From OmniIsaacGymEnvs#
OmniIsaacGymEnvs was a reinforcement learning framework using the Isaac Sim platform. Features from OmniIsaacGymEnvs have been integrated into the Isaac Lab framework. We have updated OmniIsaacGymEnvs to Isaac Sim version 4.0.0 to support the migration process to Isaac Lab. Moving forward, OmniIsaacGymEnvs will be deprecated and future development will continue in Isaac Lab.
Note
The following changes are with respect to Isaac Lab 1.0 release. Please refer to the release notes for any changes in the future releases.
Task Config Setup#
In OmniIsaacGymEnvs, task config files were defined in .yaml
format. With Isaac Lab, configs are now specified
using a specialized Python class configclass
. The
configclass
module provides a wrapper on top of Python’s dataclasses
module.
Each environment should specify its own config class annotated by @configclass
that inherits from the
DirectRLEnvCfg
class, which can include simulation parameters, environment scene parameters,
robot parameters, and task-specific parameters.
Below is an example skeleton of a task config class:
from omni.isaac.lab.envs import DirectRLEnvCfg
from omni.isaac.lab.scene import InteractiveSceneCfg
from omni.isaac.lab.sim import SimulationCfg
@configclass
class MyEnvCfg(DirectRLEnvCfg):
# simulation
sim: SimulationCfg = SimulationCfg()
# robot
robot_cfg: ArticulationCfg = ArticulationCfg()
# scene
scene: InteractiveSceneCfg = InteractiveSceneCfg()
# env
decimation = 2
episode_length_s = 5.0
action_space = 1
observation_space = 4
state_space = 0
# task-specific parameters
...
Simulation Config#
Simulation related parameters are defined as part of the SimulationCfg
class,
which is a configclass
module that holds simulation parameters such as dt
,
device
, and gravity
. Each task config must have a variable named sim
defined that holds the type
SimulationCfg
.
Simulation parameters for articulations and rigid bodies such as num_position_iterations
, num_velocity_iterations
,
contact_offset
, rest_offset
, bounce_threshold_velocity
, max_depenetration_velocity
can all
be specified on a per-actor basis in the config class for each individual articulation and rigid body.
When running simulation on the GPU, buffers in PhysX require pre-allocation for computing and storing
information such as contacts, collisions and aggregate pairs. These buffers may need to be adjusted
depending on the complexity of the environment, the number of expected contacts and collisions,
and the number of actors in the environment. The PhysxCfg
class provides access
for setting the GPU buffer dimensions.
# OmniIsaacGymEnvs
sim:
dt: 0.0083 # 1/120 s
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
gravity: [0.0, 0.0, -9.81]
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${contains:"cuda",${....sim_device}}
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 100.0
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 81920
gpu_found_lost_pairs_capacity: 1024
gpu_found_lost_aggregate_pairs_capacity: 262144
gpu_total_aggregate_pairs_capacity: 1024
gpu_heap_capacity: 67108864
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
|
# IsaacLab
sim: SimulationCfg = SimulationCfg(
device = "cuda:0" # can be "cpu", "cuda", "cuda:<device_id>"
dt=1 / 120,
# use_gpu_pipeline is deduced from the device
use_fabric=True,
enable_scene_query_support=False,
disable_contact_processing=False,
gravity=(0.0, 0.0, -9.81),
physics_material=RigidBodyMaterialCfg(
static_friction=1.0,
dynamic_friction=1.0,
restitution=0.0
)
physx: PhysxCfg = PhysxCfg(
# worker_thread_count is no longer needed
solver_type=1,
# use_gpu is deduced from the device
max_position_iteration_count=4,
max_velocity_iteration_count=0,
# moved to actor config
# moved to actor config
bounce_threshold_velocity=0.2,
friction_offset_threshold=0.04,
friction_correlation_distance=0.025,
# enable_sleeping is no longer needed
enable_stabilization=True,
# moved to RigidBodyPropertiesCfg
gpu_max_rigid_contact_count=2**23,
gpu_max_rigid_patch_count=5 * 2**15,
gpu_found_lost_pairs_capacity=2**21,
gpu_found_lost_aggregate_pairs_capacity=2**25,
gpu_total_aggregate_pairs_capacity=2**21,
gpu_heap_capacity=2**26,
gpu_temp_buffer_capacity=2**24,
gpu_max_num_partitions=8,
gpu_max_soft_body_contacts=2**20,
gpu_max_particle_contacts=2**20,
)
)
|
Parameters such as add_ground_plane
and add_distant_light
are now part of the task logic when creating the scene.
enable_cameras
is now a command line argument --enable_cameras
that can be passed directly to the training script.
