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 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 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:
  
• The Model Editing tutorial shows how to create and edit models procedurally:
  
• The rollout tutorial shows how to use the multithreaded rollout module:
  
• The LQR tutorial synthesizes a linear-quadratic controller, balancing a
  humanoid on one leg:
  
• The least-squares tutorial explains how to use the Python-based nonlinear
  least-squares solver:
  
• The MJX tutorial provides usage examples of
  MuJoCo XLA, a branch of MuJoCo written in JAX:
  
• The differentiable physics tutorial trains locomotion policies with
  analytical gradients automatically derived from MuJoCo's physics step:
  

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, 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:

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.

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.
• C# bindings and Unity plug-in

Third-party bindings:

• WebAssembly: mujoco_wasm by @zalo with contributions by
  @kevinzakka, based on the emscripten build by
  @stillonearth.

  ➡️ Click here for a live demo of MuJoCo running in your browser.

• MATLAB Simulink: Simulink Blockset for MuJoCo Simulator
  by Manoj Velmurugan.

• Swift: swift-mujoco

• Java: mujoco-java

• Julia: MuJoCo.jl

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.