IsaacLab/docs/source/overview/environments.rst at fb5e3c56fb8e0bb5fa4ee855ef65dfa6fe00be84 · mmichelis/IsaacLab

Available Environments

The following lists comprises of all the RL and IL tasks implementations that are available in Isaac Lab. While we try to keep this list up-to-date, you can always get the latest list of environments by running the following command:

.. tab-set::
   :sync-group: os

   .. tab-item:: :icon:`fa-brands fa-linux` Linux
      :sync: linux

      .. note::
         Use ``--keyword <search_term>`` (optional) to filter environments by keyword.

      .. tab-set::

         .. tab-item:: uv (Recommended)

            .. code:: bash

               uv run python scripts/environments/list_envs.py --keyword <search_term>

         .. tab-item:: isaaclab.sh / isaaclab.bat

            .. code:: bash

               ./isaaclab.sh -p scripts/environments/list_envs.py --keyword <search_term>

   .. tab-item:: :icon:`fa-brands fa-windows` Windows
      :sync: windows

      .. note::
         Use ``--keyword <search_term>`` (optional) to filter environments by keyword.

      .. code:: batch

         isaaclab.bat -p scripts\environments\list_envs.py --keyword <search_term>

To also see the available presets for each environment, pass --show_presets:

.. tab-set::
   :sync-group: os

   .. tab-item:: :icon:`fa-brands fa-linux` Linux
      :sync: linux

      .. tab-set::

         .. tab-item:: uv (Recommended)

            .. code:: bash

               uv run python scripts/environments/list_envs.py --show_presets

         .. tab-item:: isaaclab.sh / isaaclab.bat

            .. code:: bash

               ./isaaclab.sh -p scripts/environments/list_envs.py --show_presets

   .. tab-item:: :icon:`fa-brands fa-windows` Windows
      :sync: windows

      .. code:: batch

         isaaclab.bat -p scripts\environments\list_envs.py --show_presets

The RL Library and Presets columns in the :ref:`comprehensive-environment-list` below are generated automatically from the Gym registry.

Environment IDs beginning with Isaac- identify core tasks maintained as part of Isaac Lab. IDs beginning with IsaacContrib- identify contributed tasks, which showcase advanced tasks and features but may not maintain the same level of support as core tasks.

We are actively working on adding more environments to the list. If you have any environments that you would like to add to Isaac Lab, please feel free to open a pull request!

Preset Selectors

Many environments support multiple physics backends, rendering backends, and observation modes. The Presets column in each table below is divided into three labeled groups:

physics= — physics-backend name passed as physics=NAME (e.g. isaacsim_physx, newton_mjwarp, newton_kamino, ovphysx, newton_mjwarp_vbd_proxy). physics=physx is also a valid automatic selector that chooses a compatible PhysX backend. The tables list only concrete backends so that each row identifies the runtimes it supports.
renderer= — renderer-backend name passed as renderer=NAME (e.g. isaacsim_rtx, newton_renderer, ovrtx). Cameras using the multi-backend renderer config default to concrete isaacsim_rtx. renderer=rtx is also a valid automatic selector that chooses a compatible RTX backend; the tables list only concrete renderer backends.
presets= — environment-specific (domain) preset name passed as presets=NAME[,NAME,...] (e.g. rgb, depth, single_camera, duo_camera)

If the Presets cell is empty, the environment does not expose any selectable physics, renderer, or domain preset alternatives. Run it without a physics=, renderer=, or presets= selector; the environment will use its registered default configuration, which may already select a fixed physics or renderer backend. Passing a selector that is not listed for the environment is not supported and causes configuration validation to fail.

For curated rows containing multiple environment IDs, the Presets cell is the union of the concrete selectors supported by those IDs. Use the comprehensive list for task-specific availability.

Pass --task=<task-name> --help to a training script to see all available preset names grouped by selector type at the command line, or run uv run python scripts/environments/list_envs.py --show_presets to list presets for every registered environment.

See the :doc:`Hydra preset system documentation </source/features/hydra>` for all available backend names and how the typed selectors work.

Note

The KukaAllegro lift and reorient tasks use the homogeneous cube object preset with physics=ovphysx. The heterogeneous shapes preset is not supported with OvPhysX; selecting both causes configuration validation to fail. Use presets=cube or choose another supported physics backend.

Single-agent

Classic

Classic environments that are based on IsaacGymEnvs implementation of MuJoCo-style environments.

Environment ID	Description	Presets
\|humanoid-link\| \|humanoid-direct-link\|	Move towards a direction with the MuJoCo humanoid robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|ant-link\| \|ant-direct-link\|	Move towards a direction with the MuJoCo ant robot	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|cartpole-link\| \|cartpole-direct-link\|	Move the cart to keep the pole upwards in the classic cartpole control	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|fourbar-pole-link\|	Swing up and balance a pole attached to a four-bar linkage	physics= `newton_kamino`
\|cartpole-camera-presets\|	Move the cart to keep the pole upwards in the classic cartpole control and perceptive inputs. Select data type via `presets=`.	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx` renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` presets= `albedo`, `depth`, `rgb`, `semantic_segmentation`, `simple_shading_constant_diffuse`, `simple_shading_diffuse_mdl`, `simple_shading_full_mdl`
\|cartpole-camera-link\|	Move the cart to keep the pole upwards in the classic cartpole control from raw RGB/depth observations or features extracted by pre-trained frozen vision encoders. Select pipeline via `presets=`.	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx` renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` presets= `albedo`, `depth`, `resnet18`, `rgb`, `semantic_segmentation`, `simple_shading_constant_diffuse`, `simple_shading_diffuse_mdl`, `simple_shading_full_mdl`, `theia_tiny`

Manipulation

Environments based on fixed-arm manipulation tasks.