Scene Config#
The InteractiveSceneCfg
class can be used to specify parameters related to the scene,
such as the number of environments and the spacing between environments. Each task config must have a variable named
scene
defined that holds the type InteractiveSceneCfg
.
# OmniIsaacGymEnvs
env:
numEnvs: ${resolve_default:512,${...num_envs}}
envSpacing: 4.0
|
# IsaacLab
scene: InteractiveSceneCfg = InteractiveSceneCfg(
num_envs=512,
env_spacing=4.0)
|
Task Config#
Each environment should specify its own config class that holds task specific parameters, such as the dimensions of the observation and action buffers. Reward term scaling parameters can also be specified in the config class.
In Isaac Lab, the controlFrequencyInv
parameter has been renamed to decimation
,
which must be specified as a parameter in the config class.
In addition, the maximum episode length parameter (now episode_length_s
) is in seconds instead of steps as it was
in OmniIsaacGymEnvs. To convert between step count to seconds, use the equation:
episode_length_s = dt * decimation * num_steps
.
The following parameters must be set for each environment config:
decimation = 2
episode_length_s = 5.0
action_space = 1
observation_space = 4
state_space = 0
RL Config Setup#
RL config files for the rl_games library can continue to be defined in .yaml
files in Isaac Lab.
Most of the content of the config file can be copied directly from OmniIsaacGymEnvs.
Note that in Isaac Lab, we do not use hydra to resolve relative paths in config files.
Please replace any relative paths such as ${....device}
with the actual values of the parameters.
Additionally, the observation and action clip ranges have been moved to the RL config file.
For any clipObservations
and clipActions
parameters that were defined in the IsaacGymEnvs task config file,
they should be moved to the RL config file in Isaac Lab.
IsaacGymEnvs Task Config |
Isaac Lab RL Config |
# OmniIsaacGymEnvs
env:
clipObservations: 5.0
clipActions: 1.0
|
# IsaacLab
params:
env:
clip_observations: 5.0
clip_actions: 1.0
|
Environment Creation#
In OmniIsaacGymEnvs, environment creation generally happened in the set_up_scene()
API,
which involved creating the initial environment, cloning the environment, filtering collisions,
adding the ground plane and lights, and creating the View
classes for the actors.
Similar functionality is performed in Isaac Lab in the _setup_scene()
API.
The main difference is that the base class _setup_scene()
no longer performs operations for
cloning the environment and adding ground plane and lights. Instead, these operations
should now be implemented in individual tasks’ _setup_scene
implementations to provide more
flexibility around the scene setup process.
Also note that by defining an Articulation
or RigidObject
object, the actors will be
added to the scene by parsing the spawn
parameter in the actor config and a View
class
will automatically be created for the actor. This avoids the need to separately define an
ArticulationView
or RigidPrimView
object for the actors.
OmniIsaacGymEnvs |
Isaac Lab |
def set_up_scene(self, scene) -> None:
self.get_cartpole()
super().set_up_scene(scene)
self._cartpoles = ArticulationView(
prim_paths_expr="/World/envs/.*/Cartpole",
name="cartpole_view", reset_xform_properties=False
)
scene.add(self._cartpoles)
|
def _setup_scene(self):
self.cartpole = Articulation(self.cfg.robot_cfg)
# add ground plane
spawn_ground_plane(prim_path="/World/ground", cfg=GroundPlaneCfg()
# clone, filter, and replicate
self.scene.clone_environments(copy_from_source=False)
self.scene.filter_collisions(global_prim_paths=[])
# add articulation to scene
self.scene.articulations["cartpole"] = self.cartpole
# add lights
light_cfg = sim_utils.DomeLightCfg(intensity=2000.0)
light_cfg.func("/World/Light", light_cfg)
|
Ground Plane#
In addition to the above example, more sophisticated ground planes can be defined using the TerrainImporterCfg
class.
from omni.isaac.lab.terrains import TerrainImporterCfg
terrain = TerrainImporterCfg(
prim_path="/World/ground",
terrain_type="plane",
collision_group=-1,
physics_material=sim_utils.RigidBodyMaterialCfg(
friction_combine_mode="multiply",
restitution_combine_mode="multiply",
static_friction=1.0,
dynamic_friction=1.0,
restitution=0.0,
),
)
The terrain can then be added to the scene in _setup_scene(self)
by referencing the TerrainImporterCfg
object:
Actors#
In Isaac Lab, each Articulation and Rigid Body actor can have its own config class. The
ArticulationCfg
class can be used to define parameters for articulation actors,
including file path, simulation parameters, actuator properties, and initial states.
