within group
torch.testing.assert_close(obs_critic[:, 0:3], obs_critic[:, 6:9])
torch.testing.assert_close(obs_critic[:, 3:6], obs_critic[:, 9:12])
# -- between groups
torch.testing.assert_close(obs_policy[:, 5:8], obs_critic[:, 0:3])
torch.testing.assert_close(obs_policy[:, 8:11], obs_critic[:, 3:6])
def test_invalid_observation_config(self):
"""Test the invalid observation config."""
@configclass
class MyObservationManagerCfg:
"""Test config class for observation manager."""
@configclass
class PolicyCfg(ObservationGroupCfg):
"""Test config class for policy observation group."""
term_1 = ObservationTermCfg(func=grilled_chicken_with_bbq, scale=0.1, params={"hot": False})
term_2 = ObservationTermCfg(func=grilled_chicken_with_yoghurt, scale=2.0, params={"hot": False})
policy: ObservationGroupCfg = PolicyCfg()
# create observation manager
cfg = MyObservationManagerCfg()
# check the invalid config
with self.assertRaises(ValueError):
self.obs_man = ObservationManager(cfg, self.env)
def test_callable_class_term(self):
"""Test the observation computation with callable class term."""
@configclass
class MyObservationManagerCfg:
"""Test config class for observation manager."""
@configclass
class PolicyCfg(ObservationGroupCfg):
"""Test config class for policy observation group."""
term_1 = ObservationTermCfg(func=grilled_chicken, scale=10)
term_2 = ObservationTermCfg(func=complex_function_class, scale=0.2, params={"interval": 0.5})
policy: ObservationGroupCfg = PolicyCfg()
# create observation manager
cfg = MyObservationManagerCfg()
self.obs_man = ObservationManager(cfg, self.env)
# compute observation using manager
observations = self.obs_man.compute()
# check the observation
self.assertEqual((self.env.num_envs, 5), observations["policy"].shape)
self.assertAlmostEqual(observations["policy"][0, -1].item(), 0.2 * 0.5)
# check memory in term
num_exec_count = 10
for _ in range(num_exec_count):
observations = self.obs_man.compute()
self.assertAlmostEqual(observations["policy"][0, -1].item(), 0.2 * 0.5 * (num_exec_count + 1))
# check reset works
self.obs_man.reset(env_ids=[0, 4, 9, 14, 19])
observations = self.obs_man.compute()
self.assertAlmostEqual(observations["policy"][0, -1].item(), 0.2 * 0.5)
self.assertAlmostEqual(observations["policy"][1, -1].item(), 0.2 * 0.5 * (num_exec_count + 2))
def test_non_callable_class_term(self):
"""Test the observation computation with non-callable class term."""
@configclass
class MyObservationManagerCfg:
"""Test config class for observation manager."""
@configclass
class PolicyCfg(ObservationGroupCfg):
"""Test config class for policy observation group."""
term_1 = ObservationTermCfg(func=grilled_chicken, scale=10)
term_2 = ObservationTermCfg(func=non_callable_complex_function_class, scale=0.2)
policy: ObservationGroupCfg = PolicyCfg()
# create observation manager config
cfg = MyObservationManagerCfg()
# create observation manager
with self.assertRaises(NotImplementedError):
self.obs_man = ObservationManager(cfg, self.env)
if __name__ == "__main__":
run_tests()
| 13,717 | Python | 38.762319 | 116 | 0.632135 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/managers/test_reward_manager.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import unittest
from collections import namedtuple
from omni.isaac.orbit.managers import RewardManager, RewardTermCfg
from omni.isaac.orbit.utils import configclass
def grilled_chicken(env):
return 1
def grilled_chicken_with_bbq(env, bbq: bool):
return 0
def grilled_chicken_with_curry(env, hot: bool):
return 0
def grilled_chicken_with_yoghurt(env, hot: bool, bland: float):
return 0
class TestRewardManager(unittest.TestCase):
"""Test cases for various situations with reward manager."""
def setUp(self) -> None:
self.env = namedtuple("RLTaskEnv", ["num_envs", "dt", "device"])(20, 0.1, "cpu")
def test_str(self):
"""Test the string representation of the reward manager."""
cfg = {
"term_1": RewardTermCfg(func=grilled_chicken, weight=10),
"term_2": RewardTermCfg(func=grilled_chicken_with_bbq, weight=5, params={"bbq": True}),
"term_3": RewardTermCfg(
func=grilled_chicken_with_yoghurt,
weight=1.0,
params={"hot": False, "bland": 2.0},
),
}
self.rew_man = RewardManager(cfg, self.env)
self.assertEqual(len(self.rew_man.active_terms), 3)
# print the expected string
print()
print(self.rew_man)
def test_config_equivalence(self):
"""Test the equivalence of reward manager created from different config types."""
# create from dictionary
cfg = {
"my_term": RewardTermCfg(func=grilled_chicken, weight=10),
"your_term": RewardTermCfg(func=grilled_chicken_with_bbq, weight=2.0, params={"bbq": True}),
"his_term": RewardTermCfg(
func=grilled_chicken_with_yoghurt,
weight=1.0,
params={"hot": False, "bland": 2.0},
),
}
rew_man_from_dict = RewardManager(cfg, self.env)
# create from config class
@configclass
class MyRewardManagerCfg:
"""Reward manager config with no type annotations."""
my_term = RewardTermCfg(func=grilled_chicken, weight=10.0)
your_term = RewardTermCfg(func=grilled_chicken_with_bbq, weight=2.0, params={"bbq": True})
his_term = RewardTermCfg(func=grilled_chicken_with_yoghurt, weight=1.0, params={"hot": False, "bland": 2.0})
cfg = MyRewardManagerCfg()
rew_man_from_cfg = RewardManager(cfg, self.env)
# create from config class
@configclass
class MyRewardManagerAnnotatedCfg:
"""Reward manager config with type annotations."""
my_term: RewardTermCfg = RewardTermCfg(func=grilled_chicken, weight=10.0)
your_term: RewardTermCfg = RewardTermCfg(func=grilled_chicken_with_bbq, weight=2.0, params={"bbq": True})
his_term: RewardTermCfg = RewardTermCfg(
func=grilled_chicken_with_yoghurt, weight=1.0, params={"hot": False, "bland": 2.0}
)
cfg = MyRewardManagerAnnotatedCfg()
rew_man_from_annotated_cfg = RewardManager(cfg, self.env)
# check equivalence
# parsed terms
self.assertEqual(rew_man_from_dict.active_terms, rew_man_from_annotated_cfg.active_terms)
self.assertEqual(rew_man_from_cfg.active_terms, rew_man_from_annotated_cfg.active_terms)
self.assertEqual(rew_man_from_dict.active_terms, rew_man_from_cfg.active_terms)
# parsed term configs
self.assertEqual(rew_man_from_dict._term_cfgs, rew_man_from_annotated_cfg._term_cfgs)
self.assertEqual(rew_man_from_cfg._term_cfgs, rew_man_from_annotated_cfg._term_cfgs)
self.assertEqual(rew_man_from_dict._term_cfgs, rew_man_from_cfg._term_cfgs)
def test_compute(self):
"""Test the computation of reward."""
cfg = {
"term_1": RewardTermCfg(func=grilled_chicken, weight=10),
"term_2": RewardTermCfg(func=grilled_chicken_with_curry, weight=0.0, params={"hot": False}),
}
self.rew_man = RewardManager(cfg, self.env)
# compute expected reward
expected_reward = cfg["term_1"].weight * self.env.dt
# compute reward using manager
rewards = self.rew_man.compute(dt=self.env.dt)
# check the reward for environment index 0
self.assertEqual(float(rewards[0]), expected_reward)
self.assertEqual(tuple(rewards.shape), (self.env.num_envs,))
def test_active_terms(self):
"""Test the correct reading of active terms."""
cfg = {
"term_1": RewardTermCfg(func=grilled_chicken, weight=10),
"term_2": RewardTermCfg(func=grilled_chicken_with_bbq, weight=5, params={"bbq": True}),
"term_3": RewardTermCfg(func=grilled_chicken_with_curry, weight=0.0, params={"hot": False}),
}
self.rew_man = RewardManager(cfg, self.env)
self.assertEqual(len(self.rew_man.active_terms), 3)
def test_missing_weight(self):
"""Test the missing of weight in the config."""
# TODO: The error should be raised during the config parsing, not during the reward manager creation.
cfg = {
"term_1": RewardTermCfg(func=grilled_chicken, weight=10),
"term_2": RewardTermCfg(func=grilled_chicken_with_bbq, params={"bbq": True}),
}
with self.assertRaises(TypeError):
self.rew_man = RewardManager(cfg, self.env)
def test_invalid_reward_func_module(self):
"""Test the handling of invalid reward function's module in string representation."""
cfg = {
"term_1": RewardTermCfg(func=grilled_chicken, weight=10),
"term_2": RewardTermCfg(func=grilled_chicken_with_bbq, weight=5, params={"bbq": True}),
"term_3": RewardTermCfg(func="a:grilled_chicken_with_no_bbq", weight=0.1, params={"hot": False}),
}
with self.assertRaises(ValueError):
self.rew_man = RewardManager(cfg, self.env)
def test_invalid_reward_config(self):
"""Test the handling of invalid reward function's config parameters."""
cfg = {
"term_1": RewardTermCfg(func=grilled_chicken_with_bbq, weight=0.1, params={"hot": False}),
"term_2": RewardTermCfg(func=grilled_chicken_with_yoghurt, weight=2.0, params={"hot": False}),
}
with self.assertRaises(ValueError):
self.rew_man = RewardManager(cfg, self.env)
if __name__ == "__main__":
run_tests()
| 6,805 | Python | 39.272189 | 120 | 0.629096 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/deps/test_torch.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import torch
import torch.utils.benchmark as benchmark
import unittest
from omni.isaac.orbit.app import run_tests
class TestTorchOperations(unittest.TestCase):
"""Tests for assuring torch related operations used in Orbit."""
def test_array_slicing(self):
"""Check that using ellipsis and slices work for torch tensors."""
size = (400, 300, 5)
my_tensor = torch.rand(size, device="cuda:0")
self.assertEqual(my_tensor[..., 0].shape, (400, 300))
self.assertEqual(my_tensor[:, :, 0].shape, (400, 300))
self.assertEqual(my_tensor[slice(None), slice(None), 0].shape, (400, 300))
with self.assertRaises(IndexError):
my_tensor[..., ..., 0]
self.assertEqual(my_tensor[0, ...].shape, (300, 5))
self.assertEqual(my_tensor[0, :, :].shape, (300, 5))
self.assertEqual(my_tensor[0, slice(None), slice(None)].shape, (300, 5))
self.assertEqual(my_tensor[0, ..., ...].shape, (300, 5))
self.assertEqual(my_tensor[..., 0, 0].shape, (400,))
self.assertEqual(my_tensor[slice(None), 0, 0].shape, (400,))
self.assertEqual(my_tensor[:, 0, 0].shape, (400,))
def test_array_circular(self):
"""Check circular buffer implementation in torch."""
size = (10, 30, 5)
my_tensor = torch.rand(size, device="cuda:0")
# roll up the tensor without cloning
my_tensor_1 = my_tensor.clone()
my_tensor_1[:, 1:, :] = my_tensor_1[:, :-1, :]
my_tensor_1[:, 0, :] = my_tensor[:, -1, :]
# check that circular buffer works as expected
error = torch.max(torch.abs(my_tensor_1 - my_tensor.roll(1, dims=1)))
self.assertNotEqual(error.item(), 0.0)
self.assertFalse(torch.allclose(my_tensor_1, my_tensor.roll(1, dims=1)))
# roll up the tensor with cloning
my_tensor_2 = my_tensor.clone()
my_tensor_2[:, 1:, :] = my_tensor_2[:, :-1, :].clone()
my_tensor_2[:, 0, :] = my_tensor[:, -1, :]
# check that circular buffer works as expected
error = torch.max(torch.abs(my_tensor_2 - my_tensor.roll(1, dims=1)))
self.assertEqual(error.item(), 0.0)
self.assertTrue(torch.allclose(my_tensor_2, my_tensor.roll(1, dims=1)))
# roll up the tensor with detach operation
my_tensor_3 = my_tensor.clone()
my_tensor_3[:, 1:, :] = my_tensor_3[:, :-1, :].detach()
my_tensor_3[:, 0, :] = my_tensor[:, -1, :]
# check that circular buffer works as expected
error = torch.max(torch.abs(my_tensor_3 - my_tensor.roll(1, dims=1)))
self.assertNotEqual(error.item(), 0.0)
self.assertFalse(torch.allclose(my_tensor_3, my_tensor.roll(1, dims=1)))
# roll up the tensor with roll operation
my_tensor_4 = my_tensor.clone()
my_tensor_4 = my_tensor_4.roll(1, dims=1)
my_tensor_4[:, 0, :] = my_tensor[:, -1, :]
# check that circular buffer works as expected
error = torch.max(torch.abs(my_tensor_4 - my_tensor.roll(1, dims=1)))
self.assertEqual(error.item(), 0.0)
self.assertTrue(torch.allclose(my_tensor_4, my_tensor.roll(1, dims=1)))
def test_array_circular_copy(self):
"""Check that circular buffer implementation in torch is copying data."""
size = (10, 30, 5)
my_tensor = torch.rand(size, device="cuda:0")
my_tensor_clone = my_tensor.clone()
# roll up the tensor
my_tensor_1 = my_tensor.clone()
my_tensor_1[:, 1:, :] = my_tensor_1[:, :-1, :].clone()
my_tensor_1[:, 0, :] = my_tensor[:, -1, :]
# change the source tensor
my_tensor[:, 0, :] = 1000
# check that circular buffer works as expected
self.assertFalse(torch.allclose(my_tensor_1, my_tensor.roll(1, dims=1)))
self.assertTrue(torch.allclose(my_tensor_1, my_tensor_clone.roll(1, dims=1)))
def test_array_multi_indexing(self):
"""Check multi-indexing works for torch tensors."""
size = (400, 300, 5)
my_tensor = torch.rand(size, device="cuda:0")
# this fails since array indexing cannot be broadcasted!!
with self.assertRaises(IndexError):
my_tensor[[0, 1, 2, 3], [0, 1, 2, 3, 4]]
def test_array_single_indexing(self):
"""Check how indexing effects the returned tensor."""
size = (400, 300, 5)
my_tensor = torch.rand(size, device="cuda:0")
# obtain a slice of the tensor
my_slice = my_tensor[0, ...]
self.assertEqual(my_slice.untyped_storage().data_ptr(), my_tensor.untyped_storage().data_ptr())
# obtain a slice over ranges
my_slice = my_tensor[0:2, ...]
self.assertEqual(my_slice.untyped_storage().data_ptr(), my_tensor.untyped_storage().data_ptr())
# obtain a slice over list
my_slice = my_tensor[[0, 1], ...]
self.assertNotEqual(my_slice.untyped_storage().data_ptr(), my_tensor.untyped_storage().data_ptr())
# obtain a slice over tensor
my_slice = my_tensor[torch.tensor([0, 1]), ...]
self.assertNotEqual(my_slice.untyped_storage().data_ptr(), my_tensor.untyped_storage().data_ptr())
def test_logical_or(self):
"""Test bitwise or operation."""
size = (400, 300, 5)
my_tensor_1 = torch.rand(size, device="cuda:0") > 0.5
my_tensor_2 = torch.rand(size, device="cuda:0") < 0.5
# check the speed of logical or
timer_logical_or = benchmark.Timer(
stmt="torch.logical_or(my_tensor_1, my_tensor_2)",
globals={"my_tensor_1": my_tensor_1, "my_tensor_2": my_tensor_2},
)
timer_bitwise_or = benchmark.Timer(
stmt="my_tensor_1 | my_tensor_2", globals={"my_tensor_1": my_tensor_1, "my_tensor_2": my_tensor_2}
)
print("Time for logical or:", timer_logical_or.timeit(number=1000))
print("Time for bitwise or:", timer_bitwise_or.timeit(number=1000))
# check that logical or works as expected
output_logical_or = torch.logical_or(my_tensor_1, my_tensor_2)
output_bitwise_or = my_tensor_1 | my_tensor_2
self.assertTrue(torch.allclose(output_logical_or, output_bitwise_or))
if __name__ == "__main__":
run_tests()
| 6,388 | Python | 40.219355 | 110 | 0.595492 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/deps/test_scipy.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
# isort: off
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
# isort: on
import numpy as np
import scipy.interpolate as interpolate
import unittest
from omni.isaac.orbit.app import run_tests
class TestScipyOperations(unittest.TestCase):
"""Tests for assuring scipy related operations used in Orbit."""
def test_interpolation(self):
"""Test scipy interpolation 2D method."""