For many of these tasks, we include configurations with different arm action spaces. For example, for the lift-cube environment:

|lift-cube-link|: Franka arm with joint position control
|lift-cube-ik-abs-link|: Franka arm with absolute IK control
|lift-cube-ik-rel-link|: Franka arm with relative IK control

Note

The Isaac-Lift-Soft-Franka task and its camera variant require automatic tetrahedralization. The Isaac-Lift-Cloth-Franka task and its camera variant use surface deformables and do not require these dependencies. Install the optional dependencies before running the soft-body tasks from a source checkout:

uv sync --inexact --extra tetrahedralization

With the legacy installer:

./isaaclab.sh -i tetrahedralization

Environment ID	Description	Presets
\|reach-franka-link\|	Move the end-effector to a sampled target pose with the Franka robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx` presets= `diffik`, `diffik_abs`, `joint_pos`, `newton_ik`
\|reach-ur10-link\|	Move the end-effector to a sampled target pose with the UR10 robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|deploy-reach-ur10e-link\|	Move the end-effector to a sampled target pose with the UR10e robot This policy has been deployed to a real robot
\|lift-cube-link\|	Pick a cube and bring it to a sampled target position with the Franka robot
\|lift-soft-franka-link\|	Pick a deformable soft body and bring it to a sampled target position with the Franka robot	physics= `isaacsim_physx`, `newton_mjwarp_vbd_proxy` presets= `ik`, `joint`
\|lift-soft-franka-cam-link\|	Camera (vision) variant of the soft-body lift task using RGB observations	physics= `isaacsim_physx`, `newton_mjwarp_vbd_proxy` renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` presets= `ik`, `joint`
\|lift-cloth-franka-link\|	Lift a deformable cloth from a table with the Franka robot	physics= `newton_mjwarp_vbd_proxy` presets= `ik`, `joint`
\|lift-cloth-franka-cam-link\|	Camera (vision) variant of the cloth lift task using RGB observations	physics= `newton_mjwarp_vbd_proxy` renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` presets= `ik`, `joint`
\|stack-cube-link\| \|stack-cube-bp-link\|	Stack three cubes (bottom to top: blue, red, green) with the Franka robot. Blueprint env used for the NVIDIA Isaac GR00T blueprint for synthetic manipulation motion generation	physics= `isaacsim_physx`, `newton_mjwarp`
\|long-suction-link\| \|short-suction-link\|	Stack three cubes (bottom to top: blue, red, green) with the UR10 arm and long surface gripper or short surface gripper (cpu only).	physics= `isaacsim_physx`, `newton_mjwarp`
\|cabi-franka-link\|	Grasp the handle of a cabinet's drawer and open it with the Franka robot
\|franka-direct-link\|	Grasp the handle of a cabinet's drawer and open it with the Franka robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|cube-allegro-link\|	In-hand reorientation of a cube using Allegro hand
\|allegro-direct-link\|	In-hand reorientation of a cube using Allegro hand	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|cube-shadow-link\| \|cube-shadow-ff-link\| \|cube-shadow-lstm-link\|	In-hand reorientation of a cube using Shadow hand	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|cube-shadow-vis-link\|	In-hand reorientation of a cube using Shadow hand using perceptive inputs.	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx` renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` presets= `albedo`, `depth`, `full`, `rgb`, `semantic_segmentation`, `simple_shading_constant_diffuse`, `simple_shading_diffuse_mdl`, `simple_shading_full_mdl`
\|gr1_pick_place-link\|	Pick up and place an object in a basket with a GR-1 humanoid robot
\|gr1_pp_waist-link\|	Pick up and place an object in a basket with a GR-1 humanoid robot with waist degrees-of-freedom enables that provides a wider reach space.
\|g1_pick_place-link\|	Pick up and place an object in a basket with a Unitree G1 humanoid robot
\|g1_pick_place_fixed-link\|	Pick up and place an object in a basket with a Unitree G1 humanoid robot with three-fingered hands. Robot is set up with the base fixed in place.
\|g1_pick_place_lm-link\|	Pick up and place an object in a basket with a Unitree G1 humanoid robot with three-fingered hands and in-place locomanipulation capabilities enabled (i.e. Robot lower body balances in-place while upper body is controlled via Inverse Kinematics).
\|kuka-allegro-lift-link\|	Pick up a primitive shape on the table and lift it to target position.	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx` presets= `cube`, `shapes`
\|ka-lift-cam-link\|	Camera (vision) variant of the lift task, adding single- and dual-camera observations via `presets=single_camera` / `presets=duo_camera`.	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx` renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` presets= `albedo128`, `albedo256`, `albedo64`, `cube`, `depth128`, `depth256`, `depth64`, `duo_camera`, `raycaster_depth128`, `raycaster_depth256`, `raycaster_depth64`, `rgb128`, `rgb256`, `rgb64`, `semantic_segmentation128`, `semantic_segmentation256`, `semantic_segmentation64`, `shapes`, `simple_shading_constant_diffuse128`, `simple_shading_constant_diffuse256`, `simple_shading_constant_diffuse64`, `simple_shading_diffuse_mdl128`, `simple_shading_diffuse_mdl256`, `simple_shading_diffuse_mdl64`, `simple_shading_full_mdl128`, `simple_shading_full_mdl256`, `simple_shading_full_mdl64`, `single_camera`
\|kuka-allegro-reorient-link\|	Pick up a primitive shape on the table and orient it to target pose.	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx` presets= `cube`, `shapes`
\|ka-reorient-cam-link\|	Camera (vision) variant of the reorient task, adding single- and dual-camera observations via `presets=single_camera` / `presets=duo_camera`.	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx` renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` presets= `albedo128`, `albedo256`, `albedo64`, `cube`, `depth128`, `depth256`, `depth64`, `duo_camera`, `raycaster_depth128`, `raycaster_depth256`, `raycaster_depth64`, `rgb128`, `rgb256`, `rgb64`, `semantic_segmentation128`, `semantic_segmentation256`, `semantic_segmentation64`, `shapes`, `simple_shading_constant_diffuse128`, `simple_shading_constant_diffuse256`, `simple_shading_constant_diffuse64`, `simple_shading_diffuse_mdl128`, `simple_shading_diffuse_mdl256`, `simple_shading_diffuse_mdl64`, `simple_shading_full_mdl128`, `simple_shading_full_mdl256`, `simple_shading_full_mdl64`, `single_camera`
\|galbot_stack-link\|	Stack three cubes (bottom to top: blue, red, green) with the left arm of a Galbot humanoid robot	physics= `isaacsim_physx`, `newton_mjwarp`
\|agibot_place_mug-link\|	Pick up and place a mug upright with a Agibot A2D humanoid robot	physics= `isaacsim_physx`, `newton_mjwarp`
\|agibot_place_toy-link\|	Pick up and place an object in a box with a Agibot A2D humanoid robot	physics= `isaacsim_physx`, `newton_mjwarp`
\|reach_openarm_bi-link\|	Move the end-effector to sampled target poses with the OpenArm robot
\|reach_openarm_uni-link\|	Move the end-effector to a sampled target pose with the OpenArm robot
\|lift_openarm_uni-link\|	Pick a cube and bring it to a sampled target position with the OpenArm robot
\|cabi_openarm_uni-link\|	Grasp the handle of a cabinet's drawer and open it with the OpenArm robot
\|g1_assemble_trocar-link\|	Assemble trocar with a Unitree G1 humanoid robot with Dex3 hands