Within the ArticulationCfg
, the spawn
attribute can be used to add the robot to the scene
by specifying the path to the robot file. In addition, the RigidBodyPropertiesCfg
class can be used to specify simulation properties for the rigid bodies in the articulation. Similarly, the
ArticulationRootPropertiesCfg
class can be used to specify simulation properties
for the articulation. The joint properties are now specified as part of the actuators
dictionary using
ImplicitActuatorCfg
. Joints with the same properties can be grouped into regex expressions or
provided as a list of names or expressions.
Actors are added to the scene by simply calling self.cartpole = Articulation(self.cfg.robot_cfg)
, where
self.cfg.robot_cfg
is an ArticulationCfg
object. Once initialized, they should also be added
to the InteractiveScene
by calling self.scene.articulations["cartpole"] = self.cartpole
so that
the InteractiveScene
can traverse through actors in the scene for writing values to the simulation
and resetting.
Accessing States from Simulation#
APIs for accessing physics states in Isaac Lab require the creation of an Articulation
or
RigidObject
object. Multiple objects can be initialized for different articulations or rigid bodies
in the scene by defining corresponding ArticulationCfg
or RigidObjectCfg
config,
as outlined in the section above. This replaces the previously used ArticulationView
and omni.isaac.core.prims.RigidPrimView
classes used in OmniIsaacGymEnvs.
However, functionality between the classes are similar:
OmniIsaacGymEnvs |
Isaac Lab |
dof_pos = self._cartpoles.get_joint_positions(clone=False)
dof_vel = self._cartpoles.get_joint_velocities(clone=False)
|
self.joint_pos = self._robot.data.joint_pos
self.joint_vel = self._robot.data.joint_vel
|
In Isaac Lab, Articulation
and RigidObject
classes both have a data
class.
The data classes (ArticulationData
and RigidObjectData
) contain
buffers that hold the states for the articulation and rigid objects and provide
a more performant way of retrieving states from the actors.
Apart from some renamings of APIs, setting states for actors can also be performed similarly between OmniIsaacGymEnvs and Isaac Lab.
OmniIsaacGymEnvs |
Isaac Lab |
indices = env_ids.to(dtype=torch.int32)
self._cartpoles.set_joint_positions(dof_pos, indices=indices)
self._cartpoles.set_joint_velocities(dof_vel, indices=indices)
|
self._robot.write_joint_state_to_sim(joint_pos, joint_vel,
joint_ids, env_ids)
|
In Isaac Lab, root_pose
and root_velocity
have been combined into single buffers and no longer split between
root_position
, root_orientation
, root_linear_velocity
and root_angular_velocity
.
Creating a New Environment#
Each environment in Isaac Lab should be in its own directory following this structure:
my_environment/
- agents/
- __init__.py
- rl_games_ppo_cfg.py
- __init__.py
my_env.py
my_environment
is the root directory of the task.my_environment/agents
is the directory containing all RL config files for the task. Isaac Lab supports multiple RL libraries that can each have its own individual config file.my_environment/__init__.py
is the main file that registers the environment with the Gymnasium interface. This allows the training and inferencing scripts to find the task by its name. The content of this file should be as follow:import gymnasium as gym from . import agents from .cartpole_env import CartpoleEnv, CartpoleEnvCfg ## # Register Gym environments. ## gym.register( id="Isaac-Cartpole-Direct-v0", entry_point="omni.isaac.lab_tasks.direct_workflow.cartpole:CartpoleEnv", disable_env_checker=True, kwargs={ "env_cfg_entry_point": CartpoleEnvCfg, "rl_games_cfg_entry_point": f"{agents.__name__}:rl_games_ppo_cfg.yaml" }, )
my_environment/my_env.py
is the main python script that implements the task logic and task config class for the environment.
Task Logic#
The post_reset
API in OmniIsaacGymEnvs is no longer required in Isaac Lab. Everything that was previously
done in post_reset
can be done in the __init__
method after executing the base class’s
__init__
. At this point, simulation has already started.
In OmniIsaacGymEnvs, due to limitations of the GPU APIs, resets could not be performed based on states of the current
step. Instead, resets have to be performed at the beginning of the next time step.
This restriction has been eliminated in Isaac Lab, and thus, tasks follow the correct workflow of applying actions,
stepping simulation, collecting states, computing dones, calculating rewards, performing resets, and finally computing
observations. This workflow is done automatically by the framework such that a post_physics_step
API is not
required in the task. However, individual tasks can override the step()
API to control the workflow.
