robot_lab

Overview

robot_lab is a RL extension library for robots, based on IsaacLab. It allows you to develop in an isolated environment, outside of the core Isaac Lab repository.

The table below lists all available environments:

Category	Robot Model	Environment Name (ID)	Screenshot
Quadruped	Anymal D	RobotLab-Isaac-Velocity-Rough-Anymal-D-v0
	Unitree Go2	RobotLab-Isaac-Velocity-Rough-Unitree-Go2-v0
	Unitree B2	RobotLab-Isaac-Velocity-Rough-Unitree-B2-v0
	Unitree A1	RobotLab-Isaac-Velocity-Rough-Unitree-A1-v0
	Deeprobotics Lite3	RobotLab-Isaac-Velocity-Rough-Deeprobotics-Lite3-v0
	Zsibot ZSL1	RobotLab-Isaac-Velocity-Rough-Zsibot-ZSL1-v0
	Magiclab MagicDog	RobotLab-Isaac-Velocity-Rough-MagicLab-Dog-v0
Wheeled	Unitree Go2W	RobotLab-Isaac-Velocity-Rough-Unitree-Go2W-v0
	Unitree B2W	RobotLab-Isaac-Velocity-Rough-Unitree-B2W-v0
	Deeprobotics M20	RobotLab-Isaac-Velocity-Rough-Deeprobotics-M20-v0
	DDTRobot Tita	RobotLab-Isaac-Velocity-Rough-DDTRobot-Tita-v0
	Zsibot ZSL1W	RobotLab-Isaac-Velocity-Rough-Zsibot-ZSL1W-v0
	Magiclab MagicDog-W	RobotLab-Isaac-Velocity-Rough-MagicLab-Dog-W-v0
Humanoid	Unitree G1	RobotLab-Isaac-Velocity-Rough-Unitree-G1-v0
	Unitree H1	RobotLab-Isaac-Velocity-Rough-Unitree-H1-v0
	FFTAI GR1T1	RobotLab-Isaac-Velocity-Rough-FFTAI-GR1T1-v0
	FFTAI GR1T2	RobotLab-Isaac-Velocity-Rough-FFTAI-GR1T2-v0
	Booster T1	RobotLab-Isaac-Velocity-Rough-Booster-T1-v0
	RobotEra Xbot	RobotLab-Isaac-Velocity-Rough-RobotEra-Xbot-v0
	Openloong Loong	RobotLab-Isaac-Velocity-Rough-Openloong-Loong-v0
	RoboParty ATOM01	RobotLab-Isaac-Velocity-Rough-RoboParty-ATOM01-v0
	Magiclab MagicBot-Gen1	RobotLab-Isaac-Velocity-Rough-MagicLab-Bot-Gen1-v0
	Magiclab MagicBot-Z1	RobotLab-Isaac-Velocity-Rough-MagicLab-Bot-Z1-v0

Note

If you want to run policy in gazebo or real robot, please use rl_sar project.

Discuss in Github Discussion.

Version Dependency

robot_lab Version	Isaac Lab Version	Isaac Sim Version
main branch	main branch	Isaac Sim 4.5 / 5.0
v2.2.0	v2.2.0	Isaac Sim 4.5 / 5.0
v2.1.1	v2.1.1	Isaac Sim 4.5
v1.1	v1.4.1	Isaac Sim 4.2

Installation

• Install Isaac Lab by following the installation guide. We recommend using the conda installation as it simplifies calling Python scripts from the terminal.

• Clone this repository separately from the Isaac Lab installation (i.e. outside the IsaacLab directory):

  git clone https://github.com/fan-ziqi/robot_lab.git

• Using a python interpreter that has Isaac Lab installed, install the library

  python -m pip install -e source/robot_lab

• Verify that the extension is correctly installed by running the following command to print all the available environments in the extension:

  python scripts/tools/list_envs.py

Set up IDE (Optional, click to expand)

To setup the IDE, please follow these instructions:

• Run VSCode Tasks, by pressing Ctrl+Shift+P, selecting Tasks: Run Task and running the setup_python_env in the drop down menu. When running this task, you will be prompted to add the absolute path to your Isaac Sim installation.

