[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70527":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":9,"totalLinesOfCode":9,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":9,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":9,"pushedAt":9,"updatedAt":27,"readmeContent":28,"aiSummary":29,"trendingCount":16,"starSnapshotCount":16,"syncStatus":30,"lastSyncTime":31,"discoverSource":32},70527,"mujoco","google-deepmind\u002Fmujoco","google-deepmind","Multi-Joint dynamics with Contact. A general purpose physics simulator.",null,"https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fmujoco","C++",13823,1574,130,161,0,50,133,379,150,116.59,false,"main",[25,26,5],"robotics","physics","2026-06-12 04:00:55","\u003Ch1>\n  \u003Ca href=\"#\">\u003Cimg alt=\"MuJoCo\" src=\"banner.png\" width=\"100%\"\u002F>\u003C\u002Fa>\n\u003C\u002Fh1>\n\n\u003Cp>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fmujoco\u002Factions\u002Fworkflows\u002Fbuild.yml?query=branch%3Amain\" alt=\"GitHub Actions\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fgoogle-deepmind\u002Fmujoco\u002Fbuild.yml?branch=main\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fmujoco.readthedocs.io\u002F\" alt=\"Documentation\">\n    \u003Cimg src=\"https:\u002F\u002Freadthedocs.org\u002Fprojects\u002Fmujoco\u002Fbadge\u002F?version=latest\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fmujoco\u002Fblob\u002Fmain\u002FLICENSE\" alt=\"License\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fgoogle-deepmind\u002Fmujoco\">\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\n**MuJoCo** stands for **Mu**lti-**Jo**int dynamics with **Co**ntact. It is a\ngeneral purpose physics engine that aims to facilitate research and development\nin robotics, biomechanics, graphics and animation, machine learning, and other\nareas which demand fast and accurate simulation of articulated structures\ninteracting with their environment.\n\nThis repository is maintained by [Google DeepMind](https:\u002F\u002Fwww.deepmind.com\u002F).\n\nMuJoCo has a C API and is intended for researchers and developers. The runtime\nsimulation module is tuned to maximize performance and operates on low-level\ndata structures that are preallocated by the built-in XML compiler. The library\nincludes interactive visualization with a native GUI, rendered in OpenGL. MuJoCo\nfurther exposes a large number of utility functions for computing\nphysics-related quantities.\n\nWe also provide [Python bindings] and a plug-in for the [Unity] game engine.\n\n## Documentation\n\nMuJoCo's documentation can be found at [mujoco.readthedocs.io]. Upcoming\nfeatures due for the next release can be found in the [changelog] in the\n\"latest\" branch.\n\n## Getting Started\n\nThere are two easy ways to get started with MuJoCo:\n\n1. **Run `simulate` on your machine.**\n[This video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=P83tKA1iz2Y) shows a screen capture\nof `simulate`, MuJoCo's native interactive viewer. Follow the steps described in\nthe [Getting Started] section of the documentation to get `simulate` running on\nyour machine.\n\n2. **Explore our online IPython notebooks.**\nIf you are a Python user, you might want to start with our tutorial notebooks\nrunning on Google Colab:\n\n - The **introductory** tutorial teaches MuJoCo basics:\n   [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fgoogle-deepmind\u002Fmujoco\u002Fblob\u002Fmain\u002Fpython\u002Ftutorial.ipynb)\n - The **Model Editing** tutorial shows how to create and edit models procedurally:\n   [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fgoogle-deepmind\u002Fmujoco\u002Fblob\u002Fmain\u002Fpython\u002Fmjspec.ipynb)\n - The **rollout** tutorial shows how to use the multithreaded `rollout` module:\n   [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fgoogle-deepmind\u002Fmujoco\u002Fblob\u002Fmain\u002Fpython\u002Frollout.ipynb)\n - The **LQR** tutorial synthesizes a linear-quadratic controller, balancing a\n   humanoid on one leg:\n   [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fgoogle-deepmind\u002Fmujoco\u002Fblob\u002Fmain\u002Fpython\u002FLQR.ipynb)\n - The **least-squares** tutorial explains how to use the Python-based nonlinear\n   least-squares solver:\n   [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fgoogle-deepmind\u002Fmujoco\u002Fblob\u002Fmain\u002Fpython\u002Fleast_squares.ipynb)\n - The **MJX** tutorial provides usage examples of\n   [MuJoCo XLA](https:\u002F\u002Fmujoco.readthedocs.io\u002Fen\u002Fstable\u002Fmjx.html), a branch of MuJoCo written in JAX:\n   [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fgoogle-deepmind\u002Fmujoco\u002Fblob\u002Fmain\u002Fmjx\u002Ftutorial.ipynb)\n - The **differentiable physics** tutorial trains locomotion policies with\n   analytical gradients automatically derived from MuJoCo's physics step:\n   [![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fgoogle-deepmind\u002Fmujoco\u002Fblob\u002Fmain\u002Fmjx\u002Ftraining_apg.ipynb)\n\n## Installation\n\n### Prebuilt binaries\n\nVersioned releases are available as precompiled binaries from the GitHub\n[releases page], built for Linux (x86-64 and AArch64), Windows (x86-64 only),\nand macOS (universal). This is the recommended way to use the software.\n\n### Building from source\n\nUsers who wish to build MuJoCo from source should consult the [build from\nsource] section of the documentation. However, note that the commit at\nthe tip of the `main` branch may be unstable.\n\n### Python (>= 3.10)\n\nThe native Python bindings, which come pre-packaged with a copy of MuJoCo, can\nbe installed from [PyPI] via:\n\n```bash\npip install mujoco\n```\n\nNote that Pre-built Linux wheels target `manylinux2014`, see\n[here](https:\u002F\u002Fgithub.com\u002Fpypa\u002Fmanylinux) for compatible distributions. For more\ninformation such as building the bindings from source, see the [Python bindings]\nsection of the documentation.\n\n## Versioning\n\nWe aim to release MuJoCo in the first week of each month. Our versioning\nstandards changed to modified Semantic Versioning in 3.5.0,\nsee [versioning](VERSIONING.md) for details.\n\n## Contributing\n\nWe welcome community engagement: questions, requests for help, bug reports and\nfeature requests. To read more about bug reports, feature requests and more\nambitious contributions, please see our [contributors guide](CONTRIBUTING.md)\nand [style guide](STYLEGUIDE.md).\n\n## Asking Questions\n\nQuestions and requests for help are welcome as a GitHub\n[\"Asking for Help\" Discussion](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fmujoco\u002Fdiscussions\u002Fcategories\u002Fasking-for-help)\nand should focus on a specific problem or question.\n\n## Bug reports and feature requests\n\nGitHub [Issues](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fmujoco\u002Fissues) are reserved\nfor bug reports, feature requests and other development-related subjects.\n\n## Related software\nMuJoCo is the backbone for numerous environment packages. Below we list several\nbindings and converters.\n\n### Bindings\n\nThese packages give users of various languages access to MuJoCo functionality:\n\n#### First-party bindings:\n\n- [Python bindings](https:\u002F\u002Fmujoco.readthedocs.io\u002Fen\u002Fstable\u002Fpython.html)\n  - [dm_control](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fdm_control), Google\n    DeepMind's related environment stack, includes\n    [PyMJCF](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fdm_control\u002Fblob\u002Fmain\u002Fdm_control\u002Fmjcf\u002FREADME.md),\n    a module for procedural manipulation of MuJoCo models.