In Isaac Lab, we also separate the pre_physics_step
API for processing actions from the policy with
the apply_action
API, which sets the actions into the simulation. This provides more flexibility in controlling
when actions should be written to simulation when decimation
is used.
The pre_physics_step
method will be called once per step before stepping simulation.
The apply_actions
method will be called decimation
number of times for each RL step,
once before each simulation step call.
The ordering of the calls are as follow:
OmniIsaacGymEnvs |
Isaac Lab |
pre_physics_step
|-- reset_idx()
|-- apply_action
post_physics_step
|-- get_observations()
|-- calculate_metrics()
|-- is_done()
|
pre_physics_step
|-- _pre_physics_step(action)
|-- _apply_action()
post_physics_step
|-- _get_dones()
|-- _get_rewards()
|-- _reset_idx()
|-- _get_observations()
|
With this approach, resets are performed based on actions from the current step instead of the previous step. Observations will also be computed with the correct states after resets.
We have also performed some renamings of APIs:
set_up_scene(self, scene)
–>_setup_scene(self)
post_reset(self)
–>__init__(...)
pre_physics_step(self, actions)
–>_pre_physics_step(self, actions)
and_apply_action(self)
reset_idx(self, env_ids)
–>_reset_idx(self, env_ids)
get_observations(self)
–>_get_observations(self)
-_get_observations()
should now return a dictionary{"policy": obs}
calculate_metrics(self)
–>_get_rewards(self)
-_get_rewards()
should now return the reward bufferis_done(self)
–>_get_dones(self)
-_get_dones()
should now return 2 buffers:reset
andtime_out
buffers
Putting It All Together#
The Cartpole environment is shown here in completion to fully show the comparison between the OmniIsaacGymEnvs implementation and the Isaac Lab implementation.
Task Config#
Task config in Isaac Lab can be split into the main task configuration class and individual config objects for the actors.
OmniIsaacGymEnvs |
Isaac Lab |
# used to create the object
name: Cartpole
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:512,${...num_envs}}
envSpacing: 4.0
resetDist: 3.0
maxEffort: 400.0
clipObservations: 5.0
clipActions: 1.0
controlFrequencyInv: 2 # 60 Hz
sim:
dt: 0.0083 # 1/120 s
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to...
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 100.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 81920
gpu_found_lost_pairs_capacity: 1024
gpu_found_lost_aggregate_pairs_capacity: 262144
gpu_total_aggregate_pairs_capacity: 1024
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 67108864
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
Cartpole:
override_usd_defaults: False
enable_self_collisions: False
enable_gyroscopic_forces: True
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
density: -1
max_depenetration_velocity: 100.0
contact_offset: 0.02
rest_offset: 0.001
|
@configclass
class CartpoleEnvCfg(DirectRLEnvCfg):
# simulation
sim: SimulationCfg = SimulationCfg(dt=1 / 120)
# robot
robot_cfg: ArticulationCfg = CARTPOLE_CFG.replace(
prim_path="/World/envs/env_.*/Robot")
cart_dof_name = "slider_to_cart"
pole_dof_name = "cart_to_pole"
# scene
scene: InteractiveSceneCfg = InteractiveSceneCfg(
num_envs=4096, env_spacing=4.0, replicate_physics=True)
# env
decimation = 2
episode_length_s = 5.0
action_scale = 100.0 # [N]
action_space = 1
observation_space = 4
state_space = 0
# reset
max_cart_pos = 3.0
initial_pole_angle_range = [-0.25, 0.25]
# reward scales
rew_scale_alive = 1.0
rew_scale_terminated = -2.0
rew_scale_pole_pos = -1.0
rew_scale_cart_vel = -0.01
rew_scale_pole_vel = -0.005
CARTPOLE_CFG = ArticulationCfg(
spawn=sim_utils.UsdFileCfg(
usd_path=f"{ISAACLAB_NUCLEUS_DIR}/.../cartpole.usd",
rigid_props=sim_utils.RigidBodyPropertiesCfg(
rigid_body_enabled=True,
max_linear_velocity=1000.0,
max_angular_velocity=1000.0,
max_depenetration_velocity=100.0,
enable_gyroscopic_forces=True,
),
articulation_props=sim_utils.ArticulationRootPropertiesCfg(
enabled_self_collisions=False,
solver_position_iteration_count=4,
solver_velocity_iteration_count=0,
sleep_threshold=0.005,
stabilization_threshold=0.001,
),
),
init_state=ArticulationCfg.InitialStateCfg(
pos=(0.0, 0.0, 2.0),
joint_pos={"slider_to_cart": 0.0, "cart_to_pole": 0.0}
),
actuators={
"cart_actuator": ImplicitActuatorCfg(
joint_names_expr=["slider_to_cart"],
effort_limit=400.0,
velocity_limit=100.0,
stiffness=0.0,
damping=10.0,
),
"pole_actuator": ImplicitActuatorCfg(
joint_names_expr=["cart_to_pole"], effort_limit=400.0,
velocity_limit=100.0, stiffness=0.0, damping=0.0
),
},
)
|
Task Setup#
The post_reset
API in OmniIsaacGymEnvs is no longer required in Isaac Lab.