# parameters
size = (10.0, 12.0)
horizontal_scale = 0.1
vertical_scale = 0.005
downsampled_scale = 0.2
noise_range = (-0.02, 0.1)
noise_step = 0.02
# switch parameters to discrete units
# -- horizontal scale
width_pixels = int(size[0] / horizontal_scale)
length_pixels = int(size[1] / horizontal_scale)
# -- downsampled scale
width_downsampled = int(size[0] / downsampled_scale)
length_downsampled = int(size[1] / downsampled_scale)
# -- height
height_min = int(noise_range[0] / vertical_scale)
height_max = int(noise_range[1] / vertical_scale)
height_step = int(noise_step / vertical_scale)
# create range of heights possible
height_range = np.arange(height_min, height_max + height_step, height_step)
# sample heights randomly from the range along a grid
height_field_downsampled = np.random.choice(height_range, size=(width_downsampled, length_downsampled))
# create interpolation function for the sampled heights
x = np.linspace(0, size[0] * horizontal_scale, width_downsampled)
y = np.linspace(0, size[1] * horizontal_scale, length_downsampled)
# interpolate the sampled heights to obtain the height field
x_upsampled = np.linspace(0, size[0] * horizontal_scale, width_pixels)
y_upsampled = np.linspace(0, size[1] * horizontal_scale, length_pixels)
# -- method 1: interp2d (this will be deprecated in the future 1.12 release)
func_interp2d = interpolate.interp2d(y, x, height_field_downsampled, kind="cubic")
z_upsampled_interp2d = func_interp2d(y_upsampled, x_upsampled)
# -- method 2: RectBivariateSpline (alternate to interp2d)
func_RectBiVariate = interpolate.RectBivariateSpline(x, y, height_field_downsampled)
z_upsampled_RectBivariant = func_RectBiVariate(x_upsampled, y_upsampled)
# -- method 3: RegularGridInterpolator (recommended from scipy but slow!)
# Ref: https://github.com/scipy/scipy/issues/18010
func_RegularGridInterpolator = interpolate.RegularGridInterpolator(
(x, y), height_field_downsampled, method="cubic"
)
xx_upsampled, yy_upsampled = np.meshgrid(x_upsampled, y_upsampled, indexing="ij", sparse=True)
z_upsampled_RegularGridInterpolator = func_RegularGridInterpolator((xx_upsampled, yy_upsampled))
# check if the interpolated height field is the same as the sampled height field
np.testing.assert_allclose(z_upsampled_interp2d, z_upsampled_RectBivariant, atol=1e-14)
np.testing.assert_allclose(z_upsampled_RectBivariant, z_upsampled_RegularGridInterpolator, atol=1e-14)
np.testing.assert_allclose(z_upsampled_RegularGridInterpolator, z_upsampled_interp2d, atol=1e-14)
if __name__ == "__main__":
run_tests()
| 3,462 | Python | 44.565789 | 111 | 0.68342 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/deps/isaacsim/check_floating_base_made_fixed.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""This script demonstrates how to make a floating robot fixed in Isaac Sim."""
"""Launch Isaac Sim Simulator first."""
import argparse
from omni.isaac.kit import SimulationApp
# add argparse arguments
parser = argparse.ArgumentParser(
description="This script shows the issue in Isaac Sim with making a floating robot fixed."
)
parser.add_argument("--headless", action="store_true", help="Run in headless mode.")
parser.add_argument("--fix-base", action="store_true", help="Whether to fix the base of the robot.")
# parse the arguments
args_cli = parser.parse_args()
# launch omniverse app
simulation_app = SimulationApp({"headless": args_cli.headless})
"""Rest everything follows."""
import torch
import carb
import omni.isaac.core.utils.nucleus as nucleus_utils
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
import omni.kit.commands
import omni.physx
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.carb import set_carb_setting
from omni.isaac.core.utils.viewports import set_camera_view
from omni.isaac.core.world import World
from pxr import PhysxSchema, UsdPhysics
# check nucleus connection
if nucleus_utils.get_assets_root_path() is None:
msg = (
"Unable to perform Nucleus login on Omniverse. Assets root path is not set.\n"
"\tPlease check: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html#omniverse-nucleus"
)
carb.log_error(msg)
raise RuntimeError(msg)
ISAAC_NUCLEUS_DIR = f"{nucleus_utils.get_assets_root_path()}/Isaac"
"""Path to the `Isaac` directory on the NVIDIA Nucleus Server."""
ISAAC_ORBIT_NUCLEUS_DIR = f"{nucleus_utils.get_assets_root_path()}/Isaac/Samples/Orbit"
"""Path to the `Isaac/Samples/Orbit` directory on the NVIDIA Nucleus Server."""
"""
Main
"""
def main():
"""Spawns the ANYmal robot and makes it fixed."""
# Load kit helper
world = World(physics_dt=0.005, rendering_dt=0.005, backend="torch", device="cpu")
# Set main camera
set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0])
# Enable hydra scene-graph instancing
# this is needed to visualize the scene when flatcache is enabled
set_carb_setting(world._settings, "/persistent/omnihydra/useSceneGraphInstancing", True)
# Spawn things into stage
# Ground-plane
world.scene.add_default_ground_plane(prim_path="/World/defaultGroundPlane", z_position=0.0)
# Lights-1
prim_utils.create_prim("/World/Light/GreySphere", "SphereLight", translation=(4.5, 3.5, 10.0))
# Lights-2
prim_utils.create_prim("/World/Light/WhiteSphere", "SphereLight", translation=(-4.5, 3.5, 10.0))
# -- Robot
# resolve asset
usd_path = f"{ISAAC_ORBIT_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd"
root_prim_path = "/World/Robot/base"
# add asset
print("Loading robot from: ", usd_path)
prim_utils.create_prim(
"/World/Robot",
usd_path=usd_path,
translation=(0.0, 0.0, 0.6),
)
# create fixed joint
if args_cli.fix_base:
# get all necessary information
stage = stage_utils.get_current_stage()
root_prim = stage.GetPrimAtPath(root_prim_path)
parent_prim = root_prim.GetParent()
# here we assume that the root prim is a rigid body
# there is no clear way to deal with situation where the root prim is not a rigid body but has articulation api
# in that case, it is unclear how to get the link to the first link in the tree
if not root_prim.HasAPI(UsdPhysics.RigidBodyAPI):
raise RuntimeError("The root prim does not have the RigidBodyAPI applied.")
# create fixed joint
omni.kit.commands.execute(
"CreateJointCommand",
stage=stage,
joint_type="Fixed",
from_prim=None,
to_prim=root_prim,
)
# move the root to the parent if this is a rigid body
# having a fixed joint on a rigid body makes physx treat it as a part of the maximal coordinate tree
# if we put to joint on the parent, physx parser treats it as a fixed base articulation
# get parent prim
parent_prim = root_prim.GetParent()
# apply api to parent
UsdPhysics.ArticulationRootAPI.Apply(parent_prim)
PhysxSchema.PhysxArticulationAPI.Apply(parent_prim)
# copy the attributes
# -- usd attributes
root_usd_articulation_api = UsdPhysics.ArticulationRootAPI(root_prim)
for attr_name in root_usd_articulation_api.GetSchemaAttributeNames():
attr = root_prim.GetAttribute(attr_name)
parent_prim.GetAttribute(attr_name).Set(attr.Get())
# -- physx attributes
root_physx_articulation_api = PhysxSchema.PhysxArticulationAPI(root_prim)
for attr_name in root_physx_articulation_api.GetSchemaAttributeNames():
attr = root_prim.GetAttribute(attr_name)
parent_prim.GetAttribute(attr_name).Set(attr.Get())
# remove api from root
root_prim.RemoveAPI(UsdPhysics.ArticulationRootAPI)
root_prim.RemoveAPI(PhysxSchema.PhysxArticulationAPI)
# rename root path to parent path
root_prim_path = parent_prim.GetPath().pathString
# Setup robot
robot_view = ArticulationView(root_prim_path, name="ANYMAL")
world.scene.add(robot_view)
# Play the simulator
world.reset()
# Now we are ready!
print("[INFO]: Setup complete...")
# dummy actions
# actions = torch.zeros(robot.count, robot.num_actions, device=robot.device)
init_root_pos_w, init_root_quat_w = robot_view.get_world_poses()
# Define simulation stepping
sim_dt = world.get_physics_dt()
# episode counter
sim_time = 0.0
count = 0
# Simulate physics
while simulation_app.is_running():
# If simulation is stopped, then exit.
if world.is_stopped():
break
# If simulation is paused, then skip.
if not world.is_playing():
world.step(render=False)
continue
# do reset
if count % 20 == 0:
# reset
sim_time = 0.0
count = 0
# reset root state
root_pos_w = init_root_pos_w.clone()
root_pos_w[:, :2] += torch.rand_like(root_pos_w[:, :2]) * 0.5
robot_view.set_world_poses(root_pos_w, init_root_quat_w)
# print if it is fixed base
print("Fixed base: ", robot_view._physics_view.shared_metatype.fixed_base)
print("Moving base to: ", root_pos_w[0].cpu().numpy())
print("-" * 50)
# apply random joint actions
actions = torch.rand_like(robot_view.get_joint_positions()) * 0.001
robot_view.set_joint_efforts(actions)
# perform step
world.step()
# update sim-time
sim_time += sim_dt
count += 1
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 7,175 | Python | 34.88 | 119 | 0.657143 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/deps/isaacsim/check_camera.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script shows the issue with renderer in Isaac Sim that affects episodic resets.
The first few images of every new episode are not updated. They take multiple steps to update
and have the same image as the previous episode for the first few steps.
```
# run with cube
_isaac_sim/python.sh source/extensions/omni.isaac.orbit/test/deps/isaacsim/check_camera.py --scenario cube
# run with anymal
_isaac_sim/python.sh source/extensions/omni.isaac.orbit/test/deps/isaacsim/check_camera.py --scenario anymal
```
"""
"""Launch Isaac Sim Simulator first."""
import argparse
# omni-isaac-orbit
from omni.isaac.orbit.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(
description="This script shows the issue with renderer in Isaac Sim that affects episodic resets."
)
parser.add_argument("--gpu", action="store_true", default=False, help="Use GPU device for camera rendering output.")
parser.add_argument("--scenario", type=str, default="anymal", help="Scenario to load.", choices=["anymal", "cube"])
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
"""Rest everything follows."""
import numpy as np
import os
import random
import omni.isaac.core.utils.nucleus as nucleus_utils
import omni.isaac.core.utils.prims as prim_utils
import omni.replicator.core as rep
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.prims import GeometryPrim, RigidPrim, RigidPrimView
from omni.isaac.core.utils.carb import set_carb_setting
from omni.isaac.core.utils.viewports import set_camera_view
from omni.isaac.core.world import World
from PIL import Image, ImageChops
from pxr import Gf, UsdGeom
# check nucleus connection
if nucleus_utils.get_assets_root_path() is None:
msg = (
"Unable to perform Nucleus login on Omniverse. Assets root path is not set.\n"
"\tPlease check: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html#omniverse-nucleus"
)
raise RuntimeError(msg)
ISAAC_NUCLEUS_DIR = f"{nucleus_utils.get_assets_root_path()}/Isaac"
"""Path to the `Isaac` directory on the NVIDIA Nucleus Server."""
def main():
"""Runs a camera sensor from orbit."""
# Load kit helper
world = World(physics_dt=0.005, rendering_dt=0.005, backend="torch", device="cpu")
# Set main camera
set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0])
# Enable flatcache which avoids passing data over to USD structure
# this speeds up the read-write operation of GPU buffers
if world.get_physics_context().use_gpu_pipeline:
world.get_physics_context().enable_flatcache(True)
# Enable hydra scene-graph instancing
# this is needed to visualize the scene when flatcache is enabled
set_carb_setting(world._settings, "/persistent/omnihydra/useSceneGraphInstancing", True)
# Populate scene
# Ground
world.scene.add_default_ground_plane()
# Lights-1
prim_utils.create_prim("/World/Light/GreySphere", "SphereLight", translation=(4.5, 3.5, 10.0))
# Lights-2
prim_utils.create_prim("/World/Light/WhiteSphere", "SphereLight", translation=(-4.5, 3.5, 10.0))
# Xform to hold objects
if args_cli.scenario == "cube":
prim_utils.create_prim("/World/Objects", "Xform")
# Random objects
for i in range(8):
# sample random position
position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0])
position *= np.asarray([1.5, 1.5, 0.5])
# create prim
prim_type = random.choice(["Cube", "Sphere", "Cylinder"])
_ = prim_utils.create_prim(
f"/World/Objects/Obj_{i:02d}",
prim_type,
translation=position,
scale=(0.25, 0.25, 0.25),
semantic_label=prim_type,
)
# add rigid properties
GeometryPrim(f"/World/Objects/Obj_{i:02d}", collision=True)
rigid_obj = RigidPrim(f"/World/Objects/Obj_{i:02d}", mass=5.0)
# cast to geom prim
geom_prim = getattr(UsdGeom, prim_type)(rigid_obj.prim)
# set random color
color = Gf.Vec3f(random.random(), random.random(), random.random())
geom_prim.CreateDisplayColorAttr()
geom_prim.GetDisplayColorAttr().Set([color])
# Setup camera sensor on the world
cam_prim_path = "/World/CameraSensor"
else:
# Robot
prim_utils.create_prim(
"/World/Robot",
usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/ANYbotics/anymal_instanceable.usd",
translation=(0.0, 0.0, 0.6),
)
# Setup camera sensor on the robot
cam_prim_path = "/World/CameraSensor"
# Create camera
cam_prim = prim_utils.create_prim(
cam_prim_path,
prim_type="Camera",
translation=(5.0, 5.0, 5.0),
orientation=(0.33985113, 0.17591988, 0.42470818, 0.82047324),
)
_ = UsdGeom.Camera(cam_prim)
# Get render product
render_prod_path = rep.create.render_product(cam_prim_path, resolution=(640, 480))
# create annotator node
rep_registry = {}
for name in ["rgb", "distance_to_image_plane"]:
# create annotator
rep_annotator = rep.AnnotatorRegistry.get_annotator(name, device="cpu")
rep_annotator.attach(render_prod_path)
# add to registry
rep_registry[name] = rep_annotator
# Create replicator writer
output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output", "camera", args_cli.scenario)
os.makedirs(output_dir, exist_ok=True)
# Create a view of the stuff we want to see
if args_cli.scenario == "cube":
view: RigidPrimView = world.scene.add(RigidPrimView("/World/Objects/.*", name="my_object"))
else:
view: ArticulationView = world.scene.add(ArticulationView("/World/Robot", name="my_object"))
# Play simulator
world.reset()
# Get initial state
if args_cli.scenario == "cube":
initial_pos, initial_quat = view.get_world_poses()
initial_joint_pos = None
initial_joint_vel = None
else:
initial_pos, initial_quat = view.get_world_poses()
initial_joint_pos = view.get_joint_positions()
initial_joint_vel = view.get_joint_velocities()
# Simulate for a few steps
# note: This is a workaround to ensure that the textures are loaded.
# Check "Known Issues" section in the documentation for more details.
for _ in range(5):
world.step(render=True)
# Counter
count = 0
prev_im = None
# make episode directory
episode_count = 0
episode_dir = os.path.join(output_dir, f"episode_{episode_count:06d}")
os.makedirs(episode_dir, exist_ok=True)
# Simulate physics
while simulation_app.is_running():
# If simulation is stopped, then exit.
if world.is_stopped():
break
# If simulation is paused, then skip.
if not world.is_playing():
world.step(render=False)
continue
# Reset on intervals
if count % 25 == 0:
# reset all the state
view.set_world_poses(initial_pos, initial_quat)
if initial_joint_pos is not None:
view.set_joint_positions(initial_joint_pos)
if initial_joint_vel is not None:
view.set_joint_velocities(initial_joint_vel)
# make a new episode directory
episode_dir = os.path.join(output_dir, f"episode_{episode_count:06d}")
os.makedirs(episode_dir, exist_ok=True)
# reset counters
count = 0
episode_count += 1
# Step simulation
for _ in range(15):
world.step(render=False)
world.render()
# Update camera data
rgb_data = rep_registry["rgb"].get_data()
depth_data = rep_registry["distance_to_image_plane"].get_data()
# Show current image number
print(f"[Epi {episode_count:03d}] Current image number: {count:06d}")
# Save data
curr_im = Image.fromarray(rgb_data)
curr_im.save(os.path.join(episode_dir, f"{count:06d}_rgb.png"))
# Save diff
if prev_im is not None:
diff_im = ImageChops.difference(curr_im, prev_im)
# convert to grayscale and threshold
diff_im = diff_im.convert("L")
threshold = 30
diff_im = diff_im.point(lambda p: p > threshold and 255)
# Save all of them together
dst_im = Image.new("RGB", (curr_im.width + prev_im.width + diff_im.width, diff_im.height))
dst_im.paste(prev_im, (0, 0))
dst_im.paste(curr_im, (prev_im.width, 0))
dst_im.paste(diff_im, (2 * prev_im.width, 0))
dst_im.save(os.path.join(episode_dir, f"{count:06d}_diff.png"))
# Save to previous
prev_im = curr_im.copy()
# Update counter
count += 1
# Print camera info
print("Received shape of rgb image: ", rgb_data.shape)
print("Received shape of depth image: ", depth_data.shape)
print("-------------------------------")
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 9,573 | Python | 37.143426 | 117 | 0.633657 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/deps/isaacsim/check_legged_robot_clone.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script demonstrates how to use the cloner API from Isaac Sim.
Reference: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/tutorial_gym_cloner.html
"""
"""Launch Isaac Sim Simulator first."""
import argparse
from omni.isaac.orbit.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(
description="This script shows the issue in Isaac Sim with GPU simulation of floating robots."
