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Available Environments β€” Isaac Lab Documentation

Last updated: 12/12/2025

Title: Available Environments β€” Isaac Lab Documentation

URL Source: https://isaac-sim.github.io/IsaacLab/main/source/overview/environments

Published Time: Thu, 11 Sep 2025 17:00:56 GMT

Markdown Content: 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:

Linux

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

Windows

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

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!

Single-agent#

Classic#

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

WorldEnvironment IDDescription
Image 1: humanoidIsaac-Humanoid-v0 Isaac-Humanoid-Direct-v0Move towards a direction with the MuJoCo humanoid robot
Image 2: antIsaac-Ant-v0 Isaac-Ant-Direct-v0Move towards a direction with the MuJoCo ant robot
Image 3: cartpoleIsaac-Cartpole-v0 Isaac-Cartpole-Direct-v0Move the cart to keep the pole upwards in the classic cartpole control
Image 4: cartpoleIsaac-Cartpole-RGB-v0 Isaac-Cartpole-Depth-v0 Isaac-Cartpole-RGB-Camera-Direct-v0 Isaac-Cartpole-Depth-Camera-Direct-v0Move the cart to keep the pole upwards in the classic cartpole control and perceptive inputs. Requires running with --enable_cameras.
Image 5: cartpoleIsaac-Cartpole-RGB-ResNet18-v0 Isaac-Cartpole-RGB-TheiaTiny-v0Move the cart to keep the pole upwards in the classic cartpole control based off of features extracted from perceptive inputs with pre-trained frozen vision encoders. Requires running with --enable_cameras.

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:

WorldEnvironment IDDescription
Image 6: reach-frankaIsaac-Reach-Franka-v0Move the end-effector to a sampled target pose with the Franka robot
Image 7: reach-ur10Isaac-Reach-UR10-v0Move the end-effector to a sampled target pose with the UR10 robot
Image 8: deploy-reach-ur10eIsaac-Deploy-Reach-UR10e-v0Move the end-effector to a sampled target pose with the UR10e robot This policy has been deployed to a real robot
Image 9: lift-cubeIsaac-Lift-Cube-Franka-v0Pick a cube and bring it to a sampled target position with the Franka robot
Image 10: stack-cubeIsaac-Stack-Cube-Franka-v0 Isaac-Stack-Cube-Franka-IK-Rel-Blueprint-v0Stack 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
Image 11: surface-gripperIsaac-Stack-Cube-UR10-Long-Suction-IK-Rel-v0 Isaac-Stack-Cube-UR10-Short-Suction-IK-Rel-v0Stack three cubes (bottom to top: blue, red, green) with the UR10 arm and long surface gripper or short surface gripper.
Image 12: cabi-frankaIsaac-Open-Drawer-Franka-v0 Isaac-Franka-Cabinet-Direct-v0Grasp the handle of a cabinet’s drawer and open it with the Franka robot
Image 13: cube-allegroIsaac-Repose-Cube-Allegro-v0 Isaac-Repose-Cube-Allegro-Direct-v0In-hand reorientation of a cube using Allegro hand
Image 14: cube-shadowIsaac-Repose-Cube-Shadow-Direct-v0 Isaac-Repose-Cube-Shadow-OpenAI-FF-Direct-v0 Isaac-Repose-Cube-Shadow-OpenAI-LSTM-Direct-v0In-hand reorientation of a cube using Shadow hand
Image 15: cube-shadowIsaac-Repose-Cube-Shadow-Vision-Direct-v0In-hand reorientation of a cube using Shadow hand using perceptive inputs. Requires running with --enable_cameras.
Image 16: gr1_pick_placeIsaac-PickPlace-GR1T2-Abs-v0Pick up and place an object in a basket with a GR-1 humanoid robot
Image 17: gr1_pp_waistIsaac-PickPlace-GR1T2-WaistEnabled-Abs-v0Pick 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.
Image 18: kuka-allegro-liftIsaac-Dexsuite-Kuka-Allegro-Lift-v0Pick up a primitive shape on the table and lift it to target position
Image 19: kuka-allegro-reorientIsaac-Dexsuite-Kuka-Allegro-Reorient-v0Pick up a primitive shape on the table and orient it to target pose
Image 20: galbot_stackIsaac-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow-v0Stack three cubes (bottom to top: blue, red, green) with the left arm of a Galbot humanoid robot
Image 21: agibot_place_mugIsaac-Place-Mug-Agibot-Left-Arm-RmpFlow-v0Pick up and place a mug upright with a Agibot A2D humanoid robot
Image 22: agibot_place_toyIsaac-Place-Toy2Box-Agibot-Right-Arm-RmpFlow-v0Pick up and place an object in a box with a Agibot A2D humanoid robot

