[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71073":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":30,"readmeContent":31,"aiSummary":32,"trendingCount":16,"starSnapshotCount":16,"syncStatus":33,"lastSyncTime":34,"discoverSource":35},71073,"IsaacLab","isaac-sim\u002FIsaacLab","isaac-sim","Unified framework for robot learning built on NVIDIA Isaac Sim","https:\u002F\u002Fisaac-sim.github.io\u002FIsaacLab",null,"Python",7411,3631,63,386,0,44,105,273,132,41,"BSD 3-Clause \"New\" or \"Revised\" License",false,"main",true,[7,27,28,29],"omniverse-kit-extension","robot-learning","robotics","2026-06-12 02:02:47","![Isaac Lab](docs\u002Fsource\u002F_static\u002Fisaaclab.jpg)\n\n---\n\n# Isaac Lab\n\n[![IsaacSim](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIsaacSim-5.1.0-silver.svg)](https:\u002F\u002Fdocs.isaacsim.omniverse.nvidia.com\u002Flatest\u002Findex.html)\n[![Python](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.11-blue.svg)](https:\u002F\u002Fdocs.python.org\u002F3\u002Fwhatsnew\u002F3.11.html)\n[![Linux platform](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fplatform-linux--64-orange.svg)](https:\u002F\u002Freleases.ubuntu.com\u002F22.04\u002F)\n[![Windows platform](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fplatform-windows--64-orange.svg)](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002F)\n[![pre-commit](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fisaac-sim\u002FIsaacLab\u002Fpre-commit.yaml?logo=pre-commit&logoColor=white&label=pre-commit&color=brightgreen)](https:\u002F\u002Fgithub.com\u002Fisaac-sim\u002FIsaacLab\u002Factions\u002Fworkflows\u002Fpre-commit.yaml)\n[![docs status](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fisaac-sim\u002FIsaacLab\u002Fdocs.yaml?label=docs&color=brightgreen)](https:\u002F\u002Fgithub.com\u002Fisaac-sim\u002FIsaacLab\u002Factions\u002Fworkflows\u002Fdocs.yaml)\n[![License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-BSD--3-yellow.svg)](https:\u002F\u002Fopensource.org\u002Flicenses\u002FBSD-3-Clause)\n[![License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-Apache--2.0-yellow.svg)](https:\u002F\u002Fopensource.org\u002Flicense\u002Fapache-2-0)\n\n\n**Isaac Lab** is a GPU-accelerated, open-source framework designed to unify and simplify robotics research workflows,\nsuch as reinforcement learning, imitation learning, and motion planning. Built on [NVIDIA Isaac Sim](https:\u002F\u002Fdocs.isaacsim.omniverse.nvidia.com\u002Flatest\u002Findex.html),\nit combines fast and accurate physics and sensor simulation, making it an ideal choice for sim-to-real\ntransfer in robotics.\n\nIsaac Lab provides developers with a range of essential features for accurate sensor simulation, such as RTX-based\ncameras, LIDAR, or contact sensors. The framework's GPU acceleration enables users to run complex simulations and\ncomputations faster, which is key for iterative processes like reinforcement learning and data-intensive tasks.\nMoreover, Isaac Lab can run locally or be distributed across the cloud, offering flexibility for large-scale deployments.\n\nA detailed description of Isaac Lab can be found in our [arXiv paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.04831).\n\n## Key Features\n\nIsaac Lab offers a comprehensive set of tools and environments designed to facilitate robot learning:\n\n- **Robots**: A diverse collection of robots, from manipulators, quadrupeds, to humanoids, with more than 16 commonly available models.\n- **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.\n- **Physics**: Rigid bodies, articulated systems, deformable objects\n- **Sensors**: RGB\u002Fdepth\u002Fsegmentation cameras, camera annotations, IMU, contact sensors, ray casters.\n\n\n## Getting Started\n\n### Documentation\n\nOur [documentation page](https:\u002F\u002Fisaac-sim.github.io\u002FIsaacLab) provides everything you need to get started, including\ndetailed tutorials and step-by-step guides. Follow these links to learn more about:\n\n- [Installation steps](https:\u002F\u002Fisaac-sim.github.io\u002FIsaacLab\u002Fmain\u002Fsource\u002Fsetup\u002Finstallation\u002Findex.html#local-installation)\n- [Reinforcement learning](https:\u002F\u002Fisaac-sim.github.io\u002FIsaacLab\u002Fmain\u002Fsource\u002Foverview\u002Freinforcement-learning\u002Frl_existing_scripts.html)\n- [Tutorials](https:\u002F\u002Fisaac-sim.github.io\u002FIsaacLab\u002Fmain\u002Fsource\u002Ftutorials\u002Findex.html)\n- [Available environments](https:\u002F\u002Fisaac-sim.github.io\u002FIsaacLab\u002Fmain\u002Fsource\u002Foverview\u002Fenvironments.html)\n\n\n## Isaac Sim Version Dependency\n\nIsaac Lab is built on top of Isaac Sim and requires specific versions of Isaac Sim that are compatible with each\nrelease of Isaac Lab. Below, we outline the recent Isaac Lab releases and GitHub branches and their corresponding\ndependency versions for Isaac Sim.