Everything that was previously done in post_reset
can be done in the __init__
method after
executing the base class’s __init__
. At this point, simulation has already started.
OmniIsaacGymEnvs |
Isaac Lab |
class CartpoleTask(RLTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
self._max_episode_length = 500
self._num_observations = 4
self._num_actions = 1
RLTask.__init__(self, name, env)
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.
task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._cartpole_positions = torch.tensor([0.0, 0.0, 2.0])
self._reset_dist = self._task_cfg["env"]["resetDist"]
self._max_push_effort = self._task_cfg["env"]["maxEffort"]
def post_reset(self):
self._cart_dof_idx = self._cartpoles.get_dof_index(
"cartJoint")
self._pole_dof_idx = self._cartpoles.get_dof_index(
"poleJoint")
# randomize all envs
indices = torch.arange(
self._cartpoles.count, dtype=torch.int64,
device=self._device)
self.reset_idx(indices)
|
class CartpoleEnv(DirectRLEnv):
cfg: CartpoleEnvCfg
def __init__(self, cfg: CartpoleEnvCfg,
render_mode: str | None = None, **kwargs):
super().__init__(cfg, render_mode, **kwargs)
self._cart_dof_idx, _ = self.cartpole.find_joints(
self.cfg.cart_dof_name)
self._pole_dof_idx, _ = self.cartpole.find_joints(
self.cfg.pole_dof_name)
self.action_scale=self.cfg.action_scale
self.joint_pos = self.cartpole.data.joint_pos
self.joint_vel = self.cartpole.data.joint_vel
|
Scene Setup#
The set_up_scene
method in OmniIsaacGymEnvs has been replaced by the _setup_scene
API in the task class in
Isaac Lab. Additionally, scene cloning and collision filtering have been provided as APIs for the task class to
call when necessary. Similarly, adding ground plane and lights should also be taken care of in the task class.
Adding actors to the scene has been replaced by self.scene.articulations["cartpole"] = self.cartpole
.
OmniIsaacGymEnvs |
Isaac Lab |
def set_up_scene(self, scene) -> None:
self.get_cartpole()
super().set_up_scene(scene)
self._cartpoles = ArticulationView(
prim_paths_expr="/World/envs/.*/Cartpole",
name="cartpole_view",
reset_xform_properties=False
)
scene.add(self._cartpoles)
return
def get_cartpole(self):
cartpole = Cartpole(
prim_path=self.default_zero_env_path+"/Cartpole",
name="Cartpole",
translation=self._cartpole_positions
)
# applies articulation settings from the
# task configuration yaml file
self._sim_config.apply_articulation_settings(
"Cartpole", get_prim_at_path(cartpole.prim_path),
self._sim_config.parse_actor_config("Cartpole")
)
|
def _setup_scene(self):
self.cartpole = Articulation(self.cfg.robot_cfg)
# add ground plane
spawn_ground_plane(prim_path="/World/ground",
cfg=GroundPlaneCfg())
# clone, filter, and replicate
self.scene.clone_environments(
copy_from_source=False)
self.scene.filter_collisions(
global_prim_paths=[])
# add articulation to scene
self.scene.articulations["cartpole"] = self.cartpole
# add lights
light_cfg = sim_utils.DomeLightCfg(
intensity=2000.0, color=(0.75, 0.75, 0.75))
light_cfg.func("/World/Light", light_cfg)
|
Pre-Physics Step#
Note that resets are no longer performed in the pre_physics_step
API. In addition, the separation of the
_pre_physics_step
and _apply_action
methods allow for more flexibility in processing the action buffer
and setting actions into simulation.
