Unified framework for robot learning built on NVIDIA Isaac Sim
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.
Isaac Lab offers a comprehensive set of tools and environments designed to facilitate robot learning:
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:
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 |
v2.1.0 |
Isaac Sim 4.5 |
v2.0.2 |
Isaac Sim 4.5 |
v2.0.1 |
Isaac Sim 4.5 |
v2.0.0 |
Isaac Sim 4.5 |
feature/isaacsim_5_0 branch |
Isaac Sim 5.0 |
Note that the feature/isaacsim_5_0
will contain active updates and may contain some breaking changes
until the official Isaac Lab 2.2 release.
It currently requires the Isaac Sim 5.0 branch available on GitHub built from source.
Please refer to the README in the feature/isaacsim_5_0
branch for instructions for using Isaac Lab with Isaac Sim 5.0.
We are actively working on introducing backwards compatibility support for Isaac Sim 4.5 for this branch.
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.
We encourage you to utilize our Show & Tell area in the
Discussions
section of this repository. This space is designed for you to:
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.
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.
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 discuss potential opportunities
for broader dissemination of your work.
Join us in building a vibrant, collaborative ecosystem where creativity and technology intersect. Your
contributions can make a significant impact on the Isaac Lab community and beyond!
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.
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}
}