)
parser.add_argument("--num_robots", type=int, default=128, help="Number of robots to spawn.")
parser.add_argument(
"--asset",
type=str,
default="orbit",
help="The asset source location for the robot. Can be: orbit, oige, custom asset path.",
)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
"""Rest everything follows."""
import os
import torch
import carb
import omni.isaac.core.utils.nucleus as nucleus_utils
import omni.isaac.core.utils.prims as prim_utils
from omni.isaac.cloner import GridCloner
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.carb import set_carb_setting
from omni.isaac.core.utils.viewports import set_camera_view
from omni.isaac.core.world import World
# check nucleus connection
if nucleus_utils.get_assets_root_path() is None:
msg = (
"Unable to perform Nucleus login on Omniverse. Assets root path is not set.\n"
"\tPlease check: https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html#omniverse-nucleus"
)
carb.log_error(msg)
raise RuntimeError(msg)
ISAAC_NUCLEUS_DIR = f"{nucleus_utils.get_assets_root_path()}/Isaac"
"""Path to the `Isaac` directory on the NVIDIA Nucleus Server."""
ISAAC_ORBIT_NUCLEUS_DIR = f"{nucleus_utils.get_assets_root_path()}/Isaac/Samples/Orbit"
"""Path to the `Isaac/Samples/Orbit` directory on the NVIDIA Nucleus Server."""
"""
Main
"""
def main():
"""Spawns the ANYmal robot and clones it using Isaac Sim Cloner API."""
# Load kit helper
world = World(physics_dt=0.005, rendering_dt=0.005, backend="torch", device="cuda:0")
# Set main camera
set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0])
# Enable hydra scene-graph instancing
# this is needed to visualize the scene when flatcache is enabled
set_carb_setting(world._settings, "/persistent/omnihydra/useSceneGraphInstancing", True)
# Create interface to clone the scene
cloner = GridCloner(spacing=2.0)
cloner.define_base_env("/World/envs")
# Everything under the namespace "/World/envs/env_0" will be cloned
prim_utils.define_prim("/World/envs/env_0")
# Spawn things into stage
# Ground-plane
world.scene.add_default_ground_plane(prim_path="/World/defaultGroundPlane", z_position=0.0)
# Lights-1
prim_utils.create_prim("/World/Light/GreySphere", "SphereLight", translation=(4.5, 3.5, 10.0))
# Lights-2
prim_utils.create_prim("/World/Light/WhiteSphere", "SphereLight", translation=(-4.5, 3.5, 10.0))
# -- Robot
# resolve asset
if args_cli.asset == "orbit":
usd_path = f"{ISAAC_ORBIT_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd"
root_prim_path = "/World/envs/env_.*/Robot/base"
elif args_cli.asset == "oige":
usd_path = f"{ISAAC_NUCLEUS_DIR}/Robots/ANYbotics/anymal_instanceable.usd"
root_prim_path = "/World/envs/env_.*/Robot"
elif os.path.exists(args_cli.asset):
usd_path = args_cli.asset
else:
raise ValueError(f"Invalid asset: {args_cli.asset}. Must be one of: orbit, oige.")
# add asset
print("Loading robot from: ", usd_path)
prim_utils.create_prim(
"/World/envs/env_0/Robot",
usd_path=usd_path,
translation=(0.0, 0.0, 0.6),
)
# Clone the scene
num_envs = args_cli.num_robots
cloner.define_base_env("/World/envs")
envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_envs)
envs_positions = cloner.clone(
source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True
)
# convert environment positions to torch tensor
envs_positions = torch.tensor(envs_positions, dtype=torch.float, device=world.device)
# filter collisions within each environment instance
physics_scene_path = world.get_physics_context().prim_path
cloner.filter_collisions(
physics_scene_path, "/World/collisions", envs_prim_paths, global_paths=["/World/defaultGroundPlane"]
)
# Resolve robot prim paths
if args_cli.asset == "orbit":
root_prim_path = "/World/envs/env_.*/Robot/base"
elif args_cli.asset == "oige":
root_prim_path = "/World/envs/env_.*/Robot"
elif os.path.exists(args_cli.asset):
usd_path = args_cli.asset
root_prim_path = "/World/envs/env_.*/Robot"
else:
raise ValueError(f"Invalid asset: {args_cli.asset}. Must be one of: orbit, oige.")
# Setup robot
robot_view = ArticulationView(root_prim_path, name="ANYMAL")
world.scene.add(robot_view)
# Play the simulator
world.reset()
# Now we are ready!
print("[INFO]: Setup complete...")
# dummy actions
# actions = torch.zeros(robot.count, robot.num_actions, device=robot.device)
# Define simulation stepping
sim_dt = world.get_physics_dt()
# episode counter
sim_time = 0.0
# Simulate physics
while simulation_app.is_running():
# If simulation is stopped, then exit.
if world.is_stopped():
break
# If simulation is paused, then skip.
if not world.is_playing():
world.step(render=False)
continue
# perform step
world.step()
# update sim-time
sim_time += sim_dt
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 6,106 | Python | 32.740331 | 117 | 0.677203 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/deps/isaacsim/check_rep_texture_randomizer.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script shows how to use replicator to randomly change the textures of a USD scene.
Note:
Currently this script fails since cloner does not support changing textures of cloned
USD prims. This is because the prims are cloned using `Sdf.ChangeBlock` which does not
allow individual texture changes.
Usage:
.. code-block:: bash
./orbit.sh -p source/extensions/omni.isaac.orbit/test/deps/isaacsim/check_rep_texture_randomizer.py
"""
"""Launch Isaac Sim Simulator first."""
import argparse
# omni-isaac-orbit
from omni.isaac.orbit.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(
description="This script shows how to use replicator to randomly change the textures of a USD scene."
)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
"""Rest everything follows."""
import numpy as np
import torch
import omni.isaac.core.utils.prims as prim_utils
import omni.replicator.core as rep
from omni.isaac.cloner import GridCloner
from omni.isaac.core.objects import DynamicSphere
from omni.isaac.core.prims import RigidPrimView
from omni.isaac.core.simulation_context import SimulationContext
from omni.isaac.core.utils.viewports import set_camera_view
def main():
"""Spawn a bunch of balls and randomly change their textures."""
# Load kit helper
sim_params = {
"use_gpu": True,
"use_gpu_pipeline": True,
"use_flatcache": True, # deprecated from Isaac Sim 2023.1 onwards
"use_fabric": True, # used from Isaac Sim 2023.1 onwards
"enable_scene_query_support": True,
}
sim = SimulationContext(
physics_dt=1.0 / 60.0, rendering_dt=1.0 / 60.0, sim_params=sim_params, backend="torch", device="cuda:0"
)
# Set main camera
set_camera_view([0.0, 30.0, 25.0], [0.0, 0.0, -2.5])
# Parameters
num_balls = 128
# Create interface to clone the scene
cloner = GridCloner(spacing=2.0)
cloner.define_base_env("/World/envs")
# Everything under the namespace "/World/envs/env_0" will be cloned
prim_utils.define_prim("/World/envs/env_0")
# Define the scene
# -- Ball
DynamicSphere(prim_path="/World/envs/env_0/ball", translation=np.array([0.0, 0.0, 5.0]), mass=0.5, radius=0.25)
# Clone the scene
cloner.define_base_env("/World/envs")
envs_prim_paths = cloner.generate_paths("/World/envs/env", num_paths=num_balls)
env_positions = cloner.clone(
source_prim_path="/World/envs/env_0", prim_paths=envs_prim_paths, replicate_physics=True, copy_from_source=True
)
physics_scene_path = sim.get_physics_context().prim_path
cloner.filter_collisions(
physics_scene_path, "/World/collisions", prim_paths=envs_prim_paths, global_paths=["/World/ground"]
)
# Use replicator to randomize color on the spheres
with rep.new_layer():
# Define a function to get all the shapes
def get_shapes():
shapes = rep.get.prims(path_pattern="/World/envs/env_.*/ball")
with shapes:
rep.randomizer.color(colors=rep.distribution.uniform((0, 0, 0), (1, 1, 1)))
return shapes.node
# Register the function
rep.randomizer.register(get_shapes)
# Specify the frequency of randomization
with rep.trigger.on_frame():
rep.randomizer.get_shapes()
# Set ball positions over terrain origins
# Create a view over all the balls
ball_view = RigidPrimView("/World/envs/env_.*/ball", reset_xform_properties=False)
# cache initial state of the balls
ball_initial_positions = torch.tensor(env_positions, dtype=torch.float, device=sim.device)
ball_initial_positions[:, 2] += 5.0
# set initial poses
# note: setting here writes to USD :)
ball_view.set_world_poses(positions=ball_initial_positions)
# Play simulator
sim.reset()
# Step replicator to randomize colors
rep.orchestrator.step(pause_timeline=False)
# Stop replicator to prevent further randomization
rep.orchestrator.stop()
# Pause simulator at the beginning for inspection
sim.pause()
# Initialize the ball views for physics simulation
ball_view.initialize()
ball_initial_velocities = ball_view.get_velocities()
# Create a counter for resetting the scene
step_count = 0
# Simulate physics
while simulation_app.is_running():
# If simulation is stopped, then exit.
if sim.is_stopped():
break
# If simulation is paused, then skip.
if not sim.is_playing():
sim.step()
continue
# Reset the scene
if step_count % 500 == 0:
# reset the balls
ball_view.set_world_poses(positions=ball_initial_positions)
ball_view.set_velocities(ball_initial_velocities)
# reset the counter
step_count = 0
# Step simulation
sim.step()
# Update counter
step_count += 1
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 5,376 | Python | 31.587879 | 119 | 0.670759 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/deps/isaacsim/check_app.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script shows the issue with launching Isaac Sim application in headless mode.
On launching the application in headless mode, the application does not exit gracefully.
There are multiple warnings and errors that are printed on the console.
```
_isaac_sim/python.sh source/extensions/omni.isaac.orbit/test/deps/isaacsim/check_app.py
```
Output:
```
[10.948s] Simulation App Startup Complete
[11.471s] Simulation App Shutting Down
...... [Warning] [carb] [Plugin: omni.spectree.delegate.plugin] Module /media/vulcan/packman-repo/chk/kit-sdk/105.1+release.129498.98d86eae.tc.linux-x86_64.release/exts/omni.usd_resolver/bin/libomni.spectree.delegate.plugin.so remained loaded after unload request
...... [Warning] [omni.core.ITypeFactory] Module /media/vulcan/packman-repo/chk/kit-sdk/105.1+release.129498.98d86eae.tc.linux-x86_64.release/exts/omni.graph.action/bin/libomni.graph.action.plugin.so remained loaded after unload request.
...... [Warning] [omni.core.ITypeFactory] Module /media/vulcan/packman-repo/chk/kit-sdk/105.1+release.129498.98d86eae.tc.linux-x86_64.release/exts/omni.activity.core/bin/libomni.activity.core.plugin.so remained loaded after unload request.
```
"""
from omni.isaac.kit import SimulationApp
if __name__ == "__main__":
app = SimulationApp({"headless": True})
app.close()
| 1,445 | Python | 42.818181 | 263 | 0.764014 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/controllers/test_differential_ik.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import torch
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
from omni.isaac.cloner import GridCloner
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.assets import Articulation
from omni.isaac.orbit.controllers import DifferentialIKController, DifferentialIKControllerCfg
from omni.isaac.orbit.utils.math import compute_pose_error, subtract_frame_transforms
##
# Pre-defined configs
##
from omni.isaac.orbit_assets import FRANKA_PANDA_HIGH_PD_CFG, UR10_CFG # isort:skip
class TestDifferentialIKController(unittest.TestCase):
"""Test fixture for checking that differential IK controller tracks commands properly."""
def setUp(self):
"""Create a blank new stage for each test."""
# Wait for spawning
stage_utils.create_new_stage()
# Constants
self.num_envs = 128
# Load kit helper
sim_cfg = sim_utils.SimulationCfg(dt=0.01)
self.sim = sim_utils.SimulationContext(sim_cfg)
# TODO: Remove this once we have a better way to handle this.
self.sim._app_control_on_stop_handle = None
# Create a ground plane
cfg = sim_utils.GroundPlaneCfg()
cfg.func("/World/GroundPlane", cfg)
# Create interface to clone the scene
cloner = GridCloner(spacing=2.0)
cloner.define_base_env("/World/envs")
self.env_prim_paths = cloner.generate_paths("/World/envs/env", self.num_envs)
# create source prim
prim_utils.define_prim(self.env_prim_paths[0], "Xform")
# clone the env xform
self.env_origins = cloner.clone(
source_prim_path=self.env_prim_paths[0],
prim_paths=self.env_prim_paths,
replicate_physics=True,
)
# Define goals for the arm
ee_goals_set = [
[0.5, 0.5, 0.7, 0.707, 0, 0.707, 0],
[0.5, -0.4, 0.6, 0.707, 0.707, 0.0, 0.0],
[0.5, 0, 0.5, 0.0, 1.0, 0.0, 0.0],
]
self.ee_pose_b_des_set = torch.tensor(ee_goals_set, device=self.sim.device)
def tearDown(self):
"""Stops simulator after each test."""
# stop simulation
self.sim.stop()
self.sim.clear()
self.sim.clear_all_callbacks()
self.sim.clear_instance()
"""
Test fixtures.
"""
def test_franka_ik_pose_abs(self):
"""Test IK controller for Franka arm with Franka hand."""
# Create robot instance
robot_cfg = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="/World/envs/env_.*/Robot")
robot = Articulation(cfg=robot_cfg)
# Create IK controller
diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls")
diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=self.num_envs, device=self.sim.device)
# Run the controller and check that it converges to the goal
self._run_ik_controller(robot, diff_ik_controller, "panda_hand", ["panda_joint.*"])
def test_ur10_ik_pose_abs(self):
"""Test IK controller for UR10 arm."""
# Create robot instance
robot_cfg = UR10_CFG.replace(prim_path="/World/envs/env_.*/Robot")
robot_cfg.spawn.rigid_props.disable_gravity = True
robot = Articulation(cfg=robot_cfg)
# Create IK controller
diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls")
diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=self.num_envs, device=self.sim.device)
# Run the controller and check that it converges to the goal
self._run_ik_controller(robot, diff_ik_controller, "ee_link", [".*"])
"""
Helper functions.
"""
def _run_ik_controller(
self,
robot: Articulation,
diff_ik_controller: DifferentialIKController,
ee_frame_name: str,
arm_joint_names: list[str],
):
# Define simulation stepping
sim_dt = self.sim.get_physics_dt()
# Play the simulator
self.sim.reset()
# Obtain the frame index of the end-effector
ee_frame_idx = robot.find_bodies(ee_frame_name)[0][0]
ee_jacobi_idx = ee_frame_idx - 1
# Obtain joint indices
arm_joint_ids = robot.find_joints(arm_joint_names)[0]
# Update existing buffers
# Note: We need to update buffers before the first step for the controller.
robot.update(dt=sim_dt)
# Track the given command
current_goal_idx = 0
# Current goal for the arm
ee_pose_b_des = torch.zeros(self.num_envs, diff_ik_controller.action_dim, device=self.sim.device)
ee_pose_b_des[:] = self.ee_pose_b_des_set[current_goal_idx]
# Compute current pose of the end-effector
ee_pose_w = robot.data.body_state_w[:, ee_frame_idx, 0:7]
root_pose_w = robot.data.root_state_w[:, 0:7]
ee_pos_b, ee_quat_b = subtract_frame_transforms(
root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7]
)
# Now we are ready!
for count in range(1500):
# reset every 150 steps
if count % 250 == 0:
# check that we converged to the goal
if count > 0:
pos_error, rot_error = compute_pose_error(
ee_pos_b, ee_quat_b, ee_pose_b_des[:, 0:3], ee_pose_b_des[:, 3:7]
)
pos_error_norm = torch.norm(pos_error, dim=-1)
rot_error_norm = torch.norm(rot_error, dim=-1)
# desired error (zer)
des_error = torch.zeros_like(pos_error_norm)
# check convergence
torch.testing.assert_close(pos_error_norm, des_error, rtol=0.0, atol=1e-3)
torch.testing.assert_close(rot_error_norm, des_error, rtol=0.0, atol=1e-3)
# reset joint state
joint_pos = robot.data.default_joint_pos.clone()
joint_vel = robot.data.default_joint_vel.clone()
# joint_pos *= sample_uniform(0.9, 1.1, joint_pos.shape, joint_pos.device)
robot.write_joint_state_to_sim(joint_pos, joint_vel)
robot.set_joint_position_target(joint_pos)
robot.write_data_to_sim()
robot.reset()
# reset actions
ee_pose_b_des[:] = self.ee_pose_b_des_set[current_goal_idx]
joint_pos_des = joint_pos[:, arm_joint_ids].clone()
# update goal for next iteration
current_goal_idx = (current_goal_idx + 1) % len(self.ee_pose_b_des_set)
# set the controller commands
diff_ik_controller.reset()
diff_ik_controller.set_command(ee_pose_b_des)
else:
# at reset, the jacobians are not updated to the latest state
# so we MUST skip the first step
# obtain quantities from simulation
jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, arm_joint_ids]
ee_pose_w = robot.data.body_state_w[:, ee_frame_idx, 0:7]
root_pose_w = robot.data.root_state_w[:, 0:7]
joint_pos = robot.data.joint_pos[:, arm_joint_ids]
# compute frame in root frame
ee_pos_b, ee_quat_b = subtract_frame_transforms(
root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7]
)
# compute the joint commands
joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos)
# apply actions
robot.set_joint_position_target(joint_pos_des, arm_joint_ids)
robot.write_data_to_sim()
# perform step
self.sim.step(render=False)
# update buffers
robot.update(sim_dt)
if __name__ == "__main__":
run_tests()
| 8,427 | Python | 39.714976 | 114 | 0.5925 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_spawn_from_files.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
from omni.isaac.orbit.app import AppLauncher, run_tests
"""Launch Isaac Sim Simulator first."""
