MuJoCo – 高级物理模拟
MuJoCo – Advanced Physics Simulation

原始链接: https://github.com/google-deepmind/mujoco

MuJoCo 是谷歌 DeepMind 开发的高性能物理引擎,专为机器人学、生物力学和机器学习等领域的研究和开发而设计。它擅长模拟铰接结构及其与环境的交互,提供速度和准确性。 主要特性包括 C API、通过 OpenGL 进行的交互式可视化以及广泛的实用函数。还提供 Python 绑定和 Unity 插件。针对 Linux、Windows 和 macOS 提供预编译二进制文件,也可以进行源代码构建(但可能不稳定)。 MuJoCo 自 3.5.0 版本以来,每月发布一次,遵循修改后的语义化版本控制。该项目欢迎社区通过 GitHub Discussions 寻求帮助,通过 Issues 提交错误报告/功能请求。它通过 OpenSim、SDFormat 和 OBJ 等格式的转换器与其他工具集成。 如果在研究中使用,建议引用原始的 2012 年论文。该软件采用 Apache 2.0 许可协议开源,特定组件采用知识共享署名 4.0 协议。

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原文

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:

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, 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:

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 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.

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.

Bug reports and feature requests

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:

  • 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.

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.

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