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:

WorldEnvironment IDDescription
Image 23: forge-pegIsaac-Factory-PegInsert-Direct-v0Insert peg into the socket with the Franka robot
Image 24: forge-gearIsaac-Factory-GearMesh-Direct-v0Insert and mesh gear into the base with other gears, using the Franka robot
Image 25: forge-nutIsaac-Factory-NutThread-Direct-v0Thread 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:

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

We provide environments for both disassembly and assembly.

Attention

CUDA is required for running the AutoMate environments. Follow the below steps to install CUDA 12.8:

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

When using conda, cuda toolkit can be installed with:

conda install cudatoolkit

For addition instructions and Windows installation, please refer to the CUDA installation page.

  • Isaac-AutoMate-Disassembly-Direct-v0: 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/direct/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.

  • Isaac-AutoMate-Assembly-Direct-v0: 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/direct/automate/run_w_id.py --assembly_id=ASSEMBLY_ID --train. We can customize the training process using the optional flags: --headless to run without opening the GUI windows, --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/direct/automate/run_w_id.py --assembly_id=ASSEMBLY_ID --checkpoint=CHECKPOINT --log_eval. The evaluation results are stored in evaluation_{ASSEMBLY_ID}.h5.

WorldEnvironment IDDescription
Image 26: disassemblyIsaac-AutoMate-Disassembly-Direct-v0Lift a plug out of the socket with the Franka robot
Image 27: assemblyIsaac-AutoMate-Assembly-Direct-v0Insert 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.

WorldEnvironment IDDescription
Image 28: forge-pegIsaac-Forge-PegInsert-Direct-v0Insert peg into the socket with the Franka robot
Image 29: forge-gearIsaac-Forge-GearMesh-Direct-v0Insert and mesh gear into the base with other gears, using the Franka robot
Image 30: forge-nutIsaac-Forge-NutThread-Direct-v0Thread the nut onto the first 2 threads of the bolt, using the Franka robot

Locomotion#

Environments based on legged locomotion tasks.