OmniIsaacGymEnvs |
IsaacLab |
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(
as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
actions = actions.to(self._device)
forces = torch.zeros((self._cartpoles.count,
self._cartpoles.num_dof),
dtype=torch.float32, device=self._device)
forces[:, self._cart_dof_idx] =
self._max_push_effort * actions[:, 0]
indices = torch.arange(self._cartpoles.count,
dtype=torch.int32, device=self._device)
self._cartpoles.set_joint_efforts(
forces, indices=indices)
|
def _pre_physics_step(self,
actions: torch.Tensor) -> None:
self.actions = self.action_scale * actions
def _apply_action(self) -> None:
self.cartpole.set_joint_effort_target(
self.actions, joint_ids=self._cart_dof_idx)
|
Dones and Resets#
In Isaac Lab, the dones
are computed in the _get_dones()
method and should return two variables: resets
and
time_out
. The _reset_idx()
method is also called after stepping simulation instead of before, as it was done in
OmniIsaacGymEnvs. The progress_buf
tensor has been renamed to episode_length_buf
in Isaac Lab and the
bookkeeping is now done automatically by the framework. Task implementations no longer need to increment or
reset the episode_length_buf
buffer.
OmniIsaacGymEnvs |
Isaac Lab |
def is_done(self) -> None:
resets = torch.where(
torch.abs(self.cart_pos) > self._reset_dist, 1, 0)
resets = torch.where(
torch.abs(self.pole_pos) > math.pi / 2, 1, resets)
resets = torch.where(
self.progress_buf >= self._max_episode_length, 1, resets)
self.reset_buf[:] = resets
def reset_idx(self, env_ids):
num_resets = len(env_ids)
# randomize DOF positions
dof_pos = torch.zeros((num_resets, self._cartpoles.num_dof),
device=self._device)
dof_pos[:, self._cart_dof_idx] = 1.0 * (
1.0 - 2.0 * torch.rand(num_resets, device=self._device))
dof_pos[:, self._pole_dof_idx] = 0.125 * math.pi * (
1.0 - 2.0 * torch.rand(num_resets, device=self._device))
# randomize DOF velocities
dof_vel = torch.zeros((num_resets, self._cartpoles.num_dof),
device=self._device)
dof_vel[:, self._cart_dof_idx] = 0.5 * (
1.0 - 2.0 * torch.rand(num_resets, device=self._device))
dof_vel[:, self._pole_dof_idx] = 0.25 * math.pi * (
1.0 - 2.0 * torch.rand(num_resets, device=self._device))
# apply resets
indices = env_ids.to(dtype=torch.int32)
self._cartpoles.set_joint_positions(dof_pos, indices=indices)
self._cartpoles.set_joint_velocities(dof_vel, indices=indices)
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
|
def _get_dones(self) -> tuple[torch.Tensor, torch.Tensor]:
self.joint_pos = self.cartpole.data.joint_pos
self.joint_vel = self.cartpole.data.joint_vel
time_out = self.episode_length_buf >= self.max_episode_length - 1
out_of_bounds = torch.any(torch.abs(
self.joint_pos[:, self._cart_dof_idx]) > self.cfg.max_cart_pos,
dim=1)
out_of_bounds = out_of_bounds | torch.any(
torch.abs(self.joint_pos[:, self._pole_dof_idx]) > math.pi / 2,
dim=1)
return out_of_bounds, time_out
def _reset_idx(self, env_ids: Sequence[int] | None):
if env_ids is None:
env_ids = self.cartpole._ALL_INDICES
super()._reset_idx(env_ids)
joint_pos = self.cartpole.data.default_joint_pos[env_ids]
joint_pos[:, self._pole_dof_idx] += sample_uniform(
self.cfg.initial_pole_angle_range[0] * math.pi,
self.cfg.initial_pole_angle_range[1] * math.pi,
joint_pos[:, self._pole_dof_idx].shape,
joint_pos.device,
)
joint_vel = self.cartpole.data.default_joint_vel[env_ids]
default_root_state = self.cartpole.data.default_root_state[env_ids]
default_root_state[:, :3] += self.scene.env_origins[env_ids]
self.joint_pos[env_ids] = joint_pos
self.joint_vel[env_ids] = joint_vel
self.cartpole.write_root_pose_to_sim(
default_root_state[:, :7], env_ids)
self.cartpole.write_root_velocity_to_sim(
default_root_state[:, 7:], env_ids)
self.cartpole.write_joint_state_to_sim(
joint_pos, joint_vel, None, env_ids)
|
Rewards#
In Isaac Lab, rewards are implemented in the _get_rewards
API and should return the reward buffer instead of assigning
it directly to self.rew_buf
. Computation in the reward function can also be performed using pytorch jit
through defining functions with the @torch.jit.script
annotation.
