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.

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.

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 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:

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.

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.

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.

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.

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

GitHub Issues are reserved for bug reports, feature requests and other development-related subjects.

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

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

• 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

• 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

• 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.

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}
}

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.