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
from omni.isaac.core.simulation_context import SimulationContext
from omni.isaac.core.utils.extensions import enable_extension, get_extension_path_from_name
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.utils.assets import ISAAC_ORBIT_NUCLEUS_DIR
class TestSpawningFromFiles(unittest.TestCase):
"""Test fixture for checking spawning of USD references from files with different settings."""
def setUp(self) -> None:
"""Create a blank new stage for each test."""
# Create a new stage
stage_utils.create_new_stage()
# Simulation time-step
self.dt = 0.1
# Load kit helper
self.sim = SimulationContext(physics_dt=self.dt, rendering_dt=self.dt, backend="numpy")
# Wait for spawning
stage_utils.update_stage()
def tearDown(self) -> None:
"""Stops simulator after each test."""
# stop simulation
self.sim.stop()
self.sim.clear()
self.sim.clear_all_callbacks()
self.sim.clear_instance()
"""
Basic spawning.
"""
def test_spawn_usd(self):
"""Test loading prim from Usd file."""
# Spawn cone
cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_ORBIT_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd")
prim = cfg.func("/World/Franka", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Franka"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Xform")
def test_spawn_usd_fails(self):
"""Test loading prim from Usd file fails when asset usd path is invalid."""
# Spawn cone
cfg = sim_utils.UsdFileCfg(usd_path=f"{ISAAC_ORBIT_NUCLEUS_DIR}/Robots/FrankaEmika/panda2_instanceable.usd")
with self.assertRaises(FileNotFoundError):
cfg.func("/World/Franka", cfg)
def test_spawn_urdf(self):
"""Test loading prim from URDF file."""
# retrieve path to urdf importer extension
enable_extension("omni.importer.urdf")
extension_path = get_extension_path_from_name("omni.importer.urdf")
# Spawn franka from URDF
cfg = sim_utils.UrdfFileCfg(
asset_path=f"{extension_path}/data/urdf/robots/franka_description/robots/panda_arm_hand.urdf", fix_base=True
)
prim = cfg.func("/World/Franka", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Franka"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Xform")
def test_spawn_ground_plane(self):
"""Test loading prim for the ground plane from grid world USD."""
# Spawn ground plane
cfg = sim_utils.GroundPlaneCfg(color=(0.1, 0.1, 0.1), size=(10.0, 10.0))
prim = cfg.func("/World/ground_plane", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/ground_plane"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Xform")
if __name__ == "__main__":
run_tests()
| 3,580 | Python | 35.540816 | 120 | 0.660615 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_urdf_converter.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
config = {"headless": True}
simulation_app = AppLauncher(config).app
"""Rest everything follows."""
import math
import numpy as np
import os
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.simulation_context import SimulationContext
from omni.isaac.core.utils.extensions import enable_extension, get_extension_path_from_name
from omni.isaac.orbit.sim.converters import UrdfConverter, UrdfConverterCfg
class TestUrdfConverter(unittest.TestCase):
"""Test fixture for the UrdfConverter class."""
def setUp(self):
"""Create a blank new stage for each test."""
# Create a new stage
stage_utils.create_new_stage()
# retrieve path to urdf importer extension
enable_extension("omni.importer.urdf")
extension_path = get_extension_path_from_name("omni.importer.urdf")
# default configuration
self.config = UrdfConverterCfg(
asset_path=f"{extension_path}/data/urdf/robots/franka_description/robots/panda_arm_hand.urdf", fix_base=True
)
# Simulation time-step
self.dt = 0.01
# Load kit helper
self.sim = SimulationContext(physics_dt=self.dt, rendering_dt=self.dt, backend="numpy")
def tearDown(self) -> None:
"""Stops simulator after each test."""
# stop simulation
self.sim.stop()
# cleanup stage and context
self.sim.clear()
self.sim.clear_all_callbacks()
self.sim.clear_instance()
def test_no_change(self):
"""Call conversion twice. This should not generate a new USD file."""
urdf_converter = UrdfConverter(self.config)
time_usd_file_created = os.stat(urdf_converter.usd_path).st_mtime_ns
# no change to config only define the usd directory
new_config = self.config
new_config.usd_dir = urdf_converter.usd_dir
# convert to usd but this time in the same directory as previous step
new_urdf_converter = UrdfConverter(new_config)
new_time_usd_file_created = os.stat(new_urdf_converter.usd_path).st_mtime_ns
self.assertEqual(time_usd_file_created, new_time_usd_file_created)
def test_config_change(self):
"""Call conversion twice but change the config in the second call. This should generate a new USD file."""
urdf_converter = UrdfConverter(self.config)
time_usd_file_created = os.stat(urdf_converter.usd_path).st_mtime_ns
# change the config
new_config = self.config
new_config.fix_base = not self.config.fix_base
# define the usd directory
new_config.usd_dir = urdf_converter.usd_dir
# convert to usd but this time in the same directory as previous step
new_urdf_converter = UrdfConverter(new_config)
new_time_usd_file_created = os.stat(new_urdf_converter.usd_path).st_mtime_ns
self.assertNotEqual(time_usd_file_created, new_time_usd_file_created)
def test_create_prim_from_usd(self):
"""Call conversion and create a prim from it."""
urdf_converter = UrdfConverter(self.config)
prim_path = "/World/Robot"
prim_utils.create_prim(prim_path, usd_path=urdf_converter.usd_path)
self.assertTrue(prim_utils.is_prim_path_valid(prim_path))
def test_config_drive_type(self):
"""Change the drive mechanism of the robot to be position."""
# Create directory to dump results
test_dir = os.path.dirname(os.path.abspath(__file__))
output_dir = os.path.join(test_dir, "output", "urdf_converter")
if not os.path.exists(output_dir):
os.makedirs(output_dir, exist_ok=True)
# change the config
self.config.default_drive_type = "position"
self.config.default_drive_stiffness = 400.0
self.config.default_drive_damping = 40.0
self.config.usd_dir = output_dir
urdf_converter = UrdfConverter(self.config)
# check the drive type of the robot
prim_path = "/World/Robot"
prim_utils.create_prim(prim_path, usd_path=urdf_converter.usd_path)
# access the robot
robot = ArticulationView(prim_path, reset_xform_properties=False)
# play the simulator and initialize the robot
self.sim.reset()
robot.initialize()
# check drive values for the robot (read from physx)
drive_stiffness, drive_damping = robot.get_gains()
# -- for the arm (revolute joints)
# user provides the values in radians but simulator sets them as in degrees
expected_drive_stiffness = math.degrees(self.config.default_drive_stiffness)
expected_drive_damping = math.degrees(self.config.default_drive_damping)
np.testing.assert_array_equal(drive_stiffness[:, :7], expected_drive_stiffness)
np.testing.assert_array_equal(drive_damping[:, :7], expected_drive_damping)
# -- for the hand (prismatic joints)
# note: from isaac sim 2023.1, the test asset has mimic joints for the hand
# so the mimic joint doesn't have drive values
expected_drive_stiffness = self.config.default_drive_stiffness
expected_drive_damping = self.config.default_drive_damping
np.testing.assert_array_equal(drive_stiffness[:, 7], expected_drive_stiffness)
np.testing.assert_array_equal(drive_damping[:, 7], expected_drive_damping)
# check drive values for the robot (read from usd)
self.sim.stop()
drive_stiffness, drive_damping = robot.get_gains()
# -- for the arm (revolute joints)
# user provides the values in radians but simulator sets them as in degrees
expected_drive_stiffness = math.degrees(self.config.default_drive_stiffness)
expected_drive_damping = math.degrees(self.config.default_drive_damping)
np.testing.assert_array_equal(drive_stiffness[:, :7], expected_drive_stiffness)
np.testing.assert_array_equal(drive_damping[:, :7], expected_drive_damping)
# -- for the hand (prismatic joints)
# note: from isaac sim 2023.1, the test asset has mimic joints for the hand
# so the mimic joint doesn't have drive values
expected_drive_stiffness = self.config.default_drive_stiffness
expected_drive_damping = self.config.default_drive_damping
np.testing.assert_array_equal(drive_stiffness[:, 7], expected_drive_stiffness)
np.testing.assert_array_equal(drive_damping[:, 7], expected_drive_damping)
if __name__ == "__main__":
run_tests()
| 6,890 | Python | 41.801242 | 120 | 0.677358 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_schemas.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
from omni.isaac.core.simulation_context import SimulationContext
from pxr import UsdPhysics
import omni.isaac.orbit.sim.schemas as schemas
from omni.isaac.orbit.sim.utils import find_global_fixed_joint_prim
from omni.isaac.orbit.utils.assets import ISAAC_NUCLEUS_DIR
from omni.isaac.orbit.utils.string import to_camel_case
class TestPhysicsSchema(unittest.TestCase):
"""Test fixture for checking schemas modifications through Orbit."""
def setUp(self) -> None:
"""Create a blank new stage for each test."""
# Create a new stage
stage_utils.create_new_stage()
# Simulation time-step
self.dt = 0.1
# Load kit helper
self.sim = SimulationContext(physics_dt=self.dt, rendering_dt=self.dt, backend="numpy")
# Set some default values for test
self.arti_cfg = schemas.ArticulationRootPropertiesCfg(
enabled_self_collisions=False,
articulation_enabled=True,
solver_position_iteration_count=4,
solver_velocity_iteration_count=1,
sleep_threshold=1.0,
stabilization_threshold=5.0,
fix_root_link=False,
)
self.rigid_cfg = schemas.RigidBodyPropertiesCfg(
rigid_body_enabled=True,
kinematic_enabled=False,
disable_gravity=False,
linear_damping=0.1,
angular_damping=0.5,
max_linear_velocity=1000.0,
max_angular_velocity=1000.0,
max_depenetration_velocity=10.0,
max_contact_impulse=10.0,
enable_gyroscopic_forces=True,
retain_accelerations=True,
solver_position_iteration_count=8,
solver_velocity_iteration_count=1,
sleep_threshold=1.0,
stabilization_threshold=6.0,
)
self.collision_cfg = schemas.CollisionPropertiesCfg(
collision_enabled=True,
contact_offset=0.05,
rest_offset=0.001,
min_torsional_patch_radius=0.1,
torsional_patch_radius=1.0,
)
self.mass_cfg = schemas.MassPropertiesCfg(mass=1.0, density=100.0)
self.joint_cfg = schemas.JointDrivePropertiesCfg(drive_type="acceleration")
def tearDown(self) -> None:
"""Stops simulator after each test."""
# stop simulation
self.sim.stop()
self.sim.clear()
self.sim.clear_all_callbacks()
self.sim.clear_instance()
def test_valid_properties_cfg(self):
"""Test that all the config instances have non-None values.
This is to ensure that we check that all the properties of the schema are set.
"""
for cfg in [self.arti_cfg, self.rigid_cfg, self.collision_cfg, self.mass_cfg, self.joint_cfg]:
# check nothing is none
for k, v in cfg.__dict__.items():
self.assertIsNotNone(v, f"{cfg.__class__.__name__}:{k} is None. Please make sure schemas are valid.")
def test_modify_properties_on_invalid_prim(self):
"""Test modifying properties on a prim that does not exist."""
# set properties
with self.assertRaises(ValueError):
schemas.modify_rigid_body_properties("/World/asset_xyz", self.rigid_cfg)
def test_modify_properties_on_articulation_instanced_usd(self):
"""Test modifying properties on articulation instanced usd.
In this case, modifying collision properties on the articulation instanced usd will fail.
"""
# spawn asset to the stage
asset_usd_file = f"{ISAAC_NUCLEUS_DIR}/Robots/ANYbotics/anymal_instanceable.usd"
prim_utils.create_prim("/World/asset_instanced", usd_path=asset_usd_file, translation=(0.0, 0.0, 0.62))
# set properties on the asset and check all properties are set
schemas.modify_articulation_root_properties("/World/asset_instanced", self.arti_cfg)
schemas.modify_rigid_body_properties("/World/asset_instanced", self.rigid_cfg)
schemas.modify_mass_properties("/World/asset_instanced", self.mass_cfg)
schemas.modify_joint_drive_properties("/World/asset_instanced", self.joint_cfg)
# validate the properties
self._validate_articulation_properties_on_prim("/World/asset_instanced", has_default_fixed_root=False)
self._validate_rigid_body_properties_on_prim("/World/asset_instanced")
self._validate_mass_properties_on_prim("/World/asset_instanced")
self._validate_joint_drive_properties_on_prim("/World/asset_instanced")
# make a fixed joint
# note: for this asset, it doesn't work because the root is not a rigid body
self.arti_cfg.fix_root_link = True
with self.assertRaises(NotImplementedError):
schemas.modify_articulation_root_properties("/World/asset_instanced", self.arti_cfg)
def test_modify_properties_on_articulation_usd(self):
"""Test setting properties on articulation usd."""
# spawn asset to the stage
asset_usd_file = f"{ISAAC_NUCLEUS_DIR}/Robots/Franka/franka.usd"
prim_utils.create_prim("/World/asset", usd_path=asset_usd_file, translation=(0.0, 0.0, 0.62))
# set properties on the asset and check all properties are set
schemas.modify_articulation_root_properties("/World/asset", self.arti_cfg)
schemas.modify_rigid_body_properties("/World/asset", self.rigid_cfg)
schemas.modify_collision_properties("/World/asset", self.collision_cfg)
schemas.modify_mass_properties("/World/asset", self.mass_cfg)
schemas.modify_joint_drive_properties("/World/asset", self.joint_cfg)
# validate the properties
self._validate_articulation_properties_on_prim("/World/asset", has_default_fixed_root=True)
self._validate_rigid_body_properties_on_prim("/World/asset")
self._validate_collision_properties_on_prim("/World/asset")
self._validate_mass_properties_on_prim("/World/asset")
self._validate_joint_drive_properties_on_prim("/World/asset")
# make a fixed joint
self.arti_cfg.fix_root_link = True
schemas.modify_articulation_root_properties("/World/asset", self.arti_cfg)
# validate the properties
self._validate_articulation_properties_on_prim("/World/asset", has_default_fixed_root=True)
def test_defining_rigid_body_properties_on_prim(self):
"""Test defining rigid body properties on a prim."""
# create a prim
prim_utils.create_prim("/World/parent", prim_type="XForm")
# spawn a prim
prim_utils.create_prim("/World/cube1", prim_type="Cube", translation=(0.0, 0.0, 0.62))
# set properties on the asset and check all properties are set
schemas.define_rigid_body_properties("/World/cube1", self.rigid_cfg)
schemas.define_collision_properties("/World/cube1", self.collision_cfg)
schemas.define_mass_properties("/World/cube1", self.mass_cfg)
# validate the properties
self._validate_rigid_body_properties_on_prim("/World/cube1")
self._validate_collision_properties_on_prim("/World/cube1")
self._validate_mass_properties_on_prim("/World/cube1")
# spawn another prim
prim_utils.create_prim("/World/cube2", prim_type="Cube", translation=(1.0, 1.0, 0.62))
# set properties on the asset and check all properties are set
schemas.define_rigid_body_properties("/World/cube2", self.rigid_cfg)
schemas.define_collision_properties("/World/cube2", self.collision_cfg)
# validate the properties
self._validate_rigid_body_properties_on_prim("/World/cube2")
self._validate_collision_properties_on_prim("/World/cube2")
# check if we can play
self.sim.reset()
for _ in range(100):
self.sim.step()
def test_defining_articulation_properties_on_prim(self):
"""Test defining articulation properties on a prim."""
# create a parent articulation
prim_utils.create_prim("/World/parent", prim_type="Xform")
schemas.define_articulation_root_properties("/World/parent", self.arti_cfg)
# validate the properties
self._validate_articulation_properties_on_prim("/World/parent", has_default_fixed_root=False)
# create a child articulation
prim_utils.create_prim("/World/parent/child", prim_type="Cube", translation=(0.0, 0.0, 0.62))
schemas.define_rigid_body_properties("/World/parent/child", self.rigid_cfg)
schemas.define_mass_properties("/World/parent/child", self.mass_cfg)
# check if we can play
self.sim.reset()
for _ in range(100):
self.sim.step()
"""
Helper functions.
"""
def _validate_articulation_properties_on_prim(
self, prim_path: str, has_default_fixed_root: False, verbose: bool = False
):
"""Validate the articulation properties on the prim.
If :attr:`has_default_fixed_root` is True, then the asset already has a fixed root link. This is used to check the
expected behavior of the fixed root link configuration.
