[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72517":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":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":32,"readmeContent":33,"aiSummary":34,"trendingCount":16,"starSnapshotCount":16,"syncStatus":35,"lastSyncTime":36,"discoverSource":37},72517,"mjlab","mujocolab\u002Fmjlab","mujocolab","Isaac Lab API, powered by MuJoCo-Warp, for RL and robotics research","https:\u002F\u002Fmujocolab.github.io\u002Fmjlab\u002F",null,"Python",2480,414,23,25,0,26,68,196,78,109.85,"Apache License 2.0",false,"main",true,[27,28,29,30,31],"isaaclab","mujoco","mujoco-warp","reinforcement-learning","robotics-simulation","2026-06-12 04:01:06","![Project banner](https:\u002F\u002Fraw.githubusercontent.com\u002Fmujocolab\u002Fmjlab\u002Fmain\u002Fdocs\u002Fsource\u002F_static\u002Fmjlab-banner.jpg)\n\n# mjlab\n\n[![GitHub Actions](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fmujocolab\u002Fmjlab\u002Fci.yml?branch=main)](https:\u002F\u002Fgithub.com\u002Fmujocolab\u002Fmjlab\u002Factions\u002Fworkflows\u002Fci.yml?query=branch%3Amain)\n[![Documentation](https:\u002F\u002Fgithub.com\u002Fmujocolab\u002Fmjlab\u002Factions\u002Fworkflows\u002Fdocs.yml\u002Fbadge.svg)](https:\u002F\u002Fmujocolab.github.io\u002Fmjlab\u002F)\n[![License](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fmujocolab\u002Fmjlab)](https:\u002F\u002Fgithub.com\u002Fmujocolab\u002Fmjlab\u002Fblob\u002Fmain\u002FLICENSE)\n[![Nightly Benchmarks](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNightly-Benchmarks-blue)](https:\u002F\u002Fmujocolab.github.io\u002Fmjlab\u002Fnightly\u002F)\n[![PyPI](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fmjlab)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fmjlab\u002F)\n[![PyPI downloads](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fmjlab?color=blue)](https:\u002F\u002Fpypistats.org\u002Fpackages\u002Fmjlab)\n\nmjlab combines [Isaac Lab](https:\u002F\u002Fgithub.com\u002Fisaac-sim\u002FIsaacLab)'s manager-based API with [MuJoCo Warp](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fmujoco_warp), a GPU-accelerated version of [MuJoCo](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fmujoco).\nThe framework provides composable building blocks for environment design,\nwith minimal dependencies and direct access to native MuJoCo data structures.\n\n## Getting Started\n\nmjlab requires an NVIDIA GPU for training. macOS is supported for evaluation only.\n\n**Try it now:**\n\nRun the demo (no installation needed):\n\n```bash\nuvx --from mjlab --refresh demo\n```\n\nOr try in [Google Colab](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fmujocolab\u002Fmjlab\u002Fblob\u002Fmain\u002Fnotebooks\u002Fdemo.ipynb) (no local setup required).\n\n**Install from source:**\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fmujocolab\u002Fmjlab.git && cd mjlab\nuv run demo\n```\n\nFor alternative installation methods (PyPI, Docker), see the [Installation Guide](https:\u002F\u002Fmujocolab.github.io\u002Fmjlab\u002Fmain\u002Fsource\u002Finstallation.html).\n\n## Training Examples\n\n### 1. Velocity Tracking\n\nTrain a Unitree G1 humanoid to follow velocity commands on flat terrain:\n\n```bash\nuv run train Mjlab-Velocity-Flat-Unitree-G1 --env.scene.num-envs 4096\n```\n\n**Multi-GPU Training:** Scale to multiple GPUs using `--gpu-ids`:\n\n```bash\nuv run train Mjlab-Velocity-Flat-Unitree-G1 \\\n  --gpu-ids \"[0, 1]\" \\\n  --env.scene.num-envs 4096\n```\n\nSee the [Distributed Training guide](https:\u002F\u002Fmujocolab.github.io\u002Fmjlab\u002Fmain\u002Fsource\u002Ftraining\u002Fdistributed_training.html) for details.