Contact-rich Manipulation

Environments based on contact-rich manipulation tasks such as peg insertion, gear meshing and nut-bolt fastening.

These tasks share the same task configurations and control options. You can switch between them by specifying the task name. For example:

|factory-peg-link|: Peg insertion with the Franka arm
|factory-gear-link|: Gear meshing with the Franka arm
|factory-nut-link|: Nut-Bolt fastening with the Franka arm

World	Environment ID	Description	Presets
	\|factory-peg-link\|	Insert peg into the socket with the Franka robot
	\|factory-gear-link\|	Insert and mesh gear into the base with other gears, using the Franka robot
	\|factory-nut-link\|	Thread the nut onto the first 2 threads of the bolt, using the Franka robot

AutoMate

Environments based on 100 diverse assembly tasks, each involving the insertion of a plug into a socket. These tasks share a common configuration and differ by th geometry and properties of the parts.

You can switch between tasks by specifying the corresponding asset ID. Available asset IDs include:

'00004', '00007', '00014', '00015', '00016', '00021', '00028', '00030', '00032', '00042', '00062', '00074', '00077', '00078', '00081', '00083', '00103', '00110', '00117', '00133', '00138', '00141', '00143', '00163', '00175', '00186', '00187', '00190', '00192', '00210', '00211', '00213', '00255', '00256', '00271', '00293', '00296', '00301', '00308', '00318', '00319', '00320', '00329', '00340', '00345', '00346', '00360', '00388', '00410', '00417', '00422', '00426', '00437', '00444', '00446', '00470', '00471', '00480', '00486', '00499', '00506', '00514', '00537', '00553', '00559', '00581', '00597', '00614', '00615', '00638', '00648', '00649', '00652', '00659', '00681', '00686', '00700', '00703', '00726', '00731', '00741', '00755', '00768', '00783', '00831', '00855', '00860', '00863', '01026', '01029', '01036', '01041', '01053', '01079', '01092', '01102', '01125', '01129', '01132', '01136'.

We provide environments for both disassembly and assembly.

Attention!