"""
# the root prim
root_prim = prim_utils.get_prim_at_path(prim_path)
# check articulation properties are set correctly
for attr_name, attr_value in self.arti_cfg.__dict__.items():
# skip names we know are not present
if attr_name == "func":
continue
# handle fixed root link
if attr_name == "fix_root_link" and attr_value is not None:
# obtain the fixed joint prim
fixed_joint_prim = find_global_fixed_joint_prim(prim_path)
# if asset does not have a fixed root link then check if the joint is created
if not has_default_fixed_root:
if attr_value:
self.assertIsNotNone(fixed_joint_prim)
else:
self.assertIsNone(fixed_joint_prim)
else:
# check a joint exists
self.assertIsNotNone(fixed_joint_prim)
# check if the joint is enabled or disabled
is_enabled = fixed_joint_prim.GetJointEnabledAttr().Get()
self.assertEqual(is_enabled, attr_value)
# skip the rest of the checks
continue
# convert attribute name in prim to cfg name
prim_prop_name = f"physxArticulation:{to_camel_case(attr_name, to='cC')}"
# validate the values
self.assertAlmostEqual(
root_prim.GetAttribute(prim_prop_name).Get(),
attr_value,
places=5,
msg=f"Failed setting for {prim_prop_name}",
)
def _validate_rigid_body_properties_on_prim(self, prim_path: str, verbose: bool = False):
"""Validate the rigid body properties on the prim.
Note:
Right now this function exploits the hierarchy in the asset to check the properties. This is not a
fool-proof way of checking the properties.
"""
# the root prim
root_prim = prim_utils.get_prim_at_path(prim_path)
# check rigid body properties are set correctly
for link_prim in root_prim.GetChildren():
if UsdPhysics.RigidBodyAPI(link_prim):
for attr_name, attr_value in self.rigid_cfg.__dict__.items():
# skip names we know are not present
if attr_name in ["func", "rigid_body_enabled", "kinematic_enabled"]:
continue
# convert attribute name in prim to cfg name
prim_prop_name = f"physxRigidBody:{to_camel_case(attr_name, to='cC')}"
# validate the values
self.assertAlmostEqual(
link_prim.GetAttribute(prim_prop_name).Get(),
attr_value,
places=5,
msg=f"Failed setting for {prim_prop_name}",
)
elif verbose:
print(f"Skipping prim {link_prim.GetPrimPath()} as it is not a rigid body.")
def _validate_collision_properties_on_prim(self, prim_path: str, verbose: bool = False):
"""Validate the collision properties on the prim.
Note:
Right now this function exploits the hierarchy in the asset to check the properties. This is not a
fool-proof way of checking the properties.
"""
# the root prim
root_prim = prim_utils.get_prim_at_path(prim_path)
# check collision properties are set correctly
for link_prim in root_prim.GetChildren():
for mesh_prim in link_prim.GetChildren():
if UsdPhysics.CollisionAPI(mesh_prim):
for attr_name, attr_value in self.collision_cfg.__dict__.items():
# skip names we know are not present
if attr_name in ["func", "collision_enabled"]:
continue
# convert attribute name in prim to cfg name
prim_prop_name = f"physxCollision:{to_camel_case(attr_name, to='cC')}"
# validate the values
self.assertAlmostEqual(
mesh_prim.GetAttribute(prim_prop_name).Get(),
attr_value,
places=5,
msg=f"Failed setting for {prim_prop_name}",
)
elif verbose:
print(f"Skipping prim {mesh_prim.GetPrimPath()} as it is not a collision mesh.")
def _validate_mass_properties_on_prim(self, prim_path: str, verbose: bool = False):
"""Validate the mass properties on the prim.
Note:
Right now this function exploits the hierarchy in the asset to check the properties. This is not a
fool-proof way of checking the properties.
"""
# the root prim
root_prim = prim_utils.get_prim_at_path(prim_path)
# check rigid body mass properties are set correctly
for link_prim in root_prim.GetChildren():
if UsdPhysics.MassAPI(link_prim):
for attr_name, attr_value in self.mass_cfg.__dict__.items():
# skip names we know are not present
if attr_name in ["func"]:
continue
# print(link_prim.GetProperties())
prim_prop_name = f"physics:{to_camel_case(attr_name, to='cC')}"
# validate the values
self.assertAlmostEqual(
link_prim.GetAttribute(prim_prop_name).Get(),
attr_value,
places=5,
msg=f"Failed setting for {prim_prop_name}",
)
elif verbose:
print(f"Skipping prim {link_prim.GetPrimPath()} as it is not a mass api.")
def _validate_joint_drive_properties_on_prim(self, prim_path: str, verbose: bool = False):
"""Validate the mass properties on the prim.
Note:
Right now this function exploits the hierarchy in the asset to check the properties. This is not a
fool-proof way of checking the properties.
"""
# the root prim
root_prim = prim_utils.get_prim_at_path(prim_path)
# check joint drive properties are set correctly
for link_prim in root_prim.GetAllChildren():
for joint_prim in link_prim.GetChildren():
if joint_prim.IsA(UsdPhysics.PrismaticJoint) or joint_prim.IsA(UsdPhysics.RevoluteJoint):
# check it has drive API
self.assertTrue(joint_prim.HasAPI(UsdPhysics.DriveAPI))
# iterate over the joint properties
for attr_name, attr_value in self.joint_cfg.__dict__.items():
# skip names we know are not present
if attr_name == "func":
continue
# manually check joint type
if attr_name == "drive_type":
if joint_prim.IsA(UsdPhysics.PrismaticJoint):
prim_attr_name = "drive:linear:physics:type"
elif joint_prim.IsA(UsdPhysics.RevoluteJoint):
prim_attr_name = "drive:angular:physics:type"
else:
raise ValueError(f"Unknown joint type for prim {joint_prim.GetPrimPath()}")
# check the value
self.assertEqual(attr_value, joint_prim.GetAttribute(prim_attr_name).Get())
continue
elif verbose:
print(f"Skipping prim {joint_prim.GetPrimPath()} as it is not a joint drive api.")
if __name__ == "__main__":
run_tests()
| 17,605 | Python | 46.972752 | 122 | 0.600738 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_spawn_materials.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
from omni.isaac.core.simulation_context import SimulationContext
from pxr import UsdPhysics, UsdShade
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.utils.assets import NVIDIA_NUCLEUS_DIR
class TestSpawningMaterials(unittest.TestCase):
"""Test fixture for checking spawning of materials."""
def setUp(self) -> None:
"""Create a blank new stage for each test."""
# Create a new stage
stage_utils.create_new_stage()
# Simulation time-step
self.dt = 0.1
# Load kit helper
self.sim = SimulationContext(physics_dt=self.dt, rendering_dt=self.dt, backend="numpy")
# Wait for spawning
stage_utils.update_stage()
def tearDown(self) -> None:
"""Stops simulator after each test."""
# stop simulation
self.sim.stop()
self.sim.clear()
self.sim.clear_all_callbacks()
self.sim.clear_instance()
def test_spawn_preview_surface(self):
"""Test spawning preview surface."""
# Spawn preview surface
cfg = sim_utils.materials.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0))
prim = cfg.func("/Looks/PreviewSurface", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/Looks/PreviewSurface"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Shader")
# Check properties
self.assertEqual(prim.GetAttribute("inputs:diffuseColor").Get(), cfg.diffuse_color)
def test_spawn_mdl_material(self):
"""Test spawning mdl material."""
# Spawn mdl material
cfg = sim_utils.materials.MdlFileCfg(
mdl_path=f"{NVIDIA_NUCLEUS_DIR}/Materials/Base/Metals/Aluminum_Anodized.mdl",
project_uvw=True,
albedo_brightness=0.5,
)
prim = cfg.func("/Looks/MdlMaterial", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/Looks/MdlMaterial"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Shader")
# Check properties
self.assertEqual(prim.GetAttribute("inputs:project_uvw").Get(), cfg.project_uvw)
self.assertEqual(prim.GetAttribute("inputs:albedo_brightness").Get(), cfg.albedo_brightness)
def test_spawn_glass_mdl_material(self):
"""Test spawning a glass mdl material."""
# Spawn mdl material
cfg = sim_utils.materials.GlassMdlCfg(thin_walled=False, glass_ior=1.0, glass_color=(0.0, 1.0, 0.0))
prim = cfg.func("/Looks/GlassMaterial", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/Looks/GlassMaterial"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Shader")
# Check properties
self.assertEqual(prim.GetAttribute("inputs:thin_walled").Get(), cfg.thin_walled)
self.assertEqual(prim.GetAttribute("inputs:glass_ior").Get(), cfg.glass_ior)
self.assertEqual(prim.GetAttribute("inputs:glass_color").Get(), cfg.glass_color)
def test_spawn_rigid_body_material(self):
"""Test spawning a rigid body material."""
# spawn physics material
cfg = sim_utils.materials.RigidBodyMaterialCfg(
dynamic_friction=1.5,
restitution=1.5,
static_friction=0.5,
restitution_combine_mode="max",
friction_combine_mode="max",
improve_patch_friction=True,
)
prim = cfg.func("/Looks/RigidBodyMaterial", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/Looks/RigidBodyMaterial"))
# Check properties
self.assertEqual(prim.GetAttribute("physics:staticFriction").Get(), cfg.static_friction)
self.assertEqual(prim.GetAttribute("physics:dynamicFriction").Get(), cfg.dynamic_friction)
self.assertEqual(prim.GetAttribute("physics:restitution").Get(), cfg.restitution)
self.assertEqual(prim.GetAttribute("physxMaterial:improvePatchFriction").Get(), cfg.improve_patch_friction)
self.assertEqual(prim.GetAttribute("physxMaterial:restitutionCombineMode").Get(), cfg.restitution_combine_mode)
self.assertEqual(prim.GetAttribute("physxMaterial:frictionCombineMode").Get(), cfg.friction_combine_mode)
def test_apply_rigid_body_material_on_visual_material(self):
"""Test applying a rigid body material on a visual material."""
# Spawn mdl material
cfg = sim_utils.materials.GlassMdlCfg(thin_walled=False, glass_ior=1.0, glass_color=(0.0, 1.0, 0.0))
prim = cfg.func("/Looks/Material", cfg)
# spawn physics material
cfg = sim_utils.materials.RigidBodyMaterialCfg(
dynamic_friction=1.5,
restitution=1.5,
static_friction=0.5,
restitution_combine_mode="max",
friction_combine_mode="max",
improve_patch_friction=True,
)
prim = cfg.func("/Looks/Material", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/Looks/Material"))
# Check properties
self.assertEqual(prim.GetAttribute("physics:staticFriction").Get(), cfg.static_friction)
self.assertEqual(prim.GetAttribute("physics:dynamicFriction").Get(), cfg.dynamic_friction)
self.assertEqual(prim.GetAttribute("physics:restitution").Get(), cfg.restitution)
self.assertEqual(prim.GetAttribute("physxMaterial:improvePatchFriction").Get(), cfg.improve_patch_friction)
self.assertEqual(prim.GetAttribute("physxMaterial:restitutionCombineMode").Get(), cfg.restitution_combine_mode)
self.assertEqual(prim.GetAttribute("physxMaterial:frictionCombineMode").Get(), cfg.friction_combine_mode)
def test_bind_prim_to_material(self):
"""Test binding a rigid body material on a mesh prim."""
# create a mesh prim
object_prim = prim_utils.create_prim("/World/Geometry/box", "Cube")
UsdPhysics.CollisionAPI.Apply(object_prim)
# create a visual material
visual_material_cfg = sim_utils.GlassMdlCfg(glass_ior=1.0, thin_walled=True)
visual_material_cfg.func("/World/Looks/glassMaterial", visual_material_cfg)
# create a physics material
physics_material_cfg = sim_utils.RigidBodyMaterialCfg(
static_friction=0.5, dynamic_friction=1.5, restitution=1.5
)
physics_material_cfg.func("/World/Physics/rubberMaterial", physics_material_cfg)
# bind the visual material to the mesh prim
sim_utils.bind_visual_material("/World/Geometry/box", "/World/Looks/glassMaterial")
sim_utils.bind_physics_material("/World/Geometry/box", "/World/Physics/rubberMaterial")
# check the main material binding
material_binding_api = UsdShade.MaterialBindingAPI(object_prim)
# -- visual
material_direct_binding = material_binding_api.GetDirectBinding()
self.assertEqual(material_direct_binding.GetMaterialPath(), "/World/Looks/glassMaterial")
self.assertEqual(material_direct_binding.GetMaterialPurpose(), "")
# -- physics
material_direct_binding = material_binding_api.GetDirectBinding("physics")
self.assertEqual(material_direct_binding.GetMaterialPath(), "/World/Physics/rubberMaterial")
self.assertEqual(material_direct_binding.GetMaterialPurpose(), "physics")
if __name__ == "__main__":
run_tests()
| 8,041 | Python | 45.485549 | 119 | 0.673175 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_spawn_lights.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
from omni.isaac.core.simulation_context import SimulationContext
from pxr import UsdLux
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.utils.string import to_camel_case
class TestSpawningLights(unittest.TestCase):
"""Test fixture for checking spawning of USD lights with different settings."""
def setUp(self) -> None:
"""Create a blank new stage for each test."""
# Create a new stage
stage_utils.create_new_stage()
# Simulation time-step
self.dt = 0.1
# Load kit helper
self.sim = SimulationContext(physics_dt=self.dt, rendering_dt=self.dt, backend="numpy")
# Wait for spawning
stage_utils.update_stage()
def tearDown(self) -> None:
"""Stops simulator after each test."""
# stop simulation
self.sim.stop()
self.sim.clear()
self.sim.clear_all_callbacks()
self.sim.clear_instance()
"""
Basic spawning.
"""
def test_spawn_disk_light(self):
"""Test spawning a disk light source."""
cfg = sim_utils.DiskLightCfg(
color=(0.1, 0.1, 0.1), enable_color_temperature=True, color_temperature=5500, intensity=100, radius=20.0
)
prim = cfg.func("/World/disk_light", cfg)
# check if the light is spawned
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/disk_light"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "DiskLight")
# validate properties on the prim
self._validate_properties_on_prim("/World/disk_light", cfg)
def test_spawn_distant_light(self):
"""Test spawning a distant light."""
cfg = sim_utils.DistantLightCfg(
color=(0.1, 0.1, 0.1), enable_color_temperature=True, color_temperature=5500, intensity=100, angle=20
)
prim = cfg.func("/World/distant_light", cfg)
# check if the light is spawned
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/distant_light"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "DistantLight")
# validate properties on the prim
self._validate_properties_on_prim("/World/distant_light", cfg)
def test_spawn_dome_light(self):
"""Test spawning a dome light source."""
cfg = sim_utils.DomeLightCfg(
color=(0.1, 0.1, 0.1), enable_color_temperature=True, color_temperature=5500, intensity=100
)
prim = cfg.func("/World/dome_light", cfg)
# check if the light is spawned
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/dome_light"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "DomeLight")
# validate properties on the prim
self._validate_properties_on_prim("/World/dome_light", cfg)
def test_spawn_cylinder_light(self):
"""Test spawning a cylinder light source."""
cfg = sim_utils.CylinderLightCfg(
color=(0.1, 0.1, 0.1), enable_color_temperature=True, color_temperature=5500, intensity=100, radius=20.0
)
prim = cfg.func("/World/cylinder_light", cfg)
# check if the light is spawned
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/cylinder_light"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "CylinderLight")
# validate properties on the prim
self._validate_properties_on_prim("/World/cylinder_light", cfg)
def test_spawn_sphere_light(self):
"""Test spawning a sphere light source."""
cfg = sim_utils.SphereLightCfg(
color=(0.1, 0.1, 0.1), enable_color_temperature=True, color_temperature=5500, intensity=100, radius=20.0
)
prim = cfg.func("/World/sphere_light", cfg)
# check if the light is spawned
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/sphere_light"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "SphereLight")
# validate properties on the prim
self._validate_properties_on_prim("/World/sphere_light", cfg)
"""
Helper functions.
"""
def _validate_properties_on_prim(self, prim_path: str, cfg: sim_utils.LightCfg):
"""Validate the properties on the prim.
Args:
prim_path: The prim name.
cfg: The configuration for the light source.
"""
# default list of params to skip
non_usd_params = ["func", "prim_type", "visible", "semantic_tags", "copy_from_source"]
# obtain prim
prim = prim_utils.get_prim_at_path(prim_path)
for attr_name, attr_value in cfg.__dict__.items():
# skip names we know are not present
if attr_name in non_usd_params or attr_value is None:
continue
# deal with texture input names
if "texture" in attr_name:
light_prim = UsdLux.DomeLight(prim)
if attr_name == "texture_file":
configured_value = light_prim.GetTextureFileAttr().Get()
elif attr_name == "texture_format":
configured_value = light_prim.GetTextureFormatAttr().Get()
else:
raise ValueError(f"Unknown texture attribute: '{attr_name}'")
else:
# convert attribute name in prim to cfg name
prim_prop_name = f"inputs:{to_camel_case(attr_name, to='cC')}"
# configured value
configured_value = prim.GetAttribute(prim_prop_name).Get()
# validate the values
self.assertEqual(configured_value, attr_value, msg=f"Failed for attribute: '{attr_name}'")
if __name__ == "__main__":
run_tests()
| 6,329 | Python | 38.31677 | 116 | 0.626165 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_utils.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
config = {"headless": True}
simulation_app = AppLauncher(config).app
"""Rest everything follows."""
import numpy as np
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
from pxr import Sdf, Usd, UsdGeom
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.utils.assets import ISAAC_NUCLEUS_DIR, ISAAC_ORBIT_NUCLEUS_DIR
class TestUtilities(unittest.TestCase):
"""Test fixture for the sim utility functions."""
def setUp(self):
"""Create a blank new stage for each test."""
# Create a new stage
stage_utils.create_new_stage()
stage_utils.update_stage()
def tearDown(self) -> None:
"""Clear stage after each test."""
stage_utils.clear_stage()
def test_get_all_matching_child_prims(self):
"""Test get_all_matching_child_prims() function."""