WorldEnvironment IDDescription
Image 31: velocity-flat-anymal-bIsaac-Velocity-Flat-Anymal-B-v0Track a velocity command on flat terrain with the Anymal B robot
Image 32: velocity-rough-anymal-bIsaac-Velocity-Rough-Anymal-B-v0Track a velocity command on rough terrain with the Anymal B robot
Image 33: velocity-flat-anymal-cIsaac-Velocity-Flat-Anymal-C-v0 Isaac-Velocity-Flat-Anymal-C-Direct-v0Track a velocity command on flat terrain with the Anymal C robot
Image 34: velocity-rough-anymal-cIsaac-Velocity-Rough-Anymal-C-v0 Isaac-Velocity-Rough-Anymal-C-Direct-v0Track a velocity command on rough terrain with the Anymal C robot
Image 35: velocity-flat-anymal-dIsaac-Velocity-Flat-Anymal-D-v0Track a velocity command on flat terrain with the Anymal D robot
Image 36: velocity-rough-anymal-dIsaac-Velocity-Rough-Anymal-D-v0Track a velocity command on rough terrain with the Anymal D robot
Image 37: velocity-flat-unitree-a1Isaac-Velocity-Flat-Unitree-A1-v0Track a velocity command on flat terrain with the Unitree A1 robot
Image 38: velocity-rough-unitree-a1Isaac-Velocity-Rough-Unitree-A1-v0Track a velocity command on rough terrain with the Unitree A1 robot
Image 39: velocity-flat-unitree-go1Isaac-Velocity-Flat-Unitree-Go1-v0Track a velocity command on flat terrain with the Unitree Go1 robot
Image 40: velocity-rough-unitree-go1Isaac-Velocity-Rough-Unitree-Go1-v0Track a velocity command on rough terrain with the Unitree Go1 robot
Image 41: velocity-flat-unitree-go2Isaac-Velocity-Flat-Unitree-Go2-v0Track a velocity command on flat terrain with the Unitree Go2 robot
Image 42: velocity-rough-unitree-go2Isaac-Velocity-Rough-Unitree-Go2-v0Track a velocity command on rough terrain with the Unitree Go2 robot
Image 43: velocity-flat-spotIsaac-Velocity-Flat-Spot-v0Track a velocity command on flat terrain with the Boston Dynamics Spot robot
Image 44: velocity-flat-h1Isaac-Velocity-Flat-H1-v0Track a velocity command on flat terrain with the Unitree H1 robot
Image 45: velocity-rough-h1Isaac-Velocity-Rough-H1-v0Track a velocity command on rough terrain with the Unitree H1 robot
Image 46: velocity-flat-g1Isaac-Velocity-Flat-G1-v0Track a velocity command on flat terrain with the Unitree G1 robot
Image 47: velocity-rough-g1Isaac-Velocity-Rough-G1-v0Track a velocity command on rough terrain with the Unitree G1 robot
Image 48: velocity-flat-digitIsaac-Velocity-Flat-Digit-v0Track a velocity command on flat terrain with the Agility Digit robot
Image 49: velocity-rough-digitIsaac-Velocity-Rough-Digit-v0Track a velocity command on rough terrain with the Agility Digit robot
Image 50: tracking-loco-manip-digitIsaac-Tracking-LocoManip-Digit-v0Track a root velocity and hand pose command with the Agility Digit robot

Navigation#

WorldEnvironment IDDescription
Image 51: anymal_c_navIsaac-Navigation-Flat-Anymal-C-v0Navigate towards a target x-y position and heading with the ANYmal C robot.

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.

WorldEnvironment IDDescription
Image 52: quadcopterIsaac-Quadcopter-Direct-v0Fly and hover the Crazyflie copter at a goal point by applying thrust.
Image 53: humanoid_ampIsaac-Humanoid-AMP-Dance-Direct-v0 Isaac-Humanoid-AMP-Run-Direct-v0 Isaac-Humanoid-AMP-Walk-Direct-v0Move 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 observation_space / action_space documentation for more details.

The following tables summarize the different pairs of showcased spaces for the Cartpole and Cartpole-Camera tasks. Replace <OBSERVATION> and <ACTION> with the observation and action spaces to be explored in the task names for training and evaluation.

Showcase spaces for the Cartpole task

Isaac-Cartpole-Showcase-<OBSERVATION>-<ACTION>-Direct-v0

action space BoxDiscreteMultiDiscrete observation

spaceBoxx x x Discretex x x MultiDiscretex x x Dictx x x Tuplex x x

Showcase spaces for the Cartpole-Camera task

Isaac-Cartpole-Camera-Showcase-<OBSERVATION>-<ACTION>-Direct-v0

action space BoxDiscreteMultiDiscrete observation

spaceBoxx x x Discrete--- MultiDiscrete--- Dictx x x Tuplex 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#

WorldEnvironment IDDescription
Image 54: cart-double-pendulumIsaac-Cart-Double-Pendulum-Direct-v0Move 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.

WorldEnvironment IDDescription
Image 55: shadow-hand-overIsaac-Shadow-Hand-Over-Direct-v0Passing an object from one hand over to the other hand

Comprehensive List of Environments#

For environments that have a different task name listed under Inference Task Name, please use the Inference Task Name provided when running play.py or any inferencing workflows. These tasks provide more suitable configurations for inferencing, including reading from an already trained checkpoint and disabling runtime perturbations used for training.