CUDA is recommended for running the AutoMate environments. If running with Nvidia driver 570 on Linux with architecture x86_64, we follow the below steps to install CUDA 12.8. This allows for computing rewards in AutoMate environments with CUDA. If you have a different operation system or architecture, please refer to the CUDA installation page for additional instruction.

wget https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_570.86.10_linux.run
sudo sh cuda_12.8.0_570.86.10_linux.run --toolkit

When using conda, cuda toolkit can be installed with:

conda install cudatoolkit

With 580 drivers on Linux with architecture x86_64, we install CUDA 13 and additionally install several packages. Please ensure that the pytorch version is compatible with the CUDA version.

wget https://developer.download.nvidia.com/compute/cuda/13.0.2/local_installers/cuda_13.0.2_580.95.05_linux.run
sudo sh cuda_13.0.2_580.95.05_linux.run --toolkit
pip install numba-cuda[cu13] coverage==7.6.1

|disassembly-link|: The plug starts inserted in the socket. A low-level controller lifts the plug out and moves it to a random position. This process is purely scripted and does not involve any learned policy. Therefore, it does not require policy training or evaluation. The resulting trajectories serve as demonstrations for the reverse process, i.e., learning to assemble. To run disassembly for a specific task: python source/isaaclab_tasks/isaaclab_tasks/contrib/automate/run_disassembly_w_id.py --assembly_id=ASSEMBLY_ID --disassembly_dir=DISASSEMBLY_DIR. All generated trajectories are saved to a local directory DISASSEMBLY_DIR.
|assembly-link|: The goal is to insert the plug into the socket. You can use this environment to train a policy via reinforcement learning or evaluate a pre-trained checkpoint.
- To train an assembly policy, we run the command python source/isaaclab_tasks/isaaclab_tasks/contrib/automate/run_w_id.py --assembly_id=ASSEMBLY_ID --train. We can customize the training process using the optional flags: --max_iterations=MAX_ITERATIONS to set the number of training iterations, --num_envs=NUM_ENVS to set the number of parallel environments during training, --seed=SEED to assign the random seed. The policy checkpoints will be saved automatically during training in the directory logs/rl_games/Assembly/test.
- To evaluate an assembly policy, we run the command python source/isaaclab_tasks/isaaclab_tasks/contrib/automate/run_w_id.py --assembly_id=ASSEMBLY_ID --checkpoint=CHECKPOINT --log_eval. The evaluation results are stored in evaluation_{ASSEMBLY_ID}.h5.

World	Environment ID	Description	Presets
	\|disassembly-link\|	Lift a plug out of the socket with the Franka robot
	\|assembly-link\|	Insert a plug into its corresponding socket with the Franka robot

FORGE

FORGE environments extend Factory environments with:

Force sensing: Add observations for force experienced by the end-effector.
Excessive force penalty: Add an option to penalize the agent for excessive contact forces.
Dynamics randomization: Randomize controller gains, asset properties (friction, mass), and dead-zone.
Success prediction: Add an extra action that predicts task success.

These tasks share the same task configurations and control options. You can switch between them by specifying the task name.

|forge-peg-link|: Peg insertion with the Franka arm
|forge-gear-link|: Gear meshing with the Franka arm
|forge-nut-link|: Nut-Bolt fastening with the Franka arm

World	Environment ID	Description	Presets
	\|forge-peg-link\|	Insert peg into the socket with the Franka robot
	\|forge-gear-link\|	Insert and mesh gear into the base with other gears, using the Franka robot
	\|forge-nut-link\|	Thread the nut onto the first 2 threads of the bolt, using the Franka robot

Locomotion

Environments based on legged locomotion tasks.

Environment ID	Description	Presets
\|velocity-flat-anymal-b-link\|	Track a velocity command on flat terrain with the Anymal B robot	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|velocity-rough-anymal-b-link\|	Track a velocity command on rough terrain with the Anymal B robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|velocity-flat-anymal-c-link\|	Track a velocity command on flat terrain with the Anymal C robot	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|velocity-flat-anymal-c-direct-link\|	Track a velocity command on flat terrain with the Anymal C robot
\|velocity-rough-anymal-c-link\|	Track a velocity command on rough terrain with the Anymal C robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|velocity-rough-anymal-c-direct-link\|	Track a velocity command on rough terrain with the Anymal C robot
\|velocity-flat-anymal-d-link\|	Track a velocity command on flat terrain with the Anymal D robot	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|velocity-rough-anymal-d-link\|	Track a velocity command on rough terrain with the Anymal D robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|velocity-rough-cassie-link\|	Track a velocity command on rough terrain with the Cassie robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|velocity-flat-unitree-a1-link\|	Track a velocity command on flat terrain with the Unitree A1 robot	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|velocity-rough-unitree-a1-link\|	Track a velocity command on rough terrain with the Unitree A1 robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|velocity-flat-unitree-go1-link\|	Track a velocity command on flat terrain with the Unitree Go1 robot	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|velocity-rough-unitree-go1-link\|	Track a velocity command on rough terrain with the Unitree Go1 robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|velocity-flat-unitree-go2-link\|	Track a velocity command on flat terrain with the Unitree Go2 robot	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|velocity-rough-unitree-go2-link\|	Track a velocity command on rough terrain with the Unitree Go2 robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|velocity-flat-spot-link\|	Track a velocity command on flat terrain with the Boston Dynamics Spot robot	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`
\|velocity-flat-h1-link\|	Track a velocity command on flat terrain with the Unitree H1 robot	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|velocity-rough-h1-link\|	Track a velocity command on rough terrain with the Unitree H1 robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|velocity-flat-g1-link\|	Track a velocity command on flat terrain with the Unitree G1 robot	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
\|velocity-rough-g1-link\|	Track a velocity command on rough terrain with the Unitree G1 robot	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
\|dr-legs-hold-pose-link\| \|dr-legs-walk-link\|	Hold a target pose or walk with the Disney Research Legs robot	physics= `isaacsim_physx`, `newton_kamino`
\|velocity-flat-digit-link\|	Track a velocity command on flat terrain with the Agility Digit robot	physics= `isaacsim_physx`
\|velocity-rough-digit-link\|	Track a velocity command on rough terrain with the Agility Digit robot	physics= `isaacsim_physx`
\|tracking-loco-manip-digit-link\|	Track a root velocity and hand pose command with the Agility Digit robot	physics= `isaacsim_physx`

