mujoco

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Multi-Joint dynamics with Contact. A general purpose physics simulator.
liuliu/mujoco

MuJoCo Physics

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 DeepMind, please see our acquisition and open sourcing announcements.

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 is available at mujoco.readthedocs.io, which serves webpages derived from the documentation source files.

Releases

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, please 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.

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 first tutorial focuses on the basic MuJoCo Python bindings: Open In Colab

  • The second tutorial includes more examples of dm_control-specific functionality: Open In Colab

Asking Questions

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.

Questions and requests for help are welcome on the GitHub issues and discussions pages. Issues should be focused on a specific problem or question, while discussions should address wider concerns that might require input from multiple participants.

Here are some guidelines for asking good questions:

  1. Search for existing questions or issues that touch on the same subject.

    You can add comments to existing threads or start new ones. If you start a new thread and there are existing relevant threads, please link to them.

  2. Use a clear and descriptive title.

  3. Introduce yourself and your project more generally.

    If your level of expertise is exceptional (either high or low), and it might be relevant to what we can assume you know, please state that as well.

  4. Make it easy for others to reproduce the problem or understand your question.

    If this requires a model, please include it. Short, minimal, pure XML models (the preferred format) should be pasted inline. Longer XML models should be attached as a .txt file (GitHub does not accept .xml) or in a .zip. Models requiring binary assets (meshes, textures), should be attached as .zip files. Please remember to make sure the included model is loadable before you attach it.

  5. Include an illustrative screenshot or video, if relevant.

  6. Tell us which MuJoCo version and operating system you are using.

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.

Description

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Dependencies

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Last updated: Tue Aug 09 2022 14:49:47 GMT-0500 (GMT-05:00)