Task NameInference Task NameWorkflow****RL Library Isaac-Ant-Direct-v0 Directrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Ant-v0 Manager Basedrsl_rl (PPO), rl_games (PPO), skrl (PPO), sb3 (PPO) Isaac-Cart-Double-Pendulum-Direct-v0 Directrl_games (PPO), skrl (IPPO, PPO, MAPPO) Isaac-Cartpole-Camera-Showcase-Box-Box-Direct-v0 (Requires running with --enable_cameras)Directskrl (PPO) Isaac-Cartpole-Camera-Showcase-Box-Discrete-Direct-v0 (Requires running with --enable_cameras)Directskrl (PPO) Isaac-Cartpole-Camera-Showcase-Box-MultiDiscrete-Direct-v0 (Requires running with --enable_cameras)Directskrl (PPO) Isaac-Cartpole-Camera-Showcase-Dict-Box-Direct-v0 (Requires running with --enable_cameras)Directskrl (PPO) Isaac-Cartpole-Camera-Showcase-Dict-Discrete-Direct-v0 (Requires running with --enable_cameras)Directskrl (PPO) Isaac-Cartpole-Camera-Showcase-Dict-MultiDiscrete-Direct-v0 (Requires running with --enable_cameras)Directskrl (PPO) Isaac-Cartpole-Camera-Showcase-Tuple-Box-Direct-v0 (Requires running with --enable_cameras)Directskrl (PPO) Isaac-Cartpole-Camera-Showcase-Tuple-Discrete-Direct-v0 (Requires running with --enable_cameras)Directskrl (PPO) Isaac-Cartpole-Camera-Showcase-Tuple-MultiDiscrete-Direct-v0 (Requires running with --enable_cameras)Directskrl (PPO) Isaac-Cartpole-Depth-Camera-Direct-v0 (Requires running with --enable_cameras)Directrl_games (PPO), skrl (PPO) Isaac-Cartpole-Depth-v0 (Requires running with --enable_cameras)Manager Basedrl_games (PPO) Isaac-Cartpole-Direct-v0 Directrl_games (PPO), rsl_rl (PPO), skrl (PPO), sb3 (PPO) Isaac-Cartpole-RGB-Camera-Direct-v0 (Requires running with --enable_cameras)Directrl_games (PPO), skrl (PPO) Isaac-Cartpole-RGB-ResNet18-v0 (Requires running with --enable_cameras)Manager Basedrl_games (PPO) Isaac-Cartpole-RGB-TheiaTiny-v0 (Requires running with --enable_cameras)Manager Basedrl_games (PPO) Isaac-Cartpole-RGB-v0 (Requires running with --enable_cameras)Manager Basedrl_games (PPO) Isaac-Cartpole-Showcase-Box-Box-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Box-Discrete-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Box-MultiDiscrete-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Dict-Box-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Dict-Discrete-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Dict-MultiDiscrete-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Discrete-Box-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Discrete-Discrete-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Discrete-MultiDiscrete-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-MultiDiscrete-Box-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-MultiDiscrete-Discrete-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-MultiDiscrete-MultiDiscrete-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Tuple-Box-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Tuple-Discrete-Direct-v0 Directskrl (PPO) Isaac-Cartpole-Showcase-Tuple-MultiDiscrete-Direct-v0 Directskrl (PPO) Isaac-Cartpole-v0 Manager Basedrl_games (PPO), rsl_rl (PPO), skrl (PPO), sb3 (PPO) Isaac-Factory-GearMesh-Direct-v0 Directrl_games (PPO) Isaac-Factory-NutThread-Direct-v0 Directrl_games (PPO) Isaac-Factory-PegInsert-Direct-v0 Directrl_games (PPO) Isaac-AutoMate-Assembly-Direct-v0 Directrl_games (PPO) Isaac-AutoMate-Disassembly-Direct-v0 Direct Isaac-Forge-GearMesh-Direct-v0 Directrl_games (PPO) Isaac-Forge-NutThread-Direct-v0 Directrl_games (PPO) Isaac-Forge-PegInsert-Direct-v0 Directrl_games (PPO) Isaac-Franka-Cabinet-Direct-v0 Directrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Humanoid-AMP-Dance-Direct-v0 Directskrl (AMP) Isaac-Humanoid-AMP-Run-Direct-v0 Directskrl (AMP) Isaac-Humanoid-AMP-Walk-Direct-v0 Directskrl (AMP) Isaac-Humanoid-Direct-v0 Directrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Humanoid-v0 Manager Basedrsl_rl (PPO), rl_games (PPO), skrl (PPO), sb3 (PPO) Isaac-Lift-Cube-Franka-IK-Abs-v0 Manager Based Isaac-Lift-Cube-Franka-IK-Rel-v0 Manager Based Isaac-Lift-Cube-Franka-v0 Isaac-Lift-Cube-Franka-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO), rl_games (PPO), sb3 (PPO) Isaac-Lift-Teddy-Bear-Franka-IK-Abs-v0 Manager Based Isaac-Tracking-LocoManip-Digit-v0 Isaac-Tracking-LocoManip-Digit-Play-v0 Manager Basedrsl_rl (PPO) Isaac-Navigation-Flat-Anymal-C-v0 Isaac-Navigation-Flat-Anymal-C-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Open-Drawer-Franka-IK-Abs-v0 Manager Based Isaac-Open-Drawer-Franka-IK-Rel-v0 Manager Based Isaac-Open-Drawer-Franka-v0 Isaac-Open-Drawer-Franka-Play-v0 Manager Basedrsl_rl (PPO), rl_games (PPO), skrl (PPO) Isaac-Quadcopter-Direct-v0 Directrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Reach-Franka-IK-Abs-v0 Manager Based Isaac-Reach-Franka-IK-Rel-v0 Manager Based Isaac-Reach-Franka-OSC-v0 Isaac-Reach-Franka-OSC-Play-v0 Manager Basedrsl_rl (PPO) Isaac-Reach-Franka-v0 Isaac-Reach-Franka-Play-v0 Manager Basedrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Reach-UR10-v0 Isaac-Reach-UR10-Play-v0 Manager Basedrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Deploy-Reach-UR10e-v0 Isaac-Deploy-Reach-UR10e-Play-v0 Manager Basedrsl_rl (PPO) Isaac-Repose-Cube-Allegro-Direct-v0 Directrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Repose-Cube-Allegro-NoVelObs-v0 Isaac-Repose-Cube-Allegro-NoVelObs-Play-v0 Manager Basedrsl_rl (PPO), rl_games (PPO), skrl (PPO) Isaac-Repose-Cube-Allegro-v0 Isaac-Repose-Cube-Allegro-Play-v0 Manager Basedrsl_rl (PPO), rl_games (PPO), skrl (PPO) Isaac-Repose-Cube-Shadow-Direct-v0 Directrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Repose-Cube-Shadow-OpenAI-FF-Direct-v0 Directrl_games (FF), rsl_rl (PPO), skrl (PPO) Isaac-Repose-Cube-Shadow-OpenAI-LSTM-Direct-v0 Directrl_games (LSTM) Isaac-Repose-Cube-Shadow-Vision-Direct-v0 (Requires running with --enable_cameras)Isaac-Repose-Cube-Shadow-Vision-Direct-Play-v0 (Requires running with --enable_cameras)Directrsl_rl (PPO), rl_games (VISION) Isaac-Shadow-Hand-Over-Direct-v0 Directrl_games (PPO), skrl (IPPO, PPO, MAPPO) Isaac-Stack-Cube-Franka-IK-Rel-v0 Manager Based Isaac-Dexsuite-Kuka-Allegro-Lift-v0 Isaac-Dexsuite-Kuka-Allegro-Lift-Play-v0 Manager Basedrl_games (PPO), rsl_rl (PPO) Isaac-Dexsuite-Kuka-Allegro-Reorient-v0 Isaac-Dexsuite-Kuka-Allegro-Reorient-Play-v0 Manager Basedrl_games (PPO), rsl_rl (PPO) Isaac-Stack-Cube-Franka-v0 Manager Based Isaac-Stack-Cube-Instance-Randomize-Franka-IK-Rel-v0 Manager Based Isaac-Stack-Cube-Instance-Randomize-Franka-v0 Manager Based Isaac-Stack-Cube-UR10-Long-Suction-IK-Rel-v0 