If everything executes correctly, it should create a file .python.env in the .vscode directory. The file contains the python paths to all the extensions provided by Isaac Sim and Omniverse. This helps in indexing all the python modules for intelligent suggestions while writing code.

Setup as Omniverse Extension (Optional, click to expand)

We provide an example UI extension that will load upon enabling your extension defined in source/robot_lab/robot_lab/ui_extension_example.py.

To enable your extension, follow these steps:

1. Add the search path of your repository to the extension manager:

   • Navigate to the extension manager using Window -> Extensions.
   • Click on the Hamburger Icon (☰), then go to Settings.
   • In the Extension Search Paths, enter the absolute path to robot_lab/source
   • If not already present, in the Extension Search Paths, enter the path that leads to Isaac Lab's extension directory directory (IsaacLab/source)
   • Click on the Hamburger Icon (☰), then click Refresh.

2. Search and enable your extension:

   • Find your extension under the Third Party category.
   • Toggle it to enable your extension.

Docker setup

Click to expand

Building Isaac Lab Base Image

Currently, we don't have the Docker for Isaac Lab publicly available. Hence, you'd need to build the docker image for Isaac Lab locally by following the steps here.

Once you have built the base Isaac Lab image, you can check it exists by doing:

docker images

# Output should look something like:
#
# REPOSITORY                       TAG       IMAGE ID       CREATED          SIZE
# isaac-lab-base                   latest    28be62af627e   32 minutes ago   18.9GB

Building robot_lab Image

Following above, you can build the docker container for this project. It is called robot-lab. However, you can modify this name inside the docker/docker-compose.yaml.

cd docker
docker compose --env-file .env.base --file docker-compose.yaml build robot-lab

You can verify the image is built successfully using the same command as earlier:

docker images

# Output should look something like:
#
# REPOSITORY                       TAG       IMAGE ID       CREATED             SIZE
# robot-lab                        latest    00b00b647e1b   2 minutes ago       18.9GB
# isaac-lab-base                   latest    892938acb55c   About an hour ago   18.9GB

Running the container

After building, the usual next step is to start the containers associated with your services. You can do this with:

docker compose --env-file .env.base --file docker-compose.yaml up

This will start the services defined in your docker-compose.yaml file, including robot-lab.

If you want to run it in detached mode (in the background), use:

docker compose --env-file .env.base --file docker-compose.yaml up -d

Interacting with a running container

If you want to run commands inside the running container, you can use the exec command:

docker exec --interactive --tty -e DISPLAY=${DISPLAY} robot-lab /bin/bash

Shutting down the container

When you are done or want to stop the running containers, you can bring down the services:

docker compose --env-file .env.base --file docker-compose.yaml down

This stops and removes the containers, but keeps the images.

Try examples

You can use the following commands to run all environments:

RSL-RL:

# Train
python scripts/reinforcement_learning/rsl_rl/train.py --task=<ENV_NAME> --headless

# Play
python scripts/reinforcement_learning/rsl_rl/play.py --task=<ENV_NAME>

CusRL (Experimental):

# Train
python scripts/reinforcement_learning/cusrl/train.py --task=<ENV_NAME> --headless

# Play
python scripts/reinforcement_learning/cusrl/play.py --task=<ENV_NAME>

Others (Experimental)

• Train AMP Dance for Unitree G1

  # Train
  python scripts/reinforcement_learning/skrl/train.py --task=RobotLab-Isaac-G1-AMP-Dance-Direct-v0 --algorithm AMP --headless
  
  # Play
  python scripts/reinforcement_learning/skrl/play.py --task=RobotLab-Isaac-G1-AMP-Dance-Direct-v0 --algorithm AMP --num_envs=32

• Train Handstand for Unitree A1

  # Train
  python scripts/reinforcement_learning/rsl_rl/train.py --task=RobotLab-Isaac-Velocity-Flat-HandStand-Unitree-A1-v0 --headless
  
