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https://github.com/google-deepmind/mujoco

Multi-Joint dynamics with Contact. A general purpose physics simulator.
https://github.com/google-deepmind/mujoco

mujoco physics robotics

Last synced: 16 days ago
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Multi-Joint dynamics with Contact. A general purpose physics simulator.

Host: GitHub
URL: https://github.com/google-deepmind/mujoco
Owner: google-deepmind
License: apache-2.0
Created: 2021-08-27T12:25:48.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-05-22T17:45:40.000Z (6 months ago)
Last Synced: 2024-05-22T18:46:41.192Z (6 months ago)
Topics: mujoco, physics, robotics
Language: Jupyter Notebook
Homepage: https://mujoco.org
Size: 93.5 MB
Stars: 7,310
Watchers: 101
Forks: 722
Open Issues: 190
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md

Awesome Lists containing this project

awesome-legged-locomotion - MuJoCo - Joint dynamics with Contact. A general purpose physics simulator. (Simulation Tools)
awesome-multi-modal-reinforcement-learning - Mujoco

README

        


  





  

    

  

  

    

  

  

    

  



**MuJoCo** stands for **Mu**lti-**Jo**int dynamics with **Co**ntact. It is a

general purpose physics engine that aims to facilitate research and development

in robotics, biomechanics, graphics and animation, machine learning, and other

areas which demand fast and accurate simulation of articulated structures

interacting with their environment.

This repository is maintained by [Google DeepMind](https://www.deepmind.com/).

MuJoCo has a C API and is intended for researchers and developers. The runtime

simulation module is tuned to maximize performance and operates on low-level

data structures that are preallocated by the built-in XML compiler. The library

includes interactive visualization with a native GUI, rendered in OpenGL. MuJoCo

further exposes a large number of utility functions for computing

physics-related quantities.

We also provide [Python bindings] and a plug-in for the [Unity] game engine.

## Documentation

MuJoCo's documentation can be found at [mujoco.readthedocs.io]. Upcoming features due for the next

release can be found in the [changelog] in the latest branch.

## Getting Started

There are two easy ways to get started with MuJoCo:

1. **Run `simulate` on your machine.**

[This video](https://www.youtube.com/watch?v=P83tKA1iz2Y) shows a screen capture

of `simulate`, MuJoCo's native interactive viewer. Follow the steps described in

the [Getting Started] section of the documentation to get `simulate` running on

your machine.

2. **Explore our online IPython notebooks.**

If you are a Python user, you might want to start with our tutorial notebooks

running on Google Colab:

 - The **introductory** tutorial teaches MuJoCo basics:

   [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb)

 - The **LQR** tutorial synthesizes a linear-quadratic controller, balancing a humanoid on one leg:

   [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/LQR.ipynb)

 - The **least-squares** tutorial explains how to use the Python-based nonlinear least-squares solver:

   [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/least_squares.ipynb)

 - The **MJX** tutorial provides usage examples of

   [MuJoCo XLA](https://mujoco.readthedocs.io/en/stable/mjx.html), a branch of MuJoCo written in JAX:

   [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/tutorial.ipynb)

 - The **differentiable physics** tutorial trains locomotion policies with analytical gradients automatically derived from MuJoCo's physics step:

   [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/training_apg.ipynb)

## Installation

### Prebuilt binaries

Versioned releases are available as precompiled binaries from the GitHub

[releases page], built for Linux (x86-64 and AArch64), Windows (x86-64 only),

and macOS (universal). This is the recommended way to use the software.

### Building from source

Users who wish to build MuJoCo from source should consult the [build from

source] section of the documentation. However, please note that the commit at

the tip of the `main` branch may be unstable.

### Python (>= 3.9)

The native Python bindings, which come pre-packaged with a copy of MuJoCo, can

be installed from [PyPI] via:

```bash

pip install mujoco

```

Note that Pre-built Linux wheels target `manylinux2014`, see

[here](https://github.com/pypa/manylinux) for compatible distributions. For more

information such as building the bindings from source, see the [Python bindings]

section of the documentation.

## Contributing

We welcome community engagement: questions, requests for help, bug reports and

feature requests. To read more about bug reports, feature requests and more

ambitious contributions, please see our [contributors guide](CONTRIBUTING.md)

and [style guide](STYLEGUIDE.md).