Manager Based Isaac-Stack-Cube-UR10-Short-Suction-IK-Rel-v0 Manager Based Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow-v0 Manager Based Isaac-Stack-Cube-Galbot-Right-Arm-Suction-RmpFlow-v0 Manager Based Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-v0 Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-Play-v0 Manager Based Isaac-Place-Mug-Agibot-Left-Arm-RmpFlow-v0 Manager Based Isaac-Place-Toy2Box-Agibot-Right-Arm-RmpFlow-v0 Manager Based Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-RmpFlow-v0 Manager Based Isaac-Stack-Cube-Galbot-Right-Arm-Suction-RmpFlow-v0 Manager Based Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-v0 Isaac-Stack-Cube-Galbot-Left-Arm-Gripper-Visuomotor-Play-v0 Manager Based Isaac-Place-Mug-Agibot-Left-Arm-RmpFlow-v0 Manager Based Isaac-Place-Toy2Box-Agibot-Right-Arm-RmpFlow-v0 Manager Based Isaac-Velocity-Flat-Anymal-B-v0 Isaac-Velocity-Flat-Anymal-B-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Flat-Anymal-C-Direct-v0 Directrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Velocity-Flat-Anymal-C-v0 Isaac-Velocity-Flat-Anymal-C-Play-v0 Manager Basedrsl_rl (PPO), rl_games (PPO), skrl (PPO) Isaac-Velocity-Flat-Anymal-D-v0 Isaac-Velocity-Flat-Anymal-D-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Flat-Cassie-v0 Isaac-Velocity-Flat-Cassie-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Flat-Digit-v0 Isaac-Velocity-Flat-Digit-Play-v0 Manager Basedrsl_rl (PPO) Isaac-Velocity-Flat-G1-v0 Isaac-Velocity-Flat-G1-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Flat-H1-v0 Isaac-Velocity-Flat-H1-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Flat-Spot-v0 Isaac-Velocity-Flat-Spot-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Flat-Unitree-A1-v0 Isaac-Velocity-Flat-Unitree-A1-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO), sb3 (PPO) Isaac-Velocity-Flat-Unitree-Go1-v0 Isaac-Velocity-Flat-Unitree-Go1-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Flat-Unitree-Go2-v0 Isaac-Velocity-Flat-Unitree-Go2-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Rough-Anymal-B-v0 Isaac-Velocity-Rough-Anymal-B-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Rough-Anymal-C-Direct-v0 Directrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Velocity-Rough-Anymal-C-v0 Isaac-Velocity-Rough-Anymal-C-Play-v0 Manager Basedrl_games (PPO), rsl_rl (PPO), skrl (PPO) Isaac-Velocity-Rough-Anymal-D-v0 Isaac-Velocity-Rough-Anymal-D-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Rough-Cassie-v0 Isaac-Velocity-Rough-Cassie-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Rough-Digit-v0 Isaac-Velocity-Rough-Digit-Play-v0 Manager Basedrsl_rl (PPO) Isaac-Velocity-Rough-G1-v0 Isaac-Velocity-Rough-G1-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Rough-H1-v0 Isaac-Velocity-Rough-H1-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Rough-Unitree-A1-v0 Isaac-Velocity-Rough-Unitree-A1-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO), sb3 (PPO) Isaac-Velocity-Rough-Unitree-Go1-v0 Isaac-Velocity-Rough-Unitree-Go1-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO) Isaac-Velocity-Rough-Unitree-Go2-v0 Isaac-Velocity-Rough-Unitree-Go2-Play-v0 Manager Basedrsl_rl (PPO), skrl (PPO)

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