# create scene
prim_utils.create_prim("/World/Floor")
prim_utils.create_prim(
"/World/Floor/thefloor", "Cube", position=np.array([75, 75, -150.1]), attributes={"size": 300}
)
prim_utils.create_prim("/World/Room", "Sphere", attributes={"radius": 1e3})
# test
isaac_sim_result = prim_utils.get_all_matching_child_prims("/World")
orbit_result = sim_utils.get_all_matching_child_prims("/World")
self.assertListEqual(isaac_sim_result, orbit_result)
# test valid path
with self.assertRaises(ValueError):
sim_utils.get_all_matching_child_prims("World/Room")
def test_find_matching_prim_paths(self):
"""Test find_matching_prim_paths() function."""
# create scene
for index in range(2048):
random_pos = np.random.uniform(-100, 100, size=3)
prim_utils.create_prim(f"/World/Floor_{index}", "Cube", position=random_pos, attributes={"size": 2.0})
prim_utils.create_prim(f"/World/Floor_{index}/Sphere", "Sphere", attributes={"radius": 10})
prim_utils.create_prim(f"/World/Floor_{index}/Sphere/childSphere", "Sphere", attributes={"radius": 1})
prim_utils.create_prim(f"/World/Floor_{index}/Sphere/childSphere2", "Sphere", attributes={"radius": 1})
# test leaf paths
isaac_sim_result = prim_utils.find_matching_prim_paths("/World/Floor_.*/Sphere")
orbit_result = sim_utils.find_matching_prim_paths("/World/Floor_.*/Sphere")
self.assertListEqual(isaac_sim_result, orbit_result)
# test non-leaf paths
isaac_sim_result = prim_utils.find_matching_prim_paths("/World/Floor_.*")
orbit_result = sim_utils.find_matching_prim_paths("/World/Floor_.*")
self.assertListEqual(isaac_sim_result, orbit_result)
# test child-leaf paths
isaac_sim_result = prim_utils.find_matching_prim_paths("/World/Floor_.*/Sphere/childSphere.*")
orbit_result = sim_utils.find_matching_prim_paths("/World/Floor_.*/Sphere/childSphere.*")
self.assertListEqual(isaac_sim_result, orbit_result)
# test valid path
with self.assertRaises(ValueError):
sim_utils.get_all_matching_child_prims("World/Floor_.*")
def test_find_global_fixed_joint_prim(self):
"""Test find_global_fixed_joint_prim() function."""
# create scene
prim_utils.create_prim("/World")
prim_utils.create_prim(
"/World/ANYmal", usd_path=f"{ISAAC_ORBIT_NUCLEUS_DIR}/Robots/ANYbotics/ANYmal-C/anymal_c.usd"
)
prim_utils.create_prim(
"/World/Franka", usd_path=f"{ISAAC_ORBIT_NUCLEUS_DIR}/Robots/FrankaEmika/panda_instanceable.usd"
)
prim_utils.create_prim("/World/Franka_Isaac", usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Franka/franka.usd")
# test
self.assertIsNone(sim_utils.find_global_fixed_joint_prim("/World/ANYmal"))
self.assertIsNotNone(sim_utils.find_global_fixed_joint_prim("/World/Franka"))
self.assertIsNotNone(sim_utils.find_global_fixed_joint_prim("/World/Franka_Isaac"))
# make fixed joint disabled manually
joint_prim = sim_utils.find_global_fixed_joint_prim("/World/Franka")
joint_prim.GetJointEnabledAttr().Set(False)
self.assertIsNotNone(sim_utils.find_global_fixed_joint_prim("/World/Franka"))
self.assertIsNone(sim_utils.find_global_fixed_joint_prim("/World/Franka", check_enabled_only=True))
def test_select_usd_variants(self):
"""Test select_usd_variants() function."""
stage = stage_utils.get_current_stage()
prim: Usd.Prim = UsdGeom.Xform.Define(stage, Sdf.Path("/World")).GetPrim()
stage.SetDefaultPrim(prim)
# Create the variant set and add your variants to it.
variants = ["red", "blue", "green"]
variant_set = prim.GetVariantSets().AddVariantSet("colors")
for variant in variants:
variant_set.AddVariant(variant)
# Set the variant selection
sim_utils.utils.select_usd_variants("/World", {"colors": "red"}, stage)
# Check if the variant selection is correct
self.assertEqual(variant_set.GetVariantSelection(), "red")
if __name__ == "__main__":
run_tests()
| 5,443 | Python | 40.557252 | 115 | 0.653316 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_build_simulation_context_headless.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This test has a lot of duplication with ``test_build_simulation_context_nonheadless.py``. This is intentional to ensure that the
tests are run in both headless and non-headless modes, and we currently can't re-build the simulation app in a script.
If you need to make a change to this test, please make sure to also make the same change to ``test_build_simulation_context_nonheadless.py``.
"""
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
app_launcher = AppLauncher(headless=True)
simulation_app = app_launcher.app
"""Rest everything follows."""
import unittest
from omni.isaac.core.utils.prims import is_prim_path_valid
from omni.isaac.orbit.sim.simulation_cfg import SimulationCfg
from omni.isaac.orbit.sim.simulation_context import build_simulation_context
class TestBuildSimulationContextHeadless(unittest.TestCase):
"""Tests for simulation context builder with headless usecase."""
"""
Tests
"""
def test_build_simulation_context_no_cfg(self):
"""Test that the simulation context is built when no simulation cfg is passed in."""
for gravity_enabled in (True, False):
for device in ("cuda:0", "cpu"):
for dt in (0.01, 0.1):
with self.subTest(gravity_enabled=gravity_enabled, device=device, dt=dt):
with build_simulation_context(gravity_enabled=gravity_enabled, device=device, dt=dt) as sim:
if gravity_enabled:
self.assertEqual(sim.cfg.gravity, (0.0, 0.0, -9.81))
else:
self.assertEqual(sim.cfg.gravity, (0.0, 0.0, 0.0))
if device == "cuda:0":
self.assertEqual(sim.cfg.device, "cuda:0")
else:
self.assertEqual(sim.cfg.device, "cpu")
self.assertEqual(sim.cfg.dt, dt)
# Ensure that dome light didn't get added automatically as we are headless
self.assertFalse(is_prim_path_valid("/World/defaultDomeLight"))
def test_build_simulation_context_ground_plane(self):
"""Test that the simulation context is built with the correct ground plane."""
for add_ground_plane in (True, False):
with self.subTest(add_ground_plane=add_ground_plane):
with build_simulation_context(add_ground_plane=add_ground_plane) as _:
# Ensure that ground plane got added
self.assertEqual(is_prim_path_valid("/World/defaultGroundPlane"), add_ground_plane)
def test_build_simulation_context_auto_add_lighting(self):
"""Test that the simulation context is built with the correct lighting."""
for add_lighting in (True, False):
for auto_add_lighting in (True, False):
with self.subTest(add_lighting=add_lighting, auto_add_lighting=auto_add_lighting):
with build_simulation_context(add_lighting=add_lighting, auto_add_lighting=auto_add_lighting) as _:
if add_lighting:
# Ensure that dome light got added
self.assertTrue(is_prim_path_valid("/World/defaultDomeLight"))
else:
# Ensure that dome light didn't get added as there's no GUI
self.assertFalse(is_prim_path_valid("/World/defaultDomeLight"))
def test_build_simulation_context_cfg(self):
"""Test that the simulation context is built with the correct cfg and values don't get overridden."""
dt = 0.001
# Non-standard gravity
gravity = (0.0, 0.0, -1.81)
device = "cuda:0"
cfg = SimulationCfg(
gravity=gravity,
device=device,
dt=dt,
)
with build_simulation_context(sim_cfg=cfg, gravity_enabled=False, dt=0.01, device="cpu") as sim:
self.assertEqual(sim.cfg.gravity, gravity)
self.assertEqual(sim.cfg.device, device)
self.assertEqual(sim.cfg.dt, dt)
if __name__ == "__main__":
run_tests()
| 4,422 | Python | 41.941747 | 141 | 0.605834 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_build_simulation_context_nonheadless.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""This test has a lot of duplication with ``test_build_simulation_context_headless.py``. This is intentional to ensure that the
tests are run in both headless and non-headless modes, and we currently can't re-build the simulation app in a script.
If you need to make a change to this test, please make sure to also make the same change to ``test_build_simulation_context_headless.py``.
"""
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
app_launcher = AppLauncher(headless=False)
simulation_app = app_launcher.app
"""Rest everything follows."""
import unittest
from omni.isaac.core.utils.prims import is_prim_path_valid
from omni.isaac.orbit.sim.simulation_cfg import SimulationCfg
from omni.isaac.orbit.sim.simulation_context import build_simulation_context
class TestBuildSimulationContextNonheadless(unittest.TestCase):
"""Tests for simulation context builder with non-headless usecase."""
"""
Tests
"""
def test_build_simulation_context_no_cfg(self):
"""Test that the simulation context is built when no simulation cfg is passed in."""
for gravity_enabled in (True, False):
for device in ("cuda:0", "cpu"):
for dt in (0.01, 0.1):
with self.subTest(gravity_enabled=gravity_enabled, device=device, dt=dt):
with build_simulation_context(
gravity_enabled=gravity_enabled,
device=device,
dt=dt,
) as sim:
if gravity_enabled:
self.assertEqual(sim.cfg.gravity, (0.0, 0.0, -9.81))
else:
self.assertEqual(sim.cfg.gravity, (0.0, 0.0, 0.0))
if device == "cuda:0":
self.assertEqual(sim.cfg.device, "cuda:0")
else:
self.assertEqual(sim.cfg.device, "cpu")
self.assertEqual(sim.cfg.dt, dt)
def test_build_simulation_context_ground_plane(self):
"""Test that the simulation context is built with the correct ground plane."""
for add_ground_plane in (True, False):
with self.subTest(add_ground_plane=add_ground_plane):
with build_simulation_context(add_ground_plane=add_ground_plane) as _:
# Ensure that ground plane got added
self.assertEqual(is_prim_path_valid("/World/defaultGroundPlane"), add_ground_plane)
def test_build_simulation_context_auto_add_lighting(self):
"""Test that the simulation context is built with the correct lighting."""
for add_lighting in (True, False):
for auto_add_lighting in (True, False):
with self.subTest(add_lighting=add_lighting, auto_add_lighting=auto_add_lighting):
with build_simulation_context(add_lighting=add_lighting, auto_add_lighting=auto_add_lighting) as _:
if auto_add_lighting or add_lighting:
# Ensure that dome light got added
self.assertTrue(is_prim_path_valid("/World/defaultDomeLight"))
else:
# Ensure that dome light didn't get added
self.assertFalse(is_prim_path_valid("/World/defaultDomeLight"))
def test_build_simulation_context_cfg(self):
"""Test that the simulation context is built with the correct cfg and values don't get overridden."""
dt = 0.001
# Non-standard gravity
gravity = (0.0, 0.0, -1.81)
device = "cuda:0"
cfg = SimulationCfg(
gravity=gravity,
device=device,
dt=dt,
)
with build_simulation_context(sim_cfg=cfg, gravity_enabled=False, dt=0.01, device="cpu") as sim:
self.assertEqual(sim.cfg.gravity, gravity)
self.assertEqual(sim.cfg.device, device)
self.assertEqual(sim.cfg.dt, dt)
if __name__ == "__main__":
run_tests()
| 4,341 | Python | 41.155339 | 138 | 0.592721 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_simulation_context.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True, experience="omni.isaac.sim.python.gym.headless.kit").app
"""Rest everything follows."""
import ctypes
import numpy as np
import unittest
import omni.isaac.core.utils.prims as prim_utils
from omni.isaac.core.simulation_context import SimulationContext as IsaacSimulationContext
from omni.isaac.orbit.sim import SimulationCfg, SimulationContext
class TestSimulationContext(unittest.TestCase):
"""Test fixture for wrapper around simulation context."""
def setUp(self) -> None:
"""Create a blank new stage for each test."""
# Load kit helper
SimulationContext.clear_instance()
def test_singleton(self):
"""Tests that the singleton is working."""
sim1 = SimulationContext()
sim2 = SimulationContext()
sim3 = IsaacSimulationContext()
self.assertIs(sim1, sim2)
self.assertIs(sim1, sim3)
# try to delete the singleton
sim2.clear_instance()
self.assertTrue(sim1.instance() is None)
# create new instance
sim4 = SimulationContext()
self.assertIsNot(sim1, sim4)
self.assertIsNot(sim3, sim4)
self.assertIs(sim1.instance(), sim4.instance())
self.assertIs(sim3.instance(), sim4.instance())
# clear instance
sim3.clear_instance()
def test_initialization(self):
"""Test the simulation config."""
cfg = SimulationCfg(physics_prim_path="/Physics/PhysX", substeps=5, gravity=(0.0, -0.5, -0.5))
sim = SimulationContext(cfg)
# TODO: Figure out why keyword argument doesn't work.
# note: added a fix in Isaac Sim 2023.1 for this.
# sim = SimulationContext(cfg=cfg)
# check valid settings
self.assertEqual(sim.get_physics_dt(), cfg.dt)
self.assertEqual(sim.get_rendering_dt(), cfg.dt * cfg.substeps)
self.assertFalse(sim.has_rtx_sensors())
# check valid paths
self.assertTrue(prim_utils.is_prim_path_valid("/Physics/PhysX"))
self.assertTrue(prim_utils.is_prim_path_valid("/Physics/PhysX/defaultMaterial"))
# check valid gravity
gravity_dir, gravity_mag = sim.get_physics_context().get_gravity()
gravity = np.array(gravity_dir) * gravity_mag
np.testing.assert_almost_equal(gravity, cfg.gravity)
def test_sim_version(self):
"""Test obtaining the version."""
sim = SimulationContext()
version = sim.get_version()
self.assertTrue(len(version) > 0)
self.assertTrue(version[0] >= 2023)
def test_carb_setting(self):
"""Test setting carb settings."""
sim = SimulationContext()
# known carb setting
sim.set_setting("/physics/physxDispatcher", False)
self.assertEqual(sim.get_setting("/physics/physxDispatcher"), False)
# unknown carb setting
sim.set_setting("/myExt/using_omniverse_version", sim.get_version())
self.assertSequenceEqual(sim.get_setting("/myExt/using_omniverse_version"), sim.get_version())
def test_headless_mode(self):
"""Test that render mode is headless since we are running in headless mode."""
sim = SimulationContext()
# check default render mode
self.assertEqual(sim.render_mode, sim.RenderMode.NO_GUI_OR_RENDERING)
def test_boundedness(self):
"""Test that the boundedness of the simulation context remains constant.
Note: This test fails right now because Isaac Sim does not handle boundedness correctly. On creation,
it is registering itself to various callbacks and hence the boundedness is more than 1. This may not be
critical for the simulation context since we usually call various clear functions before deleting the
simulation context.
"""
sim = SimulationContext()
# manually set the boundedness to 1? -- this is not possible because of Isaac Sim.
sim.clear_all_callbacks()
sim._stage_open_callback = None
sim._physics_timer_callback = None
sim._event_timer_callback = None
# check that boundedness of simulation context is correct
sim_ref_count = ctypes.c_long.from_address(id(sim)).value
# reset the simulation
sim.reset()
self.assertEqual(ctypes.c_long.from_address(id(sim)).value, sim_ref_count)
# step the simulation
for _ in range(10):
sim.step()
self.assertEqual(ctypes.c_long.from_address(id(sim)).value, sim_ref_count)
# clear the simulation
sim.clear_instance()
self.assertEqual(ctypes.c_long.from_address(id(sim)).value, sim_ref_count - 1)
def test_zero_gravity(self):
"""Test that gravity can be properly disabled."""
cfg = SimulationCfg(gravity=(0.0, 0.0, 0.0))
sim = SimulationContext(cfg)
gravity_dir, gravity_mag = sim.get_physics_context().get_gravity()
gravity = np.array(gravity_dir) * gravity_mag
np.testing.assert_almost_equal(gravity, cfg.gravity)
if __name__ == "__main__":
run_tests()
| 5,341 | Python | 37.710145 | 111 | 0.659802 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_mesh_converter.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import os
import tempfile
import unittest
import omni
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
from omni.isaac.core.simulation_context import SimulationContext
from pxr import UsdGeom, UsdPhysics
from omni.isaac.orbit.sim.converters import MeshConverter, MeshConverterCfg
from omni.isaac.orbit.sim.schemas import schemas_cfg
from omni.isaac.orbit.utils.assets import ISAAC_ORBIT_NUCLEUS_DIR, retrieve_file_path
class TestMeshConverter(unittest.TestCase):
"""Test fixture for the MeshConverter class."""
@classmethod
def setUpClass(cls):
"""Load assets for tests."""
assets_dir = f"{ISAAC_ORBIT_NUCLEUS_DIR}/Tests/MeshConverter/duck"
# Create mapping of file endings to file paths that can be used by tests
cls.assets = {
"obj": f"{assets_dir}/duck.obj",
"stl": f"{assets_dir}/duck.stl",
"fbx": f"{assets_dir}/duck.fbx",
"mtl": f"{assets_dir}/duck.mtl",
"png": f"{assets_dir}/duckCM.png",
}
# Download all these locally
download_dir = tempfile.mkdtemp(suffix="_mesh_converter_test_assets")
for key, value in cls.assets.items():
cls.assets[key] = retrieve_file_path(value, download_dir=download_dir)
def setUp(self):
"""Create a blank new stage for each test."""
