Saran Tunyasuvunakool
107 lines
4.0 KiB
Markdown
107 lines
4.0 KiB
Markdown
# MuJoCo Physics
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**MuJoCo** stands for **Mu**lti-**Jo**int dynamics with **Co**ntact. It is a
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general purpose physics engine that aims to facilitate research and development
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in robotics, biomechanics, graphics and animation, machine learning, and other
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areas which demand fast and accurate simulation of articulated structures
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interacting with their environment.
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DeepMind has acquired MuJoCo, and we are currently making preparations to open
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source the codebase. In the meantime, MuJoCo is available for download as a free
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and unrestricted precompiled binary under the Apache 2.0 license from
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[mujoco.org](https://mujoco.org/).
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MuJoCo's source code will be released through this GitHub repository once it is
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ready. In the meantime, the repository hosts MuJoCo's documentation, C header
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files for its public API, and sample program code. If you wish to report bugs or
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make feature requests, please file them as [GitHub Issues]. You are also
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welcome to make pull requests for the [documentation source files].
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## Overview
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MuJoCo is a C/C++ library with a C API, intended for researchers and developers.
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The runtime simulation module is tuned to maximize performance and operates on
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low-level data structures which are preallocated by the built-in XML parser and
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compiler. The user defines models in the native MJCF scene description language
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-- an XML file format designed to be as human readable and editable as possible.
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URDF model files can also be loaded. The library includes interactive
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visualization with a native GUI, rendered in OpenGL. MuJoCo further exposes a
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large number of utility functions for computing physics-related quantities, not
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necessarily in a simulation loop. Features include
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- Simulation in generalized coordinates, avoiding joint violations.
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- Inverse dynamics that are well-defined even in the presence of contacts.
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- Unified continuous-time formulation of constraints via convex optimization.
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- Constraints include soft contacts, limits, dry friction, equality
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constraints.
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- Simulation of particle systems, cloth, rope and soft objects.
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- Actuators including motors, cylinders, muscles, tendons, slider-cranks.
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- Choice of Newton, Conjugate Gradient, or Projected Gauss-Seidel solvers.
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- Choice of pyramidal or elliptic friction cones, dense or sparse Jacobians.
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- Choice of Euler or Runge-Kutta numerical integrators.
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- Multi-threaded sampling and finite-difference approximations.
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- Intuitive XML model format (called MJCF) and built-in model compiler.
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- Cross-platform GUI with interactive 3D visualization in OpenGL.
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- Run-time module written in ANSI C and hand-tuned for performance.
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## Requirements
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MuJoCo binaries are currently built for Linux, macOS (Intel), and Windows.
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## Documentation
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MuJoco's current documentation is available at [mujoco.org/book], which is
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serving Sphinx-based webpages derived from the ReStructuredText
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[documentation source files].
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## Citation
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If you use MuJoCo for published research, please cite:
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```
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@inproceedings{todorov2012mujoco,
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title={Mujoco: A physics engine for model-based control},
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author={Todorov, Emanuel and Erez, Tom and Tassa, Yuval},
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booktitle={2012 IEEE/RSJ International Conference on Intelligent Robots and Systems},
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pages={5026--5033},
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year={2012},
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organization={IEEE}
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}
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```
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## License and Disclaimer
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Copyright 2021 DeepMind Technologies Limited
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ReStructuredText documents, images, and videos in the `doc` directory are made
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available under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0)
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license. You may obtain a copy of the License at
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https://creativecommons.org/licenses/by/4.0/legalcode.
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Source code is licensed under the Apache License, Version 2.0. You may obtain a
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copy of the License at https://www.apache.org/licenses/LICENSE-2.0.
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This is not an officially supported Google product.
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[GitHub Issues]: https://github.com/deepmind/mujoco/issues
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[documentation source files]: https://github.com/deepmind/mujoco/tree/main/doc
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[mujoco.org/book]: https://mujoco.org/book
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