  # Play
  python scripts/reinforcement_learning/rsl_rl/play.py --task=RobotLab-Isaac-Velocity-Flat-HandStand-Unitree-A1-v0

• Train Anymal D with symmetry

  # Train
  python scripts/reinforcement_learning/rsl_rl/train.py --task=RobotLab-Isaac-Velocity-Rough-Anymal-D-v0 --headless --agent=rsl_rl_with_symmetry_cfg_entry_point --run_name=ppo_with_symmetry_data_augmentation agent.algorithm.symmetry_cfg.use_data_augmentation=true
  # Train
  python scripts/reinforcement_learning/rsl_rl/play.py --task=RobotLab-Isaac-Velocity-Rough-Anymal-D-v0 --agent=rsl_rl_with_symmetry_cfg_entry_point --run_name=ppo_with_symmetry_data_augmentation agent.algorithm.symmetry_cfg.use_data_augmentation=true

Note

If you want to control a SINGLE ROBOT with the keyboard during playback, add --keyboard at the end of the play script.

Key bindings:
====================== ========================= ========================
Command                Key (+ve axis)            Key (-ve axis)
====================== ========================= ========================
Move along x-axis      Numpad 8 / Arrow Up       Numpad 2 / Arrow Down
Move along y-axis      Numpad 4 / Arrow Right    Numpad 6 / Arrow Left
Rotate along z-axis    Numpad 7 / Z              Numpad 9 / X
====================== ========================= ========================

• You can change Rough to Flat in the above configs.

• Record video of a trained agent (requires installing ffmpeg), add --video --video_length 200

• Play/Train with 32 environments, add --num_envs 32

• Play on specific folder or checkpoint, add --load_run run_folder_name --checkpoint /PATH/TO/model.pt

• Resume training from folder or checkpoint, add --resume --load_run run_folder_name --checkpoint /PATH/TO/model.pt

• To train with multiple GPUs, use the following command, where --nproc_per_node represents the number of available GPUs:

  python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/rsl_rl/train.py --task=<ENV_NAME> --headless --distributed

• To scale up training beyond multiple GPUs on a single machine, it is also possible to train across multiple nodes. To train across multiple nodes/machines, it is required to launch an individual process on each node.

  For the master node, use the following command, where --nproc_per_node represents the number of available GPUs, and --nnodes represents the number of nodes:

  python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=localhost:5555 scripts/reinforcement_learning/rsl_rl/train.py --task=<ENV_NAME> --headless --distributed

  Note that the port (5555) can be replaced with any other available port. For non-master nodes, use the following command, replacing --node_rank with the index of each machine:

  python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=ip_of_master_machine:5555 scripts/reinforcement_learning/rsl_rl/train.py --task=<ENV_NAME> --headless --distributed

Add your own robot

This repository supports direct import of URDF, XACRO, and MJCF robot models without requiring pre-conversion to USD format.

from robot_lab.assets.utils.usd_converter import (  # noqa: F401
    mjcf_to_usd,
    spawn_from_lazy_usd,
    urdf_to_usd,
    xacro_to_usd,
)

YOUR_ROBOT_CFG = ArticulationCfg(
    spawn=sim_utils.UsdFileCfg(
        # for urdf
        func=spawn_from_lazy_usd,
        usd_path=urdf_to_usd(  # type: ignore
            file_path=f"{ISAACLAB_ASSETS_DATA_DIR}/Robots/your_robot/your_robot.urdf",
            merge_joints=True,
            fix_base=False,
        ),
        # for xacro
        func=spawn_from_lazy_usd,
        usd_path=xacro_to_usd(  # type: ignore
            file_path=f"{ISAACLAB_ASSETS_DATA_DIR}/Robots/your_robot/your_robot.xacro",
            merge_joints=True,
            fix_base=False,
        ),
        # for mjcf
        func=spawn_from_lazy_usd,
        usd_path=mjcf_to_usd(  # type: ignore
            file_path=f"{ISAACLAB_ASSETS_DATA_DIR}/Robots/your_robot/your_robot.xml",
            import_sites=True,
            fix_base=False,
        ),
        # ... other configuration parameters ...
    ),
    # ... other configuration parameters ...
)

Check your model import compatibility using:

python scripts/tools/check_robot.py {urdf,mjcf,xacro} path_to_your_model_file

Using the core framework developed as part of Isaac Lab, we provide various learning environments for robotics research. These environments follow the gym.Env API from OpenAI Gym version 0.21.0. The environments are registered using the Gym registry.