## Asking Questions

Questions and requests for help are welcome as a GitHub

["Asking for Help" Discussion](https://github.com/google-deepmind/mujoco/discussions/categories/asking-for-help)

and should focus on a specific problem or question.

## Bug reports and feature requests

GitHub [Issues](https://github.com/google-deepmind/mujoco/issues) are reserved

for bug reports, feature requests and other development-related subjects.

## Related software

MuJoCo is the backbone for numerous environment packages. Below we list several bindings and converters.

### Bindings

These packages give users of various languages access to MuJoCo functionality:

#### First-party bindings:

- [Python bindings](https://mujoco.readthedocs.io/en/stable/python.html)

  - [dm_control](https://github.com/google-deepmind/dm_control), Google

    DeepMind's related environment stack, includes

    [PyMJCF](https://github.com/google-deepmind/dm_control/blob/main/dm_control/mjcf/README.md),

    a module for procedural manipulation of MuJoCo models.

- [C# bindings and Unity plug-in](https://mujoco.readthedocs.io/en/stable/unity.html)

#### Third-party bindings:

- **WebAssembly**: [mujoco_wasm](https://github.com/zalo/mujoco_wasm) by [@zalo](https://github.com/zalo) with contributions by

  [@kevinzakka](https://github.com/kevinzakka), based on the [emscripten build](https://github.com/stillonearth/MuJoCo-WASM) by

  [@stillonearth](https://github.com/stillonearth).

  :arrow_right: [Click here](https://zalo.github.io/mujoco_wasm/) for a live demo of MuJoCo running in your browser.

- **MATLAB Simulink**: [Simulink Blockset for MuJoCo Simulator](https://github.com/mathworks-robotics/mujoco-simulink-blockset)

  by [Manoj Velmurugan](https://github.com/vmanoj1996).

- **Swift**: [swift-mujoco](https://github.com/liuliu/swift-mujoco)

- **Java**: [mujoco-java](https://github.com/CommonWealthRobotics/mujoco-java)

- **Julia**: [MuJoCo.jl](https://github.com/JamieMair/MuJoCo.jl)

### Converters

- **OpenSim**: [MyoConverter](https://github.com/MyoHub/myoconverter) converts

  OpenSim models to MJCF.

- **SDFormat**: [gz-mujoco](https://github.com/gazebosim/gz-mujoco/) is a

  two-way SDFormat <-> MJCF conversion tool.

- **OBJ**: [obj2mjcf](https://github.com/kevinzakka/obj2mjcf)

  a script for converting composite OBJ files into a loadable MJCF model.

## Citation

If you use MuJoCo for published research, please cite:

```

@inproceedings{todorov2012mujoco,

  title={MuJoCo: A physics engine for model-based control},

  author={Todorov, Emanuel and Erez, Tom and Tassa, Yuval},

  booktitle={2012 IEEE/RSJ International Conference on Intelligent Robots and Systems},

  pages={5026--5033},

  year={2012},

  organization={IEEE},

  doi={10.1109/IROS.2012.6386109}

}

```

## License and Disclaimer

Copyright 2021 DeepMind Technologies Limited.

Box collision code ([`engine_collision_box.c`](https://github.com/google-deepmind/mujoco/blob/main/src/engine/engine_collision_box.c))

is Copyright 2016 Svetoslav Kolev.

ReStructuredText documents, images, and videos in the `doc` directory are made

available under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0)

license. You may obtain a copy of the License at

https://creativecommons.org/licenses/by/4.0/legalcode.

Source code is licensed under the Apache License, Version 2.0. You may obtain a

copy of the License at https://www.apache.org/licenses/LICENSE-2.0.

This is not an officially supported Google product.

[build from source]: https://mujoco.readthedocs.io/en/latest/programming#building-mujoco-from-source

[Getting Started]: https://mujoco.readthedocs.io/en/latest/programming#getting-started

[Unity]: https://unity.com/

[releases page]: https://github.com/google-deepmind/mujoco/releases

[GitHub Issues]: https://github.com/google-deepmind/mujoco/issues

[mujoco.readthedocs.io]: https://mujoco.readthedocs.io

[changelog]: https://mujoco.readthedocs.io/en/latest/changelog.html

[Python bindings]: https://mujoco.readthedocs.io/en/stable/python.html#python-bindings

[PyPI]: https://pypi.org/project/mujoco/