# Create a new stage
stage_utils.create_new_stage()
# Simulation time-step
self.dt = 0.01
# Load kit helper
self.sim = SimulationContext(physics_dt=self.dt, rendering_dt=self.dt, backend="numpy")
def tearDown(self) -> None:
"""Stops simulator after each test."""
# stop simulation
self.sim.stop()
# cleanup stage and context
self.sim.clear()
self.sim.clear_all_callbacks()
self.sim.clear_instance()
"""
Test fixtures.
"""
def test_no_change(self):
"""Call conversion twice on the same input asset. This should not generate a new USD file if the hash is the same."""
# create an initial USD file from asset
mesh_config = MeshConverterCfg(asset_path=self.assets["obj"])
mesh_converter = MeshConverter(mesh_config)
time_usd_file_created = os.stat(mesh_converter.usd_path).st_mtime_ns
# no change to config only define the usd directory
new_config = mesh_config
new_config.usd_dir = mesh_converter.usd_dir
# convert to usd but this time in the same directory as previous step
new_mesh_converter = MeshConverter(new_config)
new_time_usd_file_created = os.stat(new_mesh_converter.usd_path).st_mtime_ns
self.assertEqual(time_usd_file_created, new_time_usd_file_created)
def test_config_change(self):
"""Call conversion twice but change the config in the second call. This should generate a new USD file."""
# create an initial USD file from asset
mesh_config = MeshConverterCfg(asset_path=self.assets["obj"])
mesh_converter = MeshConverter(mesh_config)
time_usd_file_created = os.stat(mesh_converter.usd_path).st_mtime_ns
# change the config
new_config = mesh_config
new_config.make_instanceable = not mesh_config.make_instanceable
# define the usd directory
new_config.usd_dir = mesh_converter.usd_dir
# convert to usd but this time in the same directory as previous step
new_mesh_converter = MeshConverter(new_config)
new_time_usd_file_created = os.stat(new_mesh_converter.usd_path).st_mtime_ns
self.assertNotEqual(time_usd_file_created, new_time_usd_file_created)
def test_convert_obj(self):
"""Convert an OBJ file"""
mesh_config = MeshConverterCfg(asset_path=self.assets["obj"])
mesh_converter = MeshConverter(mesh_config)
# check that mesh conversion is successful
self._check_mesh_conversion(mesh_converter)
def test_convert_stl(self):
"""Convert an STL file"""
mesh_config = MeshConverterCfg(asset_path=self.assets["stl"])
mesh_converter = MeshConverter(mesh_config)
# check that mesh conversion is successful
self._check_mesh_conversion(mesh_converter)
def test_convert_fbx(self):
"""Convert an FBX file"""
mesh_config = MeshConverterCfg(asset_path=self.assets["fbx"])
mesh_converter = MeshConverter(mesh_config)
# check that mesh conversion is successful
self._check_mesh_conversion(mesh_converter)
def test_collider_no_approximation(self):
"""Convert an OBJ file using no approximation"""
collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True)
mesh_config = MeshConverterCfg(
asset_path=self.assets["obj"],
collision_approximation="none",
collision_props=collision_props,
)
mesh_converter = MeshConverter(mesh_config)
# check that mesh conversion is successful
self._check_mesh_collider_settings(mesh_converter)
def test_collider_convex_hull(self):
"""Convert an OBJ file using convex hull approximation"""
collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True)
mesh_config = MeshConverterCfg(
asset_path=self.assets["obj"],
collision_approximation="convexHull",
collision_props=collision_props,
)
mesh_converter = MeshConverter(mesh_config)
# check that mesh conversion is successful
self._check_mesh_collider_settings(mesh_converter)
def test_collider_mesh_simplification(self):
"""Convert an OBJ file using mesh simplification approximation"""
collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True)
mesh_config = MeshConverterCfg(
asset_path=self.assets["obj"],
collision_approximation="meshSimplification",
collision_props=collision_props,
)
mesh_converter = MeshConverter(mesh_config)
# check that mesh conversion is successful
self._check_mesh_collider_settings(mesh_converter)
def test_collider_mesh_bounding_cube(self):
"""Convert an OBJ file using bounding cube approximation"""
collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True)
mesh_config = MeshConverterCfg(
asset_path=self.assets["obj"],
collision_approximation="boundingCube",
collision_props=collision_props,
)
mesh_converter = MeshConverter(mesh_config)
# check that mesh conversion is successful
self._check_mesh_collider_settings(mesh_converter)
def test_collider_mesh_bounding_sphere(self):
"""Convert an OBJ file using bounding sphere"""
collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=True)
mesh_config = MeshConverterCfg(
asset_path=self.assets["obj"],
collision_approximation="boundingSphere",
collision_props=collision_props,
)
mesh_converter = MeshConverter(mesh_config)
# check that mesh conversion is successful
self._check_mesh_collider_settings(mesh_converter)
def test_collider_mesh_no_collision(self):
"""Convert an OBJ file using bounding sphere with collision disabled"""
collision_props = schemas_cfg.CollisionPropertiesCfg(collision_enabled=False)
mesh_config = MeshConverterCfg(
asset_path=self.assets["obj"],
collision_approximation="boundingSphere",
collision_props=collision_props,
)
mesh_converter = MeshConverter(mesh_config)
# check that mesh conversion is successful
self._check_mesh_collider_settings(mesh_converter)
"""
Helper functions.
"""
def _check_mesh_conversion(self, mesh_converter: MeshConverter):
"""Check that mesh is loadable and stage is valid."""
# Load the mesh
prim_path = "/World/Object"
prim_utils.create_prim(prim_path, usd_path=mesh_converter.usd_path)
# Check prim can be properly spawned
self.assertTrue(prim_utils.is_prim_path_valid(prim_path))
# Load a second time
prim_path = "/World/Object2"
prim_utils.create_prim(prim_path, usd_path=mesh_converter.usd_path)
# Check prim can be properly spawned
self.assertTrue(prim_utils.is_prim_path_valid(prim_path))
stage = omni.usd.get_context().get_stage()
# Check axis is z-up
axis = UsdGeom.GetStageUpAxis(stage)
self.assertEqual(axis, "Z")
# Check units is meters
units = UsdGeom.GetStageMetersPerUnit(stage)
self.assertEqual(units, 1.0)
def _check_mesh_collider_settings(self, mesh_converter: MeshConverter):
# Check prim can be properly spawned
prim_path = "/World/Object"
prim_utils.create_prim(prim_path, usd_path=mesh_converter.usd_path)
self.assertTrue(prim_utils.is_prim_path_valid(prim_path))
# Make uninstanceable to check collision settings
geom_prim = prim_utils.get_prim_at_path(prim_path + "/geometry")
# Check that instancing worked!
self.assertEqual(geom_prim.IsInstanceable(), mesh_converter.cfg.make_instanceable)
# Obtain mesh settings
geom_prim.SetInstanceable(False)
mesh_prim = prim_utils.get_prim_at_path(prim_path + "/geometry/mesh")
# Check collision settings
# -- if collision is enabled, check that API is present
exp_collision_enabled = (
mesh_converter.cfg.collision_props is not None and mesh_converter.cfg.collision_props.collision_enabled
)
collision_api = UsdPhysics.CollisionAPI(mesh_prim)
collision_enabled = collision_api.GetCollisionEnabledAttr().Get()
self.assertEqual(collision_enabled, exp_collision_enabled, "Collision enabled is not the same!")
# -- if collision is enabled, check that collision approximation is correct
if exp_collision_enabled:
exp_collision_approximation = mesh_converter.cfg.collision_approximation
mesh_collision_api = UsdPhysics.MeshCollisionAPI(mesh_prim)
collision_approximation = mesh_collision_api.GetApproximationAttr().Get()
self.assertEqual(
collision_approximation, exp_collision_approximation, "Collision approximation is not the same!"
)
if __name__ == "__main__":
run_tests()
| 10,938 | Python | 40.12406 | 125 | 0.664564 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_spawn_shapes.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
from omni.isaac.core.simulation_context import SimulationContext
import omni.isaac.orbit.sim as sim_utils
class TestSpawningUsdGeometries(unittest.TestCase):
"""Test fixture for checking spawning of USDGeom prim with different settings."""
def setUp(self) -> None:
"""Create a blank new stage for each test."""
# Create a new stage
stage_utils.create_new_stage()
# Simulation time-step
self.dt = 0.1
# Load kit helper
self.sim = SimulationContext(physics_dt=self.dt, rendering_dt=self.dt, backend="numpy")
# Wait for spawning
stage_utils.update_stage()
def tearDown(self) -> None:
"""Stops simulator after each test."""
# stop simulation
self.sim.stop()
self.sim.clear()
self.sim.clear_all_callbacks()
self.sim.clear_instance()
"""
Basic spawning.
"""
def test_spawn_cone(self):
"""Test spawning of UsdGeom.Cone prim."""
# Spawn cone
cfg = sim_utils.ConeCfg(radius=1.0, height=2.0, axis="Y")
prim = cfg.func("/World/Cone", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Cone"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Xform")
# Check properties
prim = prim_utils.get_prim_at_path("/World/Cone/geometry/mesh")
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Cone")
self.assertEqual(prim.GetAttribute("radius").Get(), cfg.radius)
self.assertEqual(prim.GetAttribute("height").Get(), cfg.height)
self.assertEqual(prim.GetAttribute("axis").Get(), cfg.axis)
def test_spawn_capsule(self):
"""Test spawning of UsdGeom.Capsule prim."""
# Spawn capsule
cfg = sim_utils.CapsuleCfg(radius=1.0, height=2.0, axis="Y")
prim = cfg.func("/World/Capsule", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Capsule"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Xform")
# Check properties
prim = prim_utils.get_prim_at_path("/World/Capsule/geometry/mesh")
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Capsule")
self.assertEqual(prim.GetAttribute("radius").Get(), cfg.radius)
self.assertEqual(prim.GetAttribute("height").Get(), cfg.height)
self.assertEqual(prim.GetAttribute("axis").Get(), cfg.axis)
def test_spawn_cylinder(self):
"""Test spawning of UsdGeom.Cylinder prim."""
# Spawn cylinder
cfg = sim_utils.CylinderCfg(radius=1.0, height=2.0, axis="Y")
prim = cfg.func("/World/Cylinder", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Cylinder"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Xform")
# Check properties
prim = prim_utils.get_prim_at_path("/World/Cylinder/geometry/mesh")
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Cylinder")
self.assertEqual(prim.GetAttribute("radius").Get(), cfg.radius)
self.assertEqual(prim.GetAttribute("height").Get(), cfg.height)
self.assertEqual(prim.GetAttribute("axis").Get(), cfg.axis)
def test_spawn_cuboid(self):
"""Test spawning of UsdGeom.Cube prim."""
# Spawn cuboid
cfg = sim_utils.CuboidCfg(size=(1.0, 2.0, 3.0))
prim = cfg.func("/World/Cube", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Cube"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Xform")
# Check properties
prim = prim_utils.get_prim_at_path("/World/Cube/geometry/mesh")
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Cube")
self.assertEqual(prim.GetAttribute("size").Get(), min(cfg.size))
def test_spawn_sphere(self):
"""Test spawning of UsdGeom.Sphere prim."""
# Spawn sphere
cfg = sim_utils.SphereCfg(radius=1.0)
prim = cfg.func("/World/Sphere", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Sphere"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Xform")
# Check properties
prim = prim_utils.get_prim_at_path("/World/Sphere/geometry/mesh")
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Sphere")
self.assertEqual(prim.GetAttribute("radius").Get(), cfg.radius)
"""
Physics properties.
"""
def test_spawn_cone_with_rigid_props(self):
"""Test spawning of UsdGeom.Cone prim with rigid body API.
Note:
Playing the simulation in this case will give a warning that no mass is specified!
Need to also setup mass and colliders.
"""
# Spawn cone
cfg = sim_utils.ConeCfg(
radius=1.0,
height=2.0,
rigid_props=sim_utils.RigidBodyPropertiesCfg(
rigid_body_enabled=True, solver_position_iteration_count=8, sleep_threshold=0.1
),
)
prim = cfg.func("/World/Cone", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Cone"))
# Check properties
prim = prim_utils.get_prim_at_path("/World/Cone")
self.assertEqual(prim.GetAttribute("physics:rigidBodyEnabled").Get(), cfg.rigid_props.rigid_body_enabled)
self.assertEqual(
prim.GetAttribute("physxRigidBody:solverPositionIterationCount").Get(),
cfg.rigid_props.solver_position_iteration_count,
)
self.assertAlmostEqual(
prim.GetAttribute("physxRigidBody:sleepThreshold").Get(), cfg.rigid_props.sleep_threshold
)
def test_spawn_cone_with_rigid_and_mass_props(self):
"""Test spawning of UsdGeom.Cone prim with rigid body and mass API."""
# Spawn cone
cfg = sim_utils.ConeCfg(
radius=1.0,
height=2.0,
rigid_props=sim_utils.RigidBodyPropertiesCfg(
rigid_body_enabled=True, solver_position_iteration_count=8, sleep_threshold=0.1
),
mass_props=sim_utils.MassPropertiesCfg(mass=1.0),
)
prim = cfg.func("/World/Cone", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Cone"))
# Check properties
prim = prim_utils.get_prim_at_path("/World/Cone")
self.assertEqual(prim.GetAttribute("physics:mass").Get(), cfg.mass_props.mass)
# check sim playing
self.sim.play()
for _ in range(10):
self.sim.step()
def test_spawn_cone_with_rigid_and_density_props(self):
"""Test spawning of UsdGeom.Cone prim with rigid body and mass API.
Note:
In this case, we specify the density instead of the mass. In that case, physics need to know
the collision shape to compute the mass. Thus, we have to set the collider properties. In
order to not have a collision shape, we disable the collision.
"""
# Spawn cone
cfg = sim_utils.ConeCfg(
radius=1.0,
height=2.0,
rigid_props=sim_utils.RigidBodyPropertiesCfg(
rigid_body_enabled=True, solver_position_iteration_count=8, sleep_threshold=0.1
),
mass_props=sim_utils.MassPropertiesCfg(density=10.0),
collision_props=sim_utils.CollisionPropertiesCfg(collision_enabled=False),
)
prim = cfg.func("/World/Cone", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Cone"))
# Check properties
prim = prim_utils.get_prim_at_path("/World/Cone")
self.assertEqual(prim.GetAttribute("physics:density").Get(), cfg.mass_props.density)
# check sim playing
self.sim.play()
for _ in range(10):
self.sim.step()
def test_spawn_cone_with_all_props(self):
"""Test spawning of UsdGeom.Cone prim with all properties."""
# Spawn cone
cfg = sim_utils.ConeCfg(
radius=1.0,
height=2.0,
mass_props=sim_utils.MassPropertiesCfg(mass=5.0),
rigid_props=sim_utils.RigidBodyPropertiesCfg(),
collision_props=sim_utils.CollisionPropertiesCfg(),
visual_material=sim_utils.materials.PreviewSurfaceCfg(diffuse_color=(0.0, 0.75, 0.5)),
physics_material=sim_utils.materials.RigidBodyMaterialCfg(),
)
prim = cfg.func("/World/Cone", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/Cone"))
self.assertTrue(prim_utils.is_prim_path_valid("/World/Cone/geometry/material"))
# Check properties
# -- rigid body
prim = prim_utils.get_prim_at_path("/World/Cone")
self.assertEqual(prim.GetAttribute("physics:rigidBodyEnabled").Get(), True)
# -- collision shape
prim = prim_utils.get_prim_at_path("/World/Cone/geometry/mesh")
self.assertEqual(prim.GetAttribute("physics:collisionEnabled").Get(), True)
# check sim playing
self.sim.play()
for _ in range(10):
self.sim.step()
"""
Cloning.
