mujoco - Comprehensive Physics Engine for Robotics, Biomechanics, and Machine Learning

Introduction to MuJoCo

What is MuJoCo?

MuJoCo, short for Multi-Joint dynamics with Contact, is a versatile physics engine designed to support research and development across several domains, including robotics, biomechanics, graphics and animation, and machine learning. The primary goal of MuJoCo is to offer quick and precise simulation of articulated structures as they interact with their surroundings. It is maintained by Google DeepMind.

Key Features

High Performance: MuJoCo’s runtime simulation module is optimized to ensure maximum performance, operating efficiently on low-level data structures.
C API: Designed with researchers and developers in mind, MuJoCo provides a C application programming interface (API) for seamless integration and usage.
Interactive Visualization: It includes an interactive visualization component using a native GUI rendered with OpenGL, enhancing the user experience.
Utility Functions: The engine offers numerous utility functions for various physics-related computations.
Python Bindings and Unity Plug-in: Additionally, MuJoCo supports Python bindings and offers a plug-in for the Unity game engine, broadening its usability across different platforms and programming environments.

How to Get Started

There are two straightforward ways to dive into using MuJoCo:

Run simulate on Your Machine: Simulate is MuJoCo’s native interactive viewer. Users can follow instructions in the Getting Started section of MuJoCo’s documentation to set it up. A video tutorial is also available to guide newcomers.
Utilize Online IPython Notebooks: For Python enthusiasts, several tutorial notebooks are available on Google Colab. These tutorials cover the basics of MuJoCo, the synthesis of linear-quadratic controllers, solving nonlinear least squares problems, and training locomotion policies using analytical gradients.

Installation

Prebuilt Binaries

Precompiled binaries are accessible via the GitHub releases page for various platforms including Linux (x86-64 and AArch64), Windows (x86-64), and macOS (universal). This is the recommended method for installation.

Building from Source

For those interested in compiling MuJoCo from source, detailed instructions are available in the documentation. However, caution is advised as the main branch might be unstable.

Python Installation

MuJoCo's native Python bindings (supporting versions >= 3.9) can be easily installed using pip:

pip install mujoco

Detailed instructions for building the Python bindings from source are available in the documentation.

Community and Contributions

MuJoCo thrives on community involvement. Users are encouraged to engage through questions, help requests, bug reports, and feature suggestions. Contributions can be made by following the guidelines outlined in the project's contributors and style guides.

Additional Resources

Bindings and Converters: MuJoCo supports a range of bindings for different programming languages and tools like WebAssembly, MATLAB Simulink, Swift, Java, and Julia. It also includes converters for models from OpenSim, SDFormat, and OBJ formats.
Documentation: Comprehensive documentation is accessible online, providing detailed insight into MuJoCo’s capabilities and future updates.

Citation

Researchers can cite MuJoCo in their work using the provided citation format from the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Licensing

MuJoCo is licensed under the Apache License, Version 2.0, with some components available under the Creative Commons Attribution 4.0 license.

MuJoCo provides a robust framework for those in academia and industry looking for accurate and efficient simulation solutions, making it a pivotal tool in modern research and development.