Note

Agility Digit environments use closed-loop articulations (achilles rod, toe push-rods) that do not run correctly on newton_mjwarp. Use isaacsim_physx for Digit-based tasks; see :ref:`known-issues-closed-loop-newton` for details.

Navigation

World	Environment ID	Description	Presets
	\|anymal_c_nav-link\|	Navigate towards a target x-y position and heading with the ANYmal C robot.

Multirotor

Note

The multirotor entry provides an environment configuration for flying the ARL robot. See the drone_arl folder and the ARL robot config (ARL_ROBOT_1_CFG) in the codebase for details.

World	Environment ID	Description	Presets
	\|arl_robot_track_position_state_based-link\|	Setpoint position control for the ARL robot using the track_position_state_based task.
	\|arl_robot_navigation-link\|	Navigate through 3D obstacles with the ARL robot using depth camera sensing.

Others

Note

Adversarial Motion Priors (AMP) training is only available with the skrl library, as it is the only one of the currently integrated libraries that supports it out-of-the-box (for the other libraries, it is necessary to implement the algorithm and architectures). See the skrl's AMP Documentation for more information. The AMP algorithm can be activated by adding the command line input --algorithm AMP to the train/play script.

For evaluation, the play script's command line input --real-time allows the interaction loop between the environment and the agent to run in real time, if possible.

World	Environment ID	Description	Presets
	\|humanoid_amp_dance-link\| \|humanoid_amp_run-link\| \|humanoid_amp_walk-link\|	Move a humanoid robot by imitating different pre-recorded human animations (Adversarial Motion Priors).

World

Environment ID

Description

Presets

|humanoid_amp_dance-link|

|humanoid_amp_run-link|

|humanoid_amp_walk-link|

Move a humanoid robot by imitating different pre-recorded human animations (Adversarial Motion Priors).

Spaces showcase

The |cartpole_showcase| folder contains showcase tasks (based on the Cartpole and Cartpole-Camera Direct tasks) for the definition/use of the various Gymnasium observation and action spaces supported in Isaac Lab.

Note

Currently, only Isaac Lab's Direct workflow supports the definition of observation and action spaces other than Box. See Direct workflow's :py:obj:`~isaaclab.envs.DirectRLEnvCfg.observation_space` / :py:obj:`~isaaclab.envs.DirectRLEnvCfg.action_space` documentation for more details.

The following tables summarize the different pairs of showcased spaces for the Cartpole and Cartpole-Camera tasks. Their registered task IDs are fixed: IsaacContrib-Cartpole-Showcase-Direct and IsaacContrib-Cartpole-Camera-Showcase-Direct. Select the observation/action pair with presets=<observation>_<action> rather than changing the task ID, and select the matching skrl agent entry point for that pair. For example:

uv run --extra isaacsim isaaclab train --rl_library skrl \
    --task IsaacContrib-Cartpole-Showcase-Direct \
    --agent skrl_box_box_cfg_entry_point presets=box_box

uv run --extra isaacsim isaaclab train --rl_library skrl \
    --task IsaacContrib-Cartpole-Camera-Showcase-Direct \
    --agent skrl_box_box_cfg_entry_point presets=box_box

Showcase spaces for the Cartpole task

IsaacContrib-Cartpole-Showcase-Direct presets=<observation>_<action>

		action space
		Box	Discrete	MultiDiscrete
observation space	Box	x	x	x
	Discrete	x	x	x
	MultiDiscrete	x	x	x
	Dict	x	x	x
	Tuple	x	x	x

Showcase spaces for the Cartpole-Camera task

IsaacContrib-Cartpole-Camera-Showcase-Direct presets=<observation>_<action>

		action space
		Box	Discrete	MultiDiscrete
observation space	Box	x	x	x
	Discrete	-	-	-
	MultiDiscrete	-	-	-
	Dict	x	x	x
	Tuple	x	x	x

Multi-agent

Note

True mutli-agent training is only available with the skrl library, see the Multi-Agents Documentation for more information. It supports the IPPO and MAPPO algorithms, which can be activated by adding the command line input --algorithm IPPO or --algorithm MAPPO to the train/play script. If these environments are run with other libraries or without the IPPO or MAPPO flags, they will be converted to single-agent environments under the hood.