\n\nEvaluate a policy while training (fetches latest checkpoint from Weights & Biases):\n\n```bash\nuv run play Mjlab-Velocity-Flat-Unitree-G1 --wandb-run-path your-org\u002Fmjlab\u002Frun-id\n```\n\n### 2. Motion Imitation\n\nTrain a humanoid to mimic reference motions. See the [motion imitation guide](https:\u002F\u002Fmujocolab.github.io\u002Fmjlab\u002Fmain\u002Fsource\u002Ftraining\u002Fmotion_imitation.html) for preprocessing setup.\n\n```bash\nuv run train Mjlab-Tracking-Flat-Unitree-G1 --registry-name your-org\u002Fmotions\u002Fmotion-name --env.scene.num-envs 4096\nuv run play Mjlab-Tracking-Flat-Unitree-G1 --wandb-run-path your-org\u002Fmjlab\u002Frun-id\n```\n\n### 3. Sanity-check with Dummy Agents\n\nUse built-in agents to sanity check your MDP before training:\n\n```bash\nuv run play Mjlab-Your-Task-Id --agent zero  # Sends zero actions\nuv run play Mjlab-Your-Task-Id --agent random  # Sends uniform random actions\n```\n\nWhen running motion-tracking tasks, add `--registry-name your-org\u002Fmotions\u002Fmotion-name` to the command.\n\n\n## Documentation\n\nFull documentation is available at **[mujocolab.github.io\u002Fmjlab](https:\u002F\u002Fmujocolab.github.io\u002Fmjlab\u002F)**.\n\n## Development\n\n```bash\nmake test          # Run all tests\nmake test-fast     # Skip slow tests\nmake format        # Format and lint\nmake docs          # Build docs locally\n```\n\nFor development setup: `uvx pre-commit install`\n\n## Citation\n\nmjlab is used in published research and open-source robotics projects. See the [Research](https:\u002F\u002Fmujocolab.github.io\u002Fmjlab\u002Fmain\u002Fsource\u002Fresearch.html) page for publications and projects, or share your own in [Show and Tell](https:\u002F\u002Fgithub.com\u002Fmujocolab\u002Fmjlab\u002Fdiscussions\u002Fcategories\u002Fshow-and-tell).\n\nIf you use mjlab in your research, please consider citing:\n\n```bibtex\n@misc{zakka2026mjlablightweightframeworkgpuaccelerated,\n  title={mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning},\n  author={Kevin Zakka and Qiayuan Liao and Brent Yi and Louis Le Lay and Koushil Sreenath and Pieter Abbeel},\n  year={2026},\n  eprint={2601.22074},\n  archivePrefix={arXiv},\n  primaryClass={cs.RO},\n  url={https:\u002F\u002Farxiv.org\u002Fabs\u002F2601.22074},\n}\n```\n\n## License\n\nmjlab is licensed under the [Apache License, Version 2.0](LICENSE).\n\n### Third-Party Code\n\nSome portions of mjlab are forked from external projects:\n\n- **`src\u002Fmjlab\u002Futils\u002Flab_api\u002F`** — Utilities forked from [NVIDIA Isaac\n  Lab](https:\u002F\u002Fgithub.com\u002Fisaac-sim\u002FIsaacLab) (BSD-3-Clause license, see file\n  headers)\n\nForked components retain their original licenses. See file headers for details.\n\n## Acknowledgments\n\nmjlab wouldn't exist without the excellent work of the Isaac Lab team, whose API\ndesign and abstractions mjlab builds upon.\n\nThanks to the MuJoCo Warp team — especially Erik Frey and Taylor Howell — for\nanswering our questions, giving helpful feedback, and implementing features\nbased on our requests countless times.\n","mjlab 是一个基于 MuJoCo-Warp 的 Isaac Lab API，专为强化学习和机器人研究设计。该项目通过结合 Isaac Lab 的管理器API与GPU加速的MuJoCo模拟器，提供了环境设计的可组合构建块，具有最小依赖性和直接访问原生MuJoCo数据结构的特点。它特别适合需要高性能物理仿真和强化学习训练的应用场景，如机器人动作控制、运动模仿等任务。项目支持NVIDIA GPU进行训练，并可在macOS上进行评估，同时提供了多种安装方式（包括PyPI和Docker）以方便用户快速上手。",2,"2026-06-11 03:42:24","high_star"]