OmniIsaacGymEnvs |
Isaac Lab |
def calculate_metrics(self) -> None:
reward = (1.0 - self.pole_pos * self.pole_pos
- 0.01 * torch.abs(self.cart_vel) - 0.005
* torch.abs(self.pole_vel))
reward = torch.where(
torch.abs(self.cart_pos) > self._reset_dist,
torch.ones_like(reward) * -2.0, reward)
reward = torch.where(
torch.abs(self.pole_pos) > np.pi / 2,
torch.ones_like(reward) * -2.0, reward)
self.rew_buf[:] = reward
|
def _get_rewards(self) -> torch.Tensor:
total_reward = compute_rewards(
self.cfg.rew_scale_alive,
self.cfg.rew_scale_terminated,
self.cfg.rew_scale_pole_pos,
self.cfg.rew_scale_cart_vel,
self.cfg.rew_scale_pole_vel,
self.joint_pos[:, self._pole_dof_idx[0]],
self.joint_vel[:, self._pole_dof_idx[0]],
self.joint_pos[:, self._cart_dof_idx[0]],
self.joint_vel[:, self._cart_dof_idx[0]],
self.reset_terminated,
)
return total_reward
@torch.jit.script
def compute_rewards(
rew_scale_alive: float,
rew_scale_terminated: float,
rew_scale_pole_pos: float,
rew_scale_cart_vel: float,
rew_scale_pole_vel: float,
pole_pos: torch.Tensor,
pole_vel: torch.Tensor,
cart_pos: torch.Tensor,
cart_vel: torch.Tensor,
reset_terminated: torch.Tensor,
):
rew_alive = rew_scale_alive * (1.0 - reset_terminated.float())
rew_termination = rew_scale_terminated * reset_terminated.float()
rew_pole_pos = rew_scale_pole_pos * torch.sum(
torch.square(pole_pos), dim=-1)
rew_cart_vel = rew_scale_cart_vel * torch.sum(
torch.abs(cart_vel), dim=-1)
rew_pole_vel = rew_scale_pole_vel * torch.sum(
torch.abs(pole_vel), dim=-1)
total_reward = (rew_alive + rew_termination
+ rew_pole_pos + rew_cart_vel + rew_pole_vel)
return total_reward
|
Observations#
In Isaac Lab, the _get_observations()
API must return a dictionary with the key policy
that has the observation buffer as the value.
When working with asymmetric actor-critic states, the states for the critic should have the key critic
and be returned
with the observation buffer in the same dictionary.
OmniIsaacGymEnvs |
Isaac Lab |
def get_observations(self) -> dict:
dof_pos = self._cartpoles.get_joint_positions(clone=False)
dof_vel = self._cartpoles.get_joint_velocities(clone=False)
self.cart_pos = dof_pos[:, self._cart_dof_idx]
self.cart_vel = dof_vel[:, self._cart_dof_idx]
self.pole_pos = dof_pos[:, self._pole_dof_idx]
self.pole_vel = dof_vel[:, self._pole_dof_idx]
self.obs_buf[:, 0] = self.cart_pos
self.obs_buf[:, 1] = self.cart_vel
self.obs_buf[:, 2] = self.pole_pos
self.obs_buf[:, 3] = self.pole_vel
observations = {self._cartpoles.name:
{"obs_buf": self.obs_buf}}
return observations
|
def _get_observations(self) -> dict:
obs = torch.cat(
(
self.joint_pos[:, self._pole_dof_idx[0]],
self.joint_vel[:, self._pole_dof_idx[0]],
self.joint_pos[:, self._cart_dof_idx[0]],
self.joint_vel[:, self._cart_dof_idx[0]],
),
dim=-1,
)
observations = {"policy": obs}
return observations
|
Domain Randomization#
In OmniIsaacGymEnvs, domain randomization was specified through the task .yaml
config file.
In Isaac Lab, the domain randomization configuration uses the configclass
module
to specify a configuration class consisting of EventTermCfg
variables.
