Isaac Lab is a unified and modular framework for robot learning that aims to simplify common workflows in robotics research (such as RL, learning from demonstrations, and motion planning). It is built upon NVIDIA Isaac Sim to leverage the latest simulation capabilities for photo-realistic scenes and fast and accurate simulation.
Please refer to our documentation page to learn more about the installation steps, features, tutorials, and how to set up your project with Isaac Lab.
-
[17.04.2024] v0.3.0: Several improvements and bug fixes to the framework. Includes cabinet opening and dexterous manipulation environments, terrain-aware patch sampling, and animation recording.
-
[22.12.2023] v0.2.0: Significant breaking updates to enhance the modularity and user-friendliness of the framework. Also includes procedural terrain generation, warp-based custom ray-casters, and legged-locomotion environments.
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.
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.
- 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.
NVIDIA Isaac Sim is available freely under individual license. For more information about its license terms, please check here.
The Isaac Lab framework is released under BSD-3 License. The license files of its dependencies and assets are present in the docs/licenses directory.
If you use this framework in your work, please cite this paper:
@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}
}