Classic

World	Environment ID	Description	Presets
	\|cart-double-pendulum-direct-link\|	Move the cart and the pendulum to keep the last one upwards in the classic inverted double pendulum on a cart control

Manipulation

Environments based on fixed-arm manipulation tasks.

World	Environment ID	Description	Presets
	\|shadow-hand-over-direct-link\|	Passing an object from one hand over to the other hand	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`

Comprehensive List of Environments

To run inference on a trained task, pass its task name to play.py. The play script applies the environment configuration's play_mode overrides automatically, providing configurations more suitable for inferencing, including disabling runtime perturbations used for training.

Note

Warp-native environment implementations no longer register separate -Warp task ids. Run the same stable tasks on the Warp runtime by passing --frontend warp together with presets=newton_mjwarp to the training and play entry points. See :doc:`core-concepts/physical-backends/newton/warp-environments` for details.

Task Name	Workflow	RL Library	Presets
Isaac-Ant	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO), sb3 (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`
Isaac-Ant-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Cartpole	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO), sb3 (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Cartpole-Camera	Manager Based	rl_games (PPO, FEATURE), rsl_rl (PPO, FEATURE)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx` \| renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` \| presets= `albedo`, `depth`, `resnet18`, `rgb`, `semantic_segmentation`, `simple_shading_constant_diffuse`, `simple_shading_diffuse_mdl`, `simple_shading_full_mdl`, `theia_tiny`
Isaac-Cartpole-Camera-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx` \| renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` \| presets= `albedo`, `depth`, `rgb`, `semantic_segmentation`, `simple_shading_constant_diffuse`, `simple_shading_diffuse_mdl`, `simple_shading_full_mdl`
Isaac-Cartpole-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO), sb3 (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Fourbar-Pole-Swingup	Manager Based	rsl_rl (PPO)	physics= `newton_kamino`
Isaac-Humanoid	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO), sb3 (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`
Isaac-Humanoid-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
Isaac-Lift-Cloth-Franka	Manager Based	rsl_rl (PPO)	physics= `newton_mjwarp_vbd_proxy` \| presets= `ik`, `joint`
Isaac-Lift-Cloth-Franka-Camera	Manager Based	rsl_rl (PPO)	physics= `newton_mjwarp_vbd_proxy` \| renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` \| presets= `ik`, `joint`
Isaac-Lift-Franka	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp` \| presets= `cube`, `shapes`
Isaac-Lift-KukaAllegro	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx` \| presets= `cube`, `shapes`
Isaac-Lift-KukaAllegro-Camera	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx` \| renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` \| presets= `albedo128`, `albedo256`, `albedo64`, `cube`, `depth128`, `depth256`, `depth64`, `duo_camera`, `raycaster_depth128`, `raycaster_depth256`, `raycaster_depth64`, `rgb128`, `rgb256`, `rgb64`, `semantic_segmentation128`, `semantic_segmentation256`, `semantic_segmentation64`, `shapes`, `simple_shading_constant_diffuse128`, `simple_shading_constant_diffuse256`, `simple_shading_constant_diffuse64`, `simple_shading_diffuse_mdl128`, `simple_shading_diffuse_mdl256`, `simple_shading_diffuse_mdl64`, `simple_shading_full_mdl128`, `simple_shading_full_mdl256`, `simple_shading_full_mdl64`, `single_camera`
Isaac-Lift-Soft-Franka	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp_vbd_proxy` \| presets= `ik`, `joint`
Isaac-Lift-Soft-Franka-Camera	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp_vbd_proxy` \| renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` \| presets= `ik`, `joint`
Isaac-Open-Drawer-Franka	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO)
Isaac-Open-Drawer-Franka-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
Isaac-Pendulum-Direct	Direct	rl_games (PPO), skrl (PPO, IPPO, MAPPO)
Isaac-Reach-Franka	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx` \| presets= `diffik`, `diffik_abs`, `joint_pos`, `newton_ik`
Isaac-Reach-Franka-OSC	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
Isaac-Reach-UR10	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
Isaac-Reorient-Cube-Allegro	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO)
Isaac-Reorient-Cube-Allegro-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
Isaac-Reorient-Cube-Shadow-Camera-Direct	Direct	rl_games (PPO), rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx` \| renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` \| presets= `albedo`, `depth`, `full`, `rgb`, `semantic_segmentation`, `simple_shading_constant_diffuse`, `simple_shading_diffuse_mdl`, `simple_shading_full_mdl`