Each environment's name is composed of Isaac-<Task>-<Robot>-v<X>, where <Task> indicates the skill to learn in the environment, <Robot> indicates the embodiment of the acting agent, and <X> represents the version of the environment (which can be used to suggest different observation or action spaces).

The environments are configured using either Python classes (wrapped using configclass decorator) or through YAML files. The template structure of the environment is always put at the same level as the environment file itself. However, its various instances are included in directories within the environment directory itself. This looks like as follows:

source/robot_lab/tasks/manager_based/locomotion/
├── __init__.py
└── velocity
    ├── config
    │   └── unitree_a1
    │       ├── agent  # <- this is where we store the learning agent configurations
    │       ├── __init__.py  # <- this is where we register the environment and configurations to gym registry
    │       ├── flat_env_cfg.py
    │       └── rough_env_cfg.py
    ├── __init__.py
    └── velocity_env_cfg.py  # <- this is the base task configuration

The environments are then registered in the source/robot_lab/tasks/manager_based/locomotion/velocity/config/unitree_a1/__init__.py:

gym.register(
    id="RobotLab-Isaac-Velocity-Flat-Unitree-A1-v0",
    entry_point="isaaclab.envs:ManagerBasedRLEnv",
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": f"{__name__}.flat_env_cfg:UnitreeA1FlatEnvCfg",
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeA1FlatPPORunnerCfg",
        "cusrl_cfg_entry_point": f"{agents.__name__}.cusrl_ppo_cfg:UnitreeA1FlatTrainerCfg",
    },
)

gym.register(
    id="RobotLab-Isaac-Velocity-Rough-Unitree-A1-v0",
    entry_point="isaaclab.envs:ManagerBasedRLEnv",
    disable_env_checker=True,
    kwargs={
        "env_cfg_entry_point": f"{__name__}.rough_env_cfg:UnitreeA1RoughEnvCfg",
        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:UnitreeA1RoughPPORunnerCfg",
        "cusrl_cfg_entry_point": f"{agents.__name__}.cusrl_ppo_cfg:UnitreeA1RoughTrainerCfg",
    },
)

Tensorboard

To view tensorboard, run:

tensorboard --logdir=logs

Code formatting

A pre-commit template is given to automatically format the code.

To install pre-commit:

pip install pre-commit

Then you can run pre-commit with:

pre-commit run --all-files

Troubleshooting

Pylance Missing Indexing of Extensions

In some VsCode versions, the indexing of part of the extensions is missing. In this case, add the path to your extension in .vscode/settings.json under the key "python.analysis.extraPaths".

Note: Replace <path-to-isaac-lab> with your own IsaacLab path.

{
    "python.analysis.extraPaths": [
        "${workspaceFolder}/source/robot_lab",
        "/<path-to-isaac-lab>/source/isaaclab",
        "/<path-to-isaac-lab>/source/isaaclab_assets",
        "/<path-to-isaac-lab>/source/isaaclab_mimic",
        "/<path-to-isaac-lab>/source/isaaclab_rl",
        "/<path-to-isaac-lab>/source/isaaclab_tasks",
    ]
}

Citation

Please cite the following if you use this code or parts of it:

@software{fan-ziqi2024robot_lab,
  author = {Ziqi Fan},
  title = {robot_lab: RL Extension Library for Robots, Based on IsaacLab.},
  url = {https://github.com/fan-ziqi/robot_lab},
  year = {2024}
}

Acknowledgements

The project uses some code from the following open-source code repositories:

• linden713/humanoid_amp