mujoco

MuJoCo stands for Multi-Joint dynamics with Contact. 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.

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 in native desktop windows and web browsers. 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

Try MuJoCo in your browser.

Visit mujoco.org to explore MuJoCo on desktop or mobile with nothing to install, or open live.mujoco.org to inspect and drag and drop your own models in the WebAssembly version of the [MuJoCo Studio] app (slower than native C, since it runs in WebAssembly).

Run a viewer on your machine.

Follow the [Getting Started] guide in the documentation to download a prebuilt release and run the [MuJoCo Studio] app or the classic simulate viewer (shown in this video) on your machine.

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:

[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb)

The Model Editing tutorial shows how to create and edit models procedurally:

[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/mjspec.ipynb)

The rollout tutorial shows how to use the multithreaded rollout module:

[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/rollout.ipynb)

The LQR tutorial synthesizes a linear-quadratic controller, balancing a

humanoid on one leg: [](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: [](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/least_squares.ipynb)

The MJX tutorial provides usage examples of

MuJoCo XLA, a branch of MuJoCo written in JAX: [](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: [](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/training_apg.ipynb)

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, note that the commit at the tip of the main branch may be unstable.

Python (>= 3.11)

The native Python bindings, which come pre-packaged with a copy of MuJoCo, can be installed from [PyPI] via:

pip install mujoco

Note that Pre-built Linux wheels target manylinux2014, see here for compatible distributions. For more information such as building the bindings from source, see the [Python bindings] section of the documentation.

Versioning

We aim to release MuJoCo in the first week of each month. Our versioning standards changed to modified Semantic Versioning in 3.5.0, see versioning for details.

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 and style guide.

Asking Questions

Questions and requests for help are welcome as a GitHub "Asking for Help" Discussion and should focus on a specific problem or question.

Bug reports and feature requests

GitHub 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

dm_control, Google

DeepMind's related environment stack, includes PyMJCF, a module for procedural manipulation of MuJoCo models.

JavaScript bindings and WebAssembly support (inspired stillonearth and zalo's community projects; mjswan extends these with real-time policy control, interactive force

application, and more).

C# bindings and Unity plug-in

Third-party bindings:

MATLAB Simulink: Simulink Blockset for MuJoCo Simulator

by Manoj Velmurugan.

Swift: swift-mujoco

Java: mujoco-java

Julia: MuJoCo.jl

Rust: MuJoCo-rs

Converters

OpenSim: MyoConverter converts

OpenSim models to MJCF.

SDFormat: gz-mujoco is a

two-way SDFormat <-> MJCF conversion tool.

OBJ: obj2mjcf

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

onshape: Onshape to Robot

Converts onshape CAD assemblies to MJCF.

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) 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.