Isaac-Reorient-Cube-Shadow-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Reorient-Cube-Shadow-OpenAI-FF-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Reorient-Cube-Shadow-OpenAI-LSTM-Direct	Direct	rl_games (PPO), rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Reorient-Franka	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp` \| presets= `cube`, `shapes`
Isaac-Reorient-KukaAllegro	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx` \| presets= `cube`, `shapes`
Isaac-Reorient-KukaAllegro-Camera	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx` \| renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx` \| presets= `albedo128`, `albedo256`, `albedo64`, `cube`, `depth128`, `depth256`, `depth64`, `duo_camera`, `raycaster_depth128`, `raycaster_depth256`, `raycaster_depth64`, `rgb128`, `rgb256`, `rgb64`, `semantic_segmentation128`, `semantic_segmentation256`, `semantic_segmentation64`, `shapes`, `simple_shading_constant_diffuse128`, `simple_shading_constant_diffuse256`, `simple_shading_constant_diffuse64`, `simple_shading_diffuse_mdl128`, `simple_shading_diffuse_mdl256`, `simple_shading_diffuse_mdl64`, `simple_shading_full_mdl128`, `simple_shading_full_mdl256`, `simple_shading_full_mdl64`, `single_camera`
Isaac-Shadow-Handover-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO, IPPO, MAPPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
Isaac-Velocity-Flat-AnymalD	Manager Based	rsl_rl (PPO, DISTILLATION, DISTILLATION_RECURRENT, RECURRENT), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Velocity-Flat-Cassie	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Velocity-Flat-G1	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Velocity-Flat-H1	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Velocity-Flat-UnitreeGo2	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
Isaac-Velocity-Rough-AnymalD	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
Isaac-Velocity-Rough-Cassie	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
Isaac-Velocity-Rough-G1	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
Isaac-Velocity-Rough-H1	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
Isaac-Velocity-Rough-UnitreeGo2	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
IsaacContrib-Assemble-Trocar-G129-Dex3	Manager Based	rlinf (PPO)
IsaacContrib-AutoMate-Assembly-Direct	Direct	rl_games (PPO)
IsaacContrib-AutoMate-Disassembly-Direct	Direct	rl_games (PPO)
IsaacContrib-Cartpole-Camera-Showcase-Direct	Direct	skrl (PPO, BOX_BOX, BOX_DISCRETE, BOX_MULTIDISCRETE, DICT_BOX, DICT_DISCRETE, DICT_MULTIDISCRETE, TUPLE_BOX, TUPLE_DISCRETE, TUPLE_MULTIDISCRETE)	presets= `box_box`, `box_discrete`, `box_multidiscrete`, `dict_box`, `dict_discrete`, `dict_multidiscrete`, `tuple_box`, `tuple_discrete`, `tuple_multidiscrete`
IsaacContrib-Cartpole-Showcase-Direct	Direct	skrl (PPO, BOX_BOX, BOX_DISCRETE, BOX_MULTIDISCRETE, DICT_BOX, DICT_DISCRETE, DICT_MULTIDISCRETE, DISCRETE_BOX, DISCRETE_DISCRETE, DISCRETE_MULTIDISCRETE, MULTIDISCRETE_BOX, MULTIDISCRETE_DISCRETE, MULTIDISCRETE_MULTIDISCRETE, TUPLE_BOX, TUPLE_DISCRETE, TUPLE_MULTIDISCRETE)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx` \| presets= `box_box`, `box_discrete`, `box_multidiscrete`, `dict_box`, `dict_discrete`, `dict_multidiscrete`, `discrete_box`, `discrete_discrete`, `discrete_multidiscrete`, `multidiscrete_box`, `multidiscrete_discrete`, `multidiscrete_multidiscrete`, `tuple_box`, `tuple_discrete`, `tuple_multidiscrete`
IsaacContrib-Deploy-GearAssembly-Rizon4s-Grav	Manager Based	rsl_rl (PPO)
IsaacContrib-Deploy-GearAssembly-Rizon4s-Grav-ROS-Inference	Manager Based	rsl_rl (PPO)
IsaacContrib-Deploy-GearAssembly-UR10e-2F140	Manager Based	rsl_rl (PPO)
IsaacContrib-Deploy-GearAssembly-UR10e-2F140-ROS-Inference	Manager Based	rsl_rl (PPO)
IsaacContrib-Deploy-GearAssembly-UR10e-2F85	Manager Based	rsl_rl (PPO)
IsaacContrib-Deploy-GearAssembly-UR10e-2F85-ROS-Inference	Manager Based	rsl_rl (PPO)
IsaacContrib-Deploy-Reach-Rizon4s	Manager Based	rsl_rl (PPO)
IsaacContrib-Deploy-Reach-Rizon4s-ROS-Inference	Manager Based	rsl_rl (PPO)
IsaacContrib-Deploy-Reach-UR10e	Manager Based	rsl_rl (PPO)
IsaacContrib-Deploy-Reach-UR10e-ROS-Inference	Manager Based	rsl_rl (PPO)
IsaacContrib-DrLegs-HoldPose	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_kamino`
IsaacContrib-DrLegs-Walk	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`, `newton_kamino`
IsaacContrib-ExhaustPipe-GR1T2-Pink-IK-Abs	Manager Based
IsaacContrib-Factory-GearMesh-Direct	Direct	rl_games (PPO)
IsaacContrib-Factory-NutThread-Direct	Direct	rl_games (PPO)
IsaacContrib-Factory-PegInsert-Direct	Direct	rl_games (PPO)
IsaacContrib-Forge-GearMesh-Direct	Direct	rl_games (PPO)
IsaacContrib-Forge-NutThread-Direct	Direct	rl_games (PPO)
IsaacContrib-Forge-PegInsert-Direct	Direct	rl_games (PPO)
IsaacContrib-Humanoid-AMP-Dance-Direct	Direct	skrl (AMP)
IsaacContrib-Humanoid-AMP-Run-Direct	Direct	skrl (AMP)
IsaacContrib-Humanoid-AMP-Walk-Direct	Direct	skrl (AMP)
IsaacContrib-Lift-Cube-Franka	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO), sb3 (PPO)
