isaac-sim/IsaacLab

This branch targets Isaac Sim 6.2. For installation instructions, see the Isaac Lab documentation.

Note that this branch is currently under active development and may experience breaking changes or error messages. Performance issues and regressions may also be observed in some use cases.

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.

A detailed description of Isaac Lab can be found in our arXiv paper.

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 more than 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.

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 and how-to guides
• Available environments

We continuously benchmark Isaac Lab across different physics backends, renderers, and data types. The Isaac Lab Performance Dashboard provides interactive charts showing preset comparison results, performance history, and environment scaling data from our internal CI/CD benchmarks.

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.

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:

• 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.

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.

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!

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.

Note that full-featured workflows (PhysX, RTX rendering, ROS, URDF/MJCF importers) require Isaac Sim, which includes components under proprietary licensing terms. Kit-less Newton workflows do not require Isaac Sim. Please see the Isaac Sim license for details.

Note that the isaaclab_mimic extension requires cuRobo, which has proprietary licensing terms that can be found in docs/licenses/dependencies/cuRobo-license.txt.

If you use Isaac Lab in your research, please cite the technical report:

@article{mittal2025isaaclab,
  title={Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning},
  author={Mayank Mittal and Pascal Roth and James Tigue and Antoine Richard and Octi Zhang and Peter Du and Antonio Serrano-Muñoz and Xinjie Yao and René Zurbrügg and Nikita Rudin and Lukasz Wawrzyniak and Milad Rakhsha and Alain Denzler and Eric Heiden and Ales Borovicka and Ossama Ahmed and Iretiayo Akinola and Abrar Anwar and Mark T. Carlson and Ji Yuan Feng and Animesh Garg and Renato Gasoto and Lionel Gulich and Yijie Guo and M. Gussert and Alex Hansen and Mihir Kulkarni and Chenran Li and Wei Liu and Viktor Makoviychuk and Grzegorz Malczyk and Hammad Mazhar and Masoud Moghani and Adithyavairavan Murali and Michael Noseworthy and Alexander Poddubny and Nathan Ratliff and Welf Rehberg and Clemens Schwarke and Ritvik Singh and James Latham Smith and Bingjie Tang and Ruchik Thaker and Matthew Trepte and Karl Van Wyk and Fangzhou Yu and Alex Millane and Vikram Ramasamy and Remo Steiner and Sangeeta Subramanian and Clemens Volk and CY Chen and Neel Jawale and Ashwin Varghese Kuruttukulam and Michael A. Lin and Ajay Mandlekar and Karsten Patzwaldt and John Welsh and Huihua Zhao and Fatima Anes and Jean-Francois Lafleche and Nicolas Moënne-Loccoz and Soowan Park and Rob Stepinski and Dirk Van Gelder and Chris Amevor and Jan Carius and Jumyung Chang and Anka He Chen and Pablo de Heras Ciechomski and Gilles Daviet and Mohammad Mohajerani and Julia von Muralt and Viktor Reutskyy and Michael Sauter and Simon Schirm and Eric L. Shi and Pierre Terdiman and Kenny Vilella and Tobias Widmer and Gordon Yeoman and Tiffany Chen and Sergey Grizan and Cathy Li and Lotus Li and Connor Smith and Rafael Wiltz and Kostas Alexis and Yan Chang and David Chu and Linxi "Jim" Fan and Farbod Farshidian and Ankur Handa and Spencer Huang and Marco Hutter and Yashraj Narang and Soha Pouya and Shiwei Sheng and Yuke Zhu and Miles Macklin and Adam Moravanszky and Philipp Reist and Yunrong Guo and David Hoeller and Gavriel State},
  journal={arXiv preprint arXiv:2511.04831},
  year={2025},
  url={https://arxiv.org/abs/2511.04831}
}

Isaac Lab development initiated from the Orbit framework. We gratefully acknowledge the authors of Orbit for their foundational contributions.