\n\n| Isaac Lab Version             | Isaac Sim Version         |\n| ----------------------------- | ------------------------- |\n| `main` branch                 | Isaac Sim 4.5 \u002F 5.0 \u002F 5.1 |\n| `v2.3.X`                      | Isaac Sim 4.5 \u002F 5.0 \u002F 5.1 |\n| `v2.2.X`                      | Isaac Sim 4.5 \u002F 5.0       |\n| `v2.1.X`                      | Isaac Sim 4.5             |\n| `v2.0.X`                      | Isaac Sim 4.5             |\n\n\n## Contributing to Isaac Lab\n\nWe wholeheartedly welcome contributions from the community to make this framework mature and useful for everyone.\nThese may happen as bug reports, feature requests, or code contributions. For details, please check our\n[contribution guidelines](https:\u002F\u002Fisaac-sim.github.io\u002FIsaacLab\u002Fmain\u002Fsource\u002Frefs\u002Fcontributing.html).\n\n## Show & Tell: Share Your Inspiration\n\nWe encourage you to utilize our [Show & Tell](https:\u002F\u002Fgithub.com\u002Fisaac-sim\u002FIsaacLab\u002Fdiscussions\u002Fcategories\u002Fshow-and-tell)\narea in the `Discussions` section of this repository. This space is designed for you to:\n\n* Share the tutorials you've created\n* Showcase your learning content\n* Present exciting projects you've developed\n\nBy sharing your work, you'll inspire others and contribute to the collective knowledge\nof our community. Your contributions can spark new ideas and collaborations, fostering\ninnovation in robotics and simulation.\n\n## Troubleshooting\n\nPlease see the [troubleshooting](https:\u002F\u002Fisaac-sim.github.io\u002FIsaacLab\u002Fmain\u002Fsource\u002Frefs\u002Ftroubleshooting.html) section for\ncommon fixes or [submit an issue](https:\u002F\u002Fgithub.com\u002Fisaac-sim\u002FIsaacLab\u002Fissues).\n\nFor issues related to Isaac Sim, we recommend checking its [documentation](https:\u002F\u002Fdocs.isaacsim.omniverse.nvidia.com\u002Flatest\u002Findex.html)\nor opening a question on its [forums](https:\u002F\u002Fforums.developer.nvidia.com\u002Fc\u002Fagx-autonomous-machines\u002Fisaac\u002F67).\n\n## Support\n\n* Please use GitHub [Discussions](https:\u002F\u002Fgithub.com\u002Fisaac-sim\u002FIsaacLab\u002Fdiscussions) for discussing ideas,\n  asking questions, and requests for new features.\n* Github [Issues](https:\u002F\u002Fgithub.com\u002Fisaac-sim\u002FIsaacLab\u002Fissues) should only be used to track executable pieces of\n  work with a definite scope and a clear deliverable. These can be fixing bugs, documentation issues, new features,\n  or general updates.\n\n## Connect with the NVIDIA Omniverse Community\n\nDo you have a project or resource you'd like to share more widely? We'd love to hear from you!\nReach out to the NVIDIA Omniverse Community team at OmniverseCommunity@nvidia.com to explore opportunities\nto spotlight your work.\n\nYou can also join the conversation on the [Omniverse Discord](https:\u002F\u002Fdiscord.com\u002Finvite\u002Fnvidiaomniverse) to\nconnect with other developers, share your projects, and help grow a vibrant, collaborative ecosystem\nwhere creativity and technology intersect. Your contributions can make a meaningful impact on the Isaac Lab\ncommunity and beyond!\n\n## License\n\nThe Isaac Lab framework is released under [BSD-3 License](LICENSE). The `isaaclab_mimic` extension and its\ncorresponding standalone scripts are released under [Apache 2.0](LICENSE-mimic). The license files of its\ndependencies and assets are present in the [`docs\u002Flicenses`](docs\u002Flicenses) directory.\n\nNote that Isaac Lab requires Isaac Sim, which includes components under proprietary licensing terms. Please see the [Isaac Sim license](docs\u002Flicenses\u002Fdependencies\u002Fisaacsim-license.txt) for information on Isaac Sim licensing.\n\nNote that the `isaaclab_mimic` extension requires cuRobo, which has proprietary licensing terms that can be found in [`docs\u002Flicenses\u002Fdependencies\u002FcuRobo-license.txt`](docs\u002Flicenses\u002Fdependencies\u002FcuRobo-license.txt).\n\n\n## Citation\n\nIf you use Isaac Lab in your research, please cite the technical report:\n\n```\n@article{mittal2025isaaclab,\n  title={Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning},\n  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},\n  journal={arXiv preprint arXiv:2511.04831},\n  year={2025},\n  url={https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.04831}\n}\n```\n\n## Acknowledgement\n\nIsaac Lab development initiated from the [Orbit](https:\u002F\u002Fisaac-orbit.github.io\u002F) framework.\nWe gratefully acknowledge the authors of Orbit for their foundational contributions.\n","Isaac Lab 是一个基于 NVIDIA Isaac Sim 构建的统一框架，旨在简化机器人学习研究流程，包括强化学习、模仿学习和运动规划。该项目利用 GPU 加速技术提供快速且精确的物理与传感器仿真功能，非常适合从仿真到现实世界的机器人技术转移应用。其核心功能包括支持多种类型的机器人模型（如操作臂、四足机器人及人形机器人）和超过30种预设环境，适用于主流强化学习框架；同时，它还提供了丰富的传感器模拟选项，如RTX相机、LIDAR等，以及刚体、关节系统和可变形物体的物理模拟。Isaac Lab 适合于需要高效迭代训练过程的研究者和开发者使用，在本地或云端均可运行，为大规模部署提供了灵活性。",2,"2026-06-11 03:35:46","high_star"]