IsaacContrib-Lift-Cube-Franka-IK-Abs	Manager Based
IsaacContrib-Lift-Cube-Franka-IK-Rel	Manager Based
IsaacContrib-Lift-Cube-OpenArm	Manager Based	rl_games (PPO), rsl_rl (PPO)
IsaacContrib-Navigation-3DObstacles-ARL-Robot-1	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO)
IsaacContrib-Navigation-Flat-AnymalC	Manager Based	rsl_rl (PPO), skrl (PPO)
IsaacContrib-NutPour-GR1T2-Pink-IK-Abs	Manager Based
IsaacContrib-Open-Drawer-Franka-IK-Abs	Manager Based
IsaacContrib-Open-Drawer-Franka-IK-Rel	Manager Based
IsaacContrib-Open-Drawer-OpenArm	Manager Based	rl_games (PPO), rsl_rl (PPO)
IsaacContrib-PickPlace-FixedBaseUpperBodyIK-G1-Abs	Manager Based
IsaacContrib-PickPlace-G1-InspireFTP-Abs	Manager Based
IsaacContrib-PickPlace-GR1T2-Abs	Manager Based
IsaacContrib-PickPlace-GR1T2-WaistEnabled-Abs	Manager Based
IsaacContrib-PickPlace-Locomanipulation-G1-Abs	Manager Based
IsaacContrib-Place-Mug-Agibot-Left-Arm-RmpFlow	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Place-Toy2Box-Agibot-Right-Arm-RmpFlow	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Reach-OpenArm	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO)
IsaacContrib-Reach-OpenArmBi	Manager Based	rl_games (PPO), rsl_rl (PPO)
IsaacContrib-Stack-Cube-Bin-Franka-IK-Rel-Mimic	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-BlueGreen-Franka-IK-Rel	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-BlueGreenRed-Franka-IK-Rel	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-Franka	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-Franka-IK-Abs	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-Franka-IK-Rel	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-Franka-IK-Rel-Blueprint	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-Franka-IK-Rel-Skillgen	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-Franka-IK-Rel-Visuomotor	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-Franka-IK-Rel-Visuomotor-Cosmos	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp` \| renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx`
IsaacContrib-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-Joint-Position	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp` \| renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx`
IsaacContrib-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-RmpFlow	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp` \| renderer= `isaacsim_rtx`, `newton_renderer`, `ovrtx`
IsaacContrib-Stack-Cube-Galbot-Right-Arm-Suction-RmpFlow	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-Instance-Randomize-Franka	Manager Based
IsaacContrib-Stack-Cube-Instance-Randomize-Franka-IK-Rel	Manager Based
IsaacContrib-Stack-Cube-RedGreen-Franka-IK-Rel	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-RedGreenBlue-Franka-IK-Rel	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-SO101-IK-Abs-v0	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-SO101-Joint-Teleop-v0	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-SO101-v0	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-UR10-Long-Suction-IK-Rel	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-Stack-Cube-UR10-Short-Suction-IK-Rel	Manager Based		physics= `isaacsim_physx`, `newton_mjwarp`
IsaacContrib-TrackPositionNoObstacles-ARL-Robot-1	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO)
IsaacContrib-Tracking-LocoManip-Digit	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`
IsaacContrib-Velocity-Flat-AnymalB	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
IsaacContrib-Velocity-Flat-AnymalC	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
IsaacContrib-Velocity-Flat-AnymalC-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO)
IsaacContrib-Velocity-Flat-Digit	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`
IsaacContrib-Velocity-Flat-Spot	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`
IsaacContrib-Velocity-Flat-UnitreeA1	Manager Based	rsl_rl (PPO), skrl (PPO), sb3 (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
IsaacContrib-Velocity-Flat-UnitreeGo1	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_kamino`, `newton_mjwarp`, `ovphysx`
IsaacContrib-Velocity-Rough-AnymalB	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
IsaacContrib-Velocity-Rough-AnymalC	Manager Based	rl_games (PPO), rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
IsaacContrib-Velocity-Rough-AnymalC-Direct	Direct	rl_games (PPO), rsl_rl (PPO), skrl (PPO)
IsaacContrib-Velocity-Rough-Digit	Manager Based	rsl_rl (PPO)	physics= `isaacsim_physx`
IsaacContrib-Velocity-Rough-UnitreeA1	Manager Based	rsl_rl (PPO), skrl (PPO), sb3 (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`
IsaacContrib-Velocity-Rough-UnitreeGo1	Manager Based	rsl_rl (PPO), skrl (PPO)	physics= `isaacsim_physx`, `newton_mjwarp`, `ovphysx`

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Available Environments

Preset Selectors

Single-agent

Classic

Manipulation

Contact-rich Manipulation

AutoMate

FORGE

Locomotion

Navigation

Multirotor

Others

Spaces showcase

Multi-agent

Classic

Manipulation

Comprehensive List of Environments