Isaac Lab

Isaac Lab is a GPU-accelerated, open-source framework designed to unify and simplify robotics research workflows, such as reinforcement learning, imitation learning, and motion planning. Built on NVIDIA Isaac Sim, it combines fast and accurate physics and sensor simulation, making it an ideal choice for sim-to-real transfer in robotics.

Isaac Lab provides developers with a range of essential features for accurate sensor simulation, such as RTX-based cameras, LIDAR, or contact sensors. The framework's GPU acceleration enables users to run complex simulations and computations faster, which is key for iterative processes like reinforcement learning and data-intensive tasks. Moreover, Isaac Lab can run locally or be distributed across the cloud, offering flexibility for large-scale deployments.

Key Features

Isaac Lab offers a comprehensive set of tools and environments designed to facilitate robot learning:

• Robots: A diverse collection of robots, from manipulators, quadrupeds, to humanoids, with 16 commonly available models.
• Environments: Ready-to-train implementations of more than 30 environments, which can be trained with popular reinforcement learning frameworks such as RSL RL, SKRL, RL Games, or Stable Baselines. We also support multi-agent reinforcement learning.
• Physics: Rigid bodies, articulated systems, deformable objects
• Sensors: RGB/depth/segmentation cameras, camera annotations, IMU, contact sensors, ray casters.

Getting Started

Getting Started with Open-Source Isaac Sim

Isaac Sim is now open source and available on GitHub!

For detailed Isaac Sim installation instructions, please refer to Isaac Sim README.

1. Clone Isaac Sim

   git clone https://github.com/isaac-sim/IsaacSim.git

2. Build Isaac Sim

   cd IsaacSim ./build.sh

   On Windows, please use build.bat instead.

3. Clone Isaac Lab

   cd .. git clone https://github.com/isaac-sim/IsaacLab.git cd isaaclab

4. Set up symlink in Isaac Lab

   Linux:

   ln -s ../IsaacSim/_build/linux-x86_64/release _isaac_sim

   Windows:

   mklink /D _isaac_sim ..\IsaacSim\_build\windows-x86_64\release

5. Install Isaac Lab

   Linux:

   ./isaaclab.sh -i

   Windows:

   isaaclab.bat -i

6. [Optional] Set up a virtual python environment (e.g. for Conda)

   Linux:

   source _isaac_sim/setup_conda_env.sh

   Windows:

   _isaac_sim\setup_python_env.bat

7. Train!

   Linux:

   ./isaaclab.sh -p scripts/reinforcement_learning/skrl/train.py --task Isaac-Ant-v0 --headless

   Windows:

   isaaclab.bat -p scripts\reinforcement_learning\skrl\train.py --task Isaac-Ant-v0 --headless

Documentation

Our documentation page provides everything you need to get started, including detailed tutorials and step-by-step guides. Follow these links to learn more about:

• Installation steps
• Reinforcement learning
• Tutorials
• Available environments

Isaac Sim Version Dependency

Isaac Lab is built on top of Isaac Sim and requires specific versions of Isaac Sim that are compatible with each release of Isaac Lab. Below, we outline the recent Isaac Lab releases and GitHub branches and their corresponding dependency versions for Isaac Sim.

| Isaac Lab Version | Isaac Sim Version | | ----------------------------- | ------------------- | | main branch | Isaac Sim 4.5 / 5.0 | | v2.2.X | Isaac Sim 4.5 / 5.0 | | v2.1.X | Isaac Sim 4.5 | | v2.0.X | Isaac Sim 4.5 |

Contributing to Isaac Lab

We wholeheartedly welcome contributions from the community to make this framework mature and useful for everyone. These may happen as bug reports, feature requests, or code contributions. For details, please check our contribution guidelines.

Show & Tell: Share Your Inspiration

We encourage you to utilize our Show & Tell area in the Discussions section of this repository. This space is designed for you to:

• Share the tutorials you've created
• Showcase your learning content
• Present exciting projects you've developed

By sharing your work, you'll inspire others and contribute to the collective knowledge of our community. Your contributions can spark new ideas and collaborations, fostering innovation in robotics and simulation.

Troubleshooting

Please see the troubleshooting section for common fixes or submit an issue.

For issues related to Isaac Sim, we recommend checking its documentation or opening a question on its forums.

Support

• Please use GitHub Discussions for discussing ideas, asking questions, and requests for new features.
• Github Issues should only be used to track executable pieces of work with a definite scope and a clear deliverable. These can be fixing bugs, documentation issues, new features, or general updates.

Connect with the NVIDIA Omniverse Community

Do you have a project or resource you'd like to share more widely? We'd love to hear from you! Reach out to the NVIDIA Omniverse Community team at OmniverseCommunity@nvidia.com to explore opportunities to spotlight your work.

You can also join the conversation on the Omniverse Discord to connect with other developers, share your projects, and help grow a vibrant, collaborative ecosystem where creativity and technology intersect. Your contributions can make a meaningful impact on the Isaac Lab community and beyond!

License

The Isaac Lab framework is released under BSD-3 License. The isaaclab_mimic extension and its corresponding standalone scripts are released under Apache 2.0. The license files of its dependencies and assets are present in the docs/licenses directory.

Acknowledgement

Isaac Lab development initiated from the Orbit framework. We would appreciate if you would cite it in academic publications as well:

@article{mittal2023orbit, author={Mittal, Mayank and Yu, Calvin and Yu, Qinxi and Liu, Jingzhou and Rudin, Nikita and Hoeller, David and Yuan, Jia Lin and Singh, Ritvik and Guo, Yunrong and Mazhar, Hammad and Mandlekar, Ajay and Babich, Buck and State, Gavriel and Hutter, Marco and Garg, Animesh}, journal={IEEE Robotics and Automation Letters}, title={Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments}, year={2023}, volume={8}, number={6}, pages={3740-3747}, doi={10.1109/LRA.2023.3270034} }