"""
def test_spawn_cone_clones_invalid_paths(self):
"""Test spawning of cone clones on invalid cloning paths."""
num_clones = 10
for i in range(num_clones):
prim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0))
# Spawn cone
cfg = sim_utils.ConeCfg(radius=1.0, height=2.0, copy_from_source=True)
# Should raise error for invalid path
with self.assertRaises(RuntimeError):
cfg.func("/World/env/env_.*/Cone", cfg)
def test_spawn_cone_clones(self):
"""Test spawning of cone clones."""
num_clones = 10
for i in range(num_clones):
prim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0))
# Spawn cone
cfg = sim_utils.ConeCfg(radius=1.0, height=2.0, copy_from_source=True)
prim = cfg.func("/World/env_.*/Cone", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertEqual(prim_utils.get_prim_path(prim), "/World/env_0/Cone")
# Find matching prims
prims = prim_utils.find_matching_prim_paths("/World/env_*/Cone")
self.assertEqual(len(prims), num_clones)
def test_spawn_cone_clone_with_all_props_global_material(self):
"""Test spawning of cone clones with global material reference."""
num_clones = 10
for i in range(num_clones):
prim_utils.create_prim(f"/World/env_{i}", "Xform", translation=(i, i, 0))
# Spawn cone
cfg = sim_utils.ConeCfg(
radius=1.0,
height=2.0,
mass_props=sim_utils.MassPropertiesCfg(mass=5.0),
rigid_props=sim_utils.RigidBodyPropertiesCfg(),
collision_props=sim_utils.CollisionPropertiesCfg(),
visual_material=sim_utils.materials.PreviewSurfaceCfg(diffuse_color=(0.0, 0.75, 0.5)),
physics_material=sim_utils.materials.RigidBodyMaterialCfg(),
visual_material_path="/Looks/visualMaterial",
physics_material_path="/Looks/physicsMaterial",
)
prim = cfg.func("/World/env_.*/Cone", cfg)
# Check validity
self.assertTrue(prim.IsValid())
self.assertEqual(prim_utils.get_prim_path(prim), "/World/env_0/Cone")
# Find matching prims
prims = prim_utils.find_matching_prim_paths("/World/env_*/Cone")
self.assertEqual(len(prims), num_clones)
# Find global materials
prims = prim_utils.find_matching_prim_paths("/Looks/visualMaterial.*")
self.assertEqual(len(prims), 1)
if __name__ == "__main__":
run_tests()
| 12,695 | Python | 40.626229 | 113 | 0.622844 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/sim/test_spawn_sensors.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import unittest
import omni.isaac.core.utils.prims as prim_utils
import omni.isaac.core.utils.stage as stage_utils
from omni.isaac.core.simulation_context import SimulationContext
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.sim.spawners.sensors.sensors import (
CUSTOM_FISHEYE_CAMERA_ATTRIBUTES,
CUSTOM_PINHOLE_CAMERA_ATTRIBUTES,
)
from omni.isaac.orbit.utils.string import to_camel_case
class TestSpawningSensors(unittest.TestCase):
"""Test fixture for checking spawning of USD sensors with different settings."""
def setUp(self) -> None:
"""Create a blank new stage for each test."""
# Create a new stage
stage_utils.create_new_stage()
# Simulation time-step
self.dt = 0.1
# Load kit helper
self.sim = SimulationContext(physics_dt=self.dt, rendering_dt=self.dt, backend="numpy")
# Wait for spawning
stage_utils.update_stage()
def tearDown(self) -> None:
"""Stops simulator after each test."""
# stop simulation
self.sim.stop()
self.sim.clear()
self.sim.clear_all_callbacks()
self.sim.clear_instance()
"""
Basic spawning.
"""
def test_spawn_pinhole_camera(self):
"""Test spawning a pinhole camera."""
cfg = sim_utils.PinholeCameraCfg(
focal_length=5.0, f_stop=10.0, clipping_range=(0.1, 1000.0), horizontal_aperture=10.0
)
prim = cfg.func("/World/pinhole_camera", cfg)
# check if the light is spawned
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/pinhole_camera"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Camera")
# validate properties on the prim
self._validate_properties_on_prim("/World/pinhole_camera", cfg, CUSTOM_PINHOLE_CAMERA_ATTRIBUTES)
def test_spawn_fisheye_camera(self):
"""Test spawning a fisheye camera."""
cfg = sim_utils.FisheyeCameraCfg(
projection_type="fisheye_equidistant",
focal_length=5.0,
f_stop=10.0,
clipping_range=(0.1, 1000.0),
horizontal_aperture=10.0,
)
# FIXME: This throws a warning. Check with Replicator team if this is expected/known.
# [omni.hydra] Camera '/World/fisheye_camera': Unknown projection type, defaulting to pinhole
prim = cfg.func("/World/fisheye_camera", cfg)
# check if the light is spawned
self.assertTrue(prim.IsValid())
self.assertTrue(prim_utils.is_prim_path_valid("/World/fisheye_camera"))
self.assertEqual(prim.GetPrimTypeInfo().GetTypeName(), "Camera")
# validate properties on the prim
self._validate_properties_on_prim("/World/fisheye_camera", cfg, CUSTOM_FISHEYE_CAMERA_ATTRIBUTES)
"""
Helper functions.
"""
def _validate_properties_on_prim(self, prim_path: str, cfg: object, custom_attr: dict):
"""Validate the properties on the prim.
Args:
prim_path: The prim name.
cfg: The configuration object.
custom_attr: The custom attributes for sensor.
"""
# delete custom attributes in the config that are not USD parameters
non_usd_cfg_param_names = ["func", "copy_from_source", "lock_camera", "visible", "semantic_tags"]
# get prim
prim = prim_utils.get_prim_at_path(prim_path)
for attr_name, attr_value in cfg.__dict__.items():
# skip names we know are not present
if attr_name in non_usd_cfg_param_names or attr_value is None:
continue
# obtain prim property name
if attr_name in custom_attr:
# check custom attributes
prim_prop_name = custom_attr[attr_name][0]
else:
# convert attribute name in prim to cfg name
prim_prop_name = to_camel_case(attr_name, to="cC")
# validate the values
self.assertAlmostEqual(prim.GetAttribute(prim_prop_name).Get(), attr_value, places=5)
if __name__ == "__main__":
run_tests()
| 4,480 | Python | 35.729508 | 105 | 0.635937 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/app/test_env_var_launch.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import os
import unittest
from omni.isaac.orbit.app import AppLauncher, run_tests
class TestAppLauncher(unittest.TestCase):
"""Test launching of the simulation app using AppLauncher."""
def test_livestream_launch_with_env_var(self):
"""Test launching with no-keyword args but environment variables."""
# manually set the settings as well to make sure they are set correctly
os.environ["LIVESTREAM"] = "1"
# everything defaults to None
app = AppLauncher().app
# import settings
import carb
# acquire settings interface
carb_settings_iface = carb.settings.get_settings()
# check settings
# -- no-gui mode
self.assertEqual(carb_settings_iface.get("/app/window/enabled"), False)
# -- livestream
self.assertEqual(carb_settings_iface.get("/app/livestream/enabled"), True)
# close the app on exit
app.close()
if __name__ == "__main__":
run_tests()
| 1,113 | Python | 27.564102 | 82 | 0.654088 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/app/test_argparser_launch.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import argparse
import unittest
from unittest import mock
from omni.isaac.orbit.app import AppLauncher, run_tests
class TestAppLauncher(unittest.TestCase):
"""Test launching of the simulation app using AppLauncher."""
@mock.patch("argparse.ArgumentParser.parse_args", return_value=argparse.Namespace(livestream=1))
def test_livestream_launch_with_argparser(self, mock_args):
"""Test launching with argparser arguments."""
# create argparser
parser = argparse.ArgumentParser()
# add app launcher arguments
AppLauncher.add_app_launcher_args(parser)
# check that argparser has the mandatory arguments
for name in AppLauncher._APPLAUNCHER_CFG_INFO:
self.assertTrue(parser._option_string_actions[f"--{name}"])
# parse args
mock_args = parser.parse_args()
# everything defaults to None
app = AppLauncher(mock_args).app
# import settings
import carb
# acquire settings interface
carb_settings_iface = carb.settings.get_settings()
# check settings
# -- no-gui mode
self.assertEqual(carb_settings_iface.get("/app/window/enabled"), False)
# -- livestream
self.assertEqual(carb_settings_iface.get("/app/livestream/enabled"), True)
# close the app on exit
app.close()
if __name__ == "__main__":
run_tests()
| 1,531 | Python | 30.916666 | 100 | 0.663619 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/app/test_kwarg_launch.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import unittest
from omni.isaac.orbit.app import AppLauncher, run_tests
class TestAppLauncher(unittest.TestCase):
"""Test launching of the simulation app using AppLauncher."""
def test_livestream_launch_with_kwarg(self):
"""Test launching with headless and livestreaming arguments."""
# everything defaults to None
app = AppLauncher(headless=True, livestream=1).app
# import settings
import carb
# acquire settings interface
carb_settings_iface = carb.settings.get_settings()
# check settings
# -- no-gui mode
self.assertEqual(carb_settings_iface.get("/app/window/enabled"), False)
# -- livestream
self.assertEqual(carb_settings_iface.get("/app/livestream/enabled"), True)
# close the app on exit
app.close()
if __name__ == "__main__":
run_tests()
| 1,004 | Python | 26.916666 | 82 | 0.659363 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/scene/check_interactive_scene.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""
This script demonstrates how to use the scene interface to quickly setup a scene with multiple
articulated robots and sensors.
"""
"""Launch Isaac Sim Simulator first."""
import argparse
from omni.isaac.orbit.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(description="This script demonstrates how to use the scene interface.")
parser.add_argument("--headless", action="store_true", default=False, help="Force display off at all times.")
parser.add_argument("--num_envs", type=int, default=2, help="Number of environments to spawn.")
args_cli = parser.parse_args()
# launch omniverse app
app_launcher = AppLauncher(headless=args_cli.headless)
simulation_app = app_launcher.app
"""Rest everything follows."""
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.assets import AssetBaseCfg
from omni.isaac.orbit.scene import InteractiveScene, InteractiveSceneCfg
from omni.isaac.orbit.sensors.ray_caster import RayCasterCfg, patterns
from omni.isaac.orbit.sim import SimulationContext
from omni.isaac.orbit.terrains import TerrainImporterCfg
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit.utils.timer import Timer
##
# Pre-defined configs
##
from omni.isaac.orbit_assets.anymal import ANYMAL_C_CFG # isort: skip
@configclass
class MySceneCfg(InteractiveSceneCfg):
"""Example scene configuration."""
# terrain - flat terrain plane
terrain = TerrainImporterCfg(
prim_path="/World/ground",
terrain_type="plane",
)
# articulation - robot 1
robot_1 = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot_1")
# articulation - robot 2
robot_2 = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot_2")
robot_2.init_state.pos = (0.0, 1.0, 0.6)
# sensor - ray caster attached to the base of robot 1 that scans the ground
height_scanner = RayCasterCfg(
prim_path="{ENV_REGEX_NS}/Robot_1/base",
offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)),
attach_yaw_only=True,
pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]),
debug_vis=True,
mesh_prim_paths=["/World/ground"],
)
# extras - light
light = AssetBaseCfg(
prim_path="/World/light",
spawn=sim_utils.DistantLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)),
init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, 500.0)),
)
def main():
"""Main function."""
# Load kit helper
sim = SimulationContext(sim_utils.SimulationCfg(dt=0.005))
# Set main camera
sim.set_camera_view(eye=[5, 5, 5], target=[0.0, 0.0, 0.0])
# Spawn things into stage
with Timer("Setup scene"):
scene = InteractiveScene(MySceneCfg(num_envs=args_cli.num_envs, env_spacing=5.0, lazy_sensor_update=False))
# Check that parsing happened as expected
assert len(scene.env_prim_paths) == args_cli.num_envs, "Number of environments does not match."
assert scene.terrain is not None, "Terrain not found."
assert len(scene.articulations) == 2, "Number of robots does not match."
assert len(scene.sensors) == 1, "Number of sensors does not match."
assert len(scene.extras) == 1, "Number of extras does not match."
# Play the simulator
with Timer("Time taken to play the simulator"):
sim.reset()
# Now we are ready!
print("[INFO]: Setup complete...")
# default joint targets
robot_1_actions = scene.articulations["robot_1"].data.default_joint_pos.clone()
robot_2_actions = scene.articulations["robot_2"].data.default_joint_pos.clone()
# Define simulation stepping
sim_dt = sim.get_physics_dt()
sim_time = 0.0
count = 0
# Simulate physics
while simulation_app.is_running():
# If simulation is stopped, then exit.
if sim.is_stopped():
break
# If simulation is paused, then skip.
if not sim.is_playing():
sim.step()
continue
# reset
if count % 50 == 0:
# reset counters
sim_time = 0.0
count = 0
# reset root state
root_state = scene.articulations["robot_1"].data.default_root_state.clone()
root_state[:, :3] += scene.env_origins
joint_pos = scene.articulations["robot_1"].data.default_joint_pos
joint_vel = scene.articulations["robot_1"].data.default_joint_vel
# -- set root state
# -- robot 1
scene.articulations["robot_1"].write_root_state_to_sim(root_state)
scene.articulations["robot_1"].write_joint_state_to_sim(joint_pos, joint_vel)
# -- robot 2
root_state[:, 1] += 1.0
scene.articulations["robot_2"].write_root_state_to_sim(root_state)
scene.articulations["robot_2"].write_joint_state_to_sim(joint_pos, joint_vel)
# reset buffers
scene.reset()
print(">>>>>>>> Reset!")
# perform this loop at policy control freq (50 Hz)
for _ in range(4):
# set joint targets
scene.articulations["robot_1"].set_joint_position_target(robot_1_actions)
scene.articulations["robot_2"].set_joint_position_target(robot_2_actions)
# write data to sim
scene.write_data_to_sim()
# perform step
sim.step()
# read data from sim
scene.update(sim_dt)
# update sim-time
sim_time += sim_dt * 4
count += 1
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 5,758 | Python | 34.331288 | 115 | 0.643279 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/scene/test_interactive_scene.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher, run_tests
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows."""
import unittest
import omni.isaac.orbit.sim as sim_utils
from omni.isaac.orbit.actuators import ImplicitActuatorCfg
from omni.isaac.orbit.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg
from omni.isaac.orbit.scene import InteractiveScene, InteractiveSceneCfg
from omni.isaac.orbit.sensors import ContactSensorCfg
from omni.isaac.orbit.sim import build_simulation_context
from omni.isaac.orbit.terrains import TerrainImporterCfg
from omni.isaac.orbit.utils import configclass
from omni.isaac.orbit.utils.assets import ISAAC_NUCLEUS_DIR
@configclass
class MySceneCfg(InteractiveSceneCfg):
"""Example scene configuration."""
# terrain - flat terrain plane
terrain = TerrainImporterCfg(
prim_path="/World/ground",
terrain_type="plane",
)
# articulation
robot = ArticulationCfg(
prim_path="/World/Robot",
spawn=sim_utils.UsdFileCfg(usd_path=f"{ISAAC_NUCLEUS_DIR}/Robots/Simple/revolute_articulation.usd"),
actuators={
"joint": ImplicitActuatorCfg(),
},
)
# rigid object
rigid_obj = RigidObjectCfg(
prim_path="/World/RigidObj",
spawn=sim_utils.CuboidCfg(
size=(0.5, 0.5, 0.5),
rigid_props=sim_utils.RigidBodyPropertiesCfg(
disable_gravity=False,
),
collision_props=sim_utils.CollisionPropertiesCfg(
collision_enabled=True,
),
),
)
# sensor
sensor = ContactSensorCfg(
prim_path="/World/Robot",
)
# extras - light
light = AssetBaseCfg(
prim_path="/World/light",
spawn=sim_utils.DistantLightCfg(),
)
class TestInteractiveScene(unittest.TestCase):
"""Test cases for InteractiveScene."""
def setUp(self) -> None:
self.devices = ["cuda:0", "cpu"]
self.sim_dt = 0.001
self.scene_cfg = MySceneCfg(num_envs=1, env_spacing=1)
def test_scene_entity_isolation(self):
"""Tests that multiple instances of InteractiveScene does not share any data.
In this test, two InteractiveScene instances are created in a loop and added to a list.
The scene at index 0 of the list will have all of its entities cleared manually, and
the test compares that the data held in the scene at index 1 remained intact.
"""
for device in self.devices:
scene_list = []
# create two InteractiveScene instances
for _ in range(2):
with build_simulation_context(device=device, dt=self.sim_dt) as _:
scene = InteractiveScene(MySceneCfg(num_envs=1, env_spacing=1))
scene_list.append(scene)
scene_0 = scene_list[0]
scene_1 = scene_list[1]
# clear entities for scene_0 - this should not affect any data in scene_1
scene_0.articulations.clear()
scene_0.rigid_objects.clear()
scene_0.sensors.clear()
scene_0.extras.clear()
# check that scene_0 and scene_1 do not share entity data via dictionary comparison
self.assertEqual(scene_0.articulations, dict())
self.assertNotEqual(scene_0.articulations, scene_1.articulations)
self.assertEqual(scene_0.rigid_objects, dict())
self.assertNotEqual(scene_0.rigid_objects, scene_1.rigid_objects)
self.assertEqual(scene_0.sensors, dict())
self.assertNotEqual(scene_0.sensors, scene_1.sensors)
self.assertEqual(scene_0.extras, dict())
self.assertNotEqual(scene_0.extras, scene_1.extras)
if __name__ == "__main__":
run_tests()
| 3,996 | Python | 34.371681 | 108 | 0.64965 |
fnuabhimanyu8713/orbit/source/extensions/omni.isaac.orbit/test/terrains/check_terrain_generator.py | # Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from omni.isaac.orbit.app import AppLauncher
# launch omniverse app
# note: we only need to do this because of `TerrainImporter` which uses Omniverse functions
app_launcher = AppLauncher(headless=True)
simulation_app = app_launcher.app
"""Rest everything follows."""
import os
import shutil
from omni.isaac.orbit.terrains.config.rough import ROUGH_TERRAINS_CFG
from omni.isaac.orbit.terrains.terrain_generator import TerrainGenerator
def main():
# Create directory to dump results
test_dir = os.path.dirname(os.path.abspath(__file__))
output_dir = os.path.join(test_dir, "output", "generator")
# remove directory
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
# create directory
os.makedirs(output_dir, exist_ok=True)
# modify the config to cache
ROUGH_TERRAINS_CFG.use_cache = True
ROUGH_TERRAINS_CFG.cache_dir = output_dir
ROUGH_TERRAINS_CFG.curriculum = False
# generate terrains
terrain_generator = TerrainGenerator(cfg=ROUGH_TERRAINS_CFG) # noqa: F841
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
| 1,313 | Python | 27.565217 | 91 | 0.715918 |
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