Below is an example of a configuration class for domain randomization:
@configclass
class EventCfg:
robot_physics_material = EventTerm(
func=mdp.randomize_rigid_body_material,
mode="reset",
params={
"asset_cfg": SceneEntityCfg("robot", body_names=".*"),
"static_friction_range": (0.7, 1.3),
"dynamic_friction_range": (1.0, 1.0),
"restitution_range": (1.0, 1.0),
"num_buckets": 250,
},
)
robot_joint_stiffness_and_damping = EventTerm(
func=mdp.randomize_actuator_gains,
mode="reset",
params={
"asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
"stiffness_distribution_params": (0.75, 1.5),
"damping_distribution_params": (0.3, 3.0),
"operation": "scale",
"distribution": "log_uniform",
},
)
reset_gravity = EventTerm(
func=mdp.randomize_physics_scene_gravity,
mode="interval",
is_global_time=True,
interval_range_s=(36.0, 36.0), # time_s = num_steps * (decimation * dt)
params={
"gravity_distribution_params": ([0.0, 0.0, 0.0], [0.0, 0.0, 0.4]),
"operation": "add",
"distribution": "gaussian",
},
)
Each EventTerm
object is of the EventTermCfg
class and takes in a func
parameter
for specifying the function to call during randomization, a mode
parameter, which can be startup
,
reset
or interval
. THe params
dictionary should provide the necessary arguments to the
function that is specified in the func
parameter.
Functions specified as func
for the EventTerm
can be found in the events
module.
Note that as part of the "asset_cfg": SceneEntityCfg("robot", body_names=".*")
parameter, the name of
the actor "robot"
is provided, along with the body or joint names specified as a regex expression,
which will be the actors and bodies/joints that will have randomization applied.
One difference with OmniIsaacGymEnvs is that interval
randomization is now specified as seconds instead of
steps. When mode="interval"
, the interval_range_s
parameter must also be provided, which specifies
the range of seconds for which randomization should be applied. This range will then be randomized to
determine a specific time in seconds when the next randomization will occur for the term.
To convert between steps to seconds, use the equation time_s = num_steps * (decimation * dt)
.
Similar to OmniIsaacGymEnvs, randomization APIs are available for randomizing articulation properties, such as joint stiffness and damping, joint limits, rigid body materials, fixed tendon properties, as well as rigid body properties, such as mass and rigid body materials. Randomization of the physics scene gravity is also supported. Note that randomization of scale is current not supported in Isaac Lab. To randomize scale, please set up the scene in a way where each environment holds the actor at a different scale.
Once the configclass
for the randomization terms have been set up, the class must be added
to the base config class for the task and be assigned to the variable events
.
@configclass
class MyTaskConfig:
events: EventCfg = EventCfg()
Action and Observation Noise#
Actions and observation noise can also be added using the configclass
module.
Action and observation noise configs must be added to the main task config using the
action_noise_model
and observation_noise_model
variables:
@configclass
class MyTaskConfig:
# at every time-step add gaussian noise + bias. The bias is a gaussian sampled at reset
action_noise_model: NoiseModelWithAdditiveBiasCfg = NoiseModelWithAdditiveBiasCfg(
noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.05, operation="add"),
bias_noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.015, operation="abs"),
)
# at every time-step add gaussian noise + bias. The bias is a gaussian sampled at reset
observation_noise_model: NoiseModelWithAdditiveBiasCfg = NoiseModelWithAdditiveBiasCfg(
noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.002, operation="add"),
bias_noise_cfg=GaussianNoiseCfg(mean=0.0, std=0.0001, operation="abs"),
)
NoiseModelWithAdditiveBiasCfg
can be used to sample both uncorrelated noise
per step as well as correlated noise that is re-sampled at reset time.
The noise_cfg
term specifies the Gaussian distribution that will be sampled at each
step for all environments. This noise will be added to the corresponding actions and
observations buffers at every step.
The bias_noise_cfg
term specifies the Gaussian distribution for the correlated noise
that will be sampled at reset time for the environments being reset. The same noise
will be applied each step for the remaining of the episode for the environments and
resampled at the next reset.
This replaces the following setup in OmniIsaacGymEnvs:
domain_randomization:
randomize: True
randomization_params:
observations:
on_reset:
operation: "additive"
distribution: "gaussian"
distribution_parameters: [0, .0001]
on_interval:
frequency_interval: 1
operation: "additive"
distribution: "gaussian"
distribution_parameters: [0, .002]
actions:
on_reset:
operation: "additive"
distribution: "gaussian"
distribution_parameters: [0, 0.015]
on_interval:
frequency_interval: 1
operation: "additive"
distribution: "gaussian"
distribution_parameters: [0., 0.05]
Launching Training#
To launch a training in Isaac Lab, use the command:
python source/standalone/workflows/rl_games/train.py --task=Isaac-Cartpole-Direct-v0 --headless
Launching Inferencing#
To launch inferencing in Isaac Lab, use the command:
python source/standalone/workflows/rl_games/play.py --task=Isaac-Cartpole-Direct-v0 --num_envs=25 --checkpoint=<path/to/checkpoint>