\n- [JavaScript bindings and WebAssembly support](\u002Fwasm\u002FREADME.md) (inspired [stillonearth](https:\u002F\u002Fgithub.com\u002Fstillonearth) and [zalo](https:\u002F\u002Fgithub.com\u002Fzalo)'s community projects; [mjswan](https:\u002F\u002Fgithub.com\u002Fttktjmt\u002Fmjswan) extends these with real-time policy control, interactive force\napplication, and more).\n- [C# bindings and Unity plug-in](https:\u002F\u002Fmujoco.readthedocs.io\u002Fen\u002Fstable\u002Funity.html)\n\n#### Third-party bindings:\n\n- **MATLAB Simulink**: [Simulink Blockset for MuJoCo Simulator](https:\u002F\u002Fgithub.com\u002Fmathworks-robotics\u002Fmujoco-simulink-blockset)\n  by [Manoj Velmurugan](https:\u002F\u002Fgithub.com\u002Fvmanoj1996).\n- **Swift**: [swift-mujoco](https:\u002F\u002Fgithub.com\u002Fliuliu\u002Fswift-mujoco)\n- **Java**: [mujoco-java](https:\u002F\u002Fgithub.com\u002FCommonWealthRobotics\u002Fmujoco-java)\n- **Julia**: [MuJoCo.jl](https:\u002F\u002Fgithub.com\u002FJamieMair\u002FMuJoCo.jl)\n- **Rust**: [MuJoCo-rs](https:\u002F\u002Fgithub.com\u002Fdavidhozic\u002Fmujoco-rs)\n\n### Converters\n\n- **OpenSim**: [MyoConverter](https:\u002F\u002Fgithub.com\u002FMyoHub\u002Fmyoconverter) converts\n  OpenSim models to MJCF.\n- **SDFormat**: [gz-mujoco](https:\u002F\u002Fgithub.com\u002Fgazebosim\u002Fgz-mujoco\u002F) is a\n  two-way SDFormat \u003C-> MJCF conversion tool.\n- **OBJ**: [obj2mjcf](https:\u002F\u002Fgithub.com\u002Fkevinzakka\u002Fobj2mjcf)\n  a script for converting composite OBJ files into a loadable MJCF model.\n- **onshape**: [Onshape to Robot](https:\u002F\u002Fgithub.com\u002Frhoban\u002Fonshape-to-robot)\n  Converts [onshape](https:\u002F\u002Fwww.onshape.com\u002Fen\u002F) CAD assemblies to MJCF.\n\n## Citation\n\nIf you use MuJoCo for published research, please cite:\n\n```\n@inproceedings{todorov2012mujoco,\n  title={MuJoCo: A physics engine for model-based control},\n  author={Todorov, Emanuel and Erez, Tom and Tassa, Yuval},\n  booktitle={2012 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems},\n  pages={5026--5033},\n  year={2012},\n  organization={IEEE},\n  doi={10.1109\u002FIROS.2012.6386109}\n}\n```\n\n## License and Disclaimer\n\nCopyright 2021 DeepMind Technologies Limited.\n\nBox collision code ([`engine_collision_box.c`](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fmujoco\u002Fblob\u002Fmain\u002Fsrc\u002Fengine\u002Fengine_collision_box.c))\nis Copyright 2016 Svetoslav Kolev.\n\nReStructuredText documents, images, and videos in the `doc` directory are made\navailable under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0)\nlicense. You may obtain a copy of the License at\nhttps:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002Flegalcode.\n\nSource code is licensed under the Apache License, Version 2.0. You may obtain a\ncopy of the License at https:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0.\n\nThis is not an officially supported Google product.\n\n[build from source]: https:\u002F\u002Fmujoco.readthedocs.io\u002Fen\u002Flatest\u002Fprogramming#building-from-source\n[Getting Started]: https:\u002F\u002Fmujoco.readthedocs.io\u002Fen\u002Flatest\u002Fprogramming#getting-started\n[Unity]: https:\u002F\u002Funity.com\u002F\n[releases page]: https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fmujoco\u002Freleases\n[mujoco.readthedocs.io]: https:\u002F\u002Fmujoco.readthedocs.io\n[changelog]: https:\u002F\u002Fmujoco.readthedocs.io\u002Fen\u002Flatest\u002Fchangelog.html\n[Python bindings]: https:\u002F\u002Fmujoco.readthedocs.io\u002Fen\u002Fstable\u002Fpython.html#python-bindings\n[PyPI]: https:\u002F\u002Fpypi.org\u002Fproject\u002Fmujoco\u002F\n","MuJoCo是一个通用的物理模拟引擎，专为机器人学、生物力学、图形与动画以及机器学习等领域中需要快速准确地模拟关节结构与其环境交互的研究和开发而设计。其核心功能包括高效的多关节动力学及接触处理能力，并通过C API提供给研究人员和开发者使用，同时支持Python绑定和Unity插件以扩展应用范围。该引擎经过优化，能够高效运行于预分配的数据结构之上，并且内置了基于OpenGL的可视化工具，方便用户进行交互式探索。适用于任何需要对复杂机械系统或生物体运动进行精确仿真分析的场景，如算法测试、虚拟实验设计等。",2,"2026-06-11 03:32:39","trending"]