MuJoCo's documentation can be found at https://mujoco.readthedocs.io[mujoco.readthedocs.io]. Upcoming features due for the next release can be found in the https://mujoco.readthedocs.io/en/latest/changelog.html[changelog] in the "latest" branch.

There are two easy ways to get started with MuJoCo: . *Run `simulate` on your machine.* https://www.youtube.com/watch?v=P83tKA1iz2Y[This video] shows a screen capture of `simulate`, MuJoCo's native interactive viewer. Follow the steps described in the https://mujoco.readthedocs.io/en/latest/programming#getting-started[Getting Started] section of the documentation to get `simulate` running on your machine. . *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: image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb] * The *Model Editing* tutorial shows how to create and edit models procedurally: image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/mjspec.ipynb] * The *rollout* tutorial shows how to use the multithreaded `rollout` module: image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/rollout.ipynb] * The *LQR* tutorial synthesizes a linear-quadratic controller, balancing a humanoid on one leg: image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/LQR.ipynb] * The *least-squares* tutorial explains how to use the Python-based nonlinear least-squares solver: image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/least_squares.ipynb] * The *MJX* tutorial provides usage examples of https://mujoco.readthedocs.io/en/stable/mjx.html[MuJoCo XLA], a branch of MuJoCo written in JAX: image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/tutorial.ipynb] * The *differentiable physics* tutorial trains locomotion policies with analytical gradients automatically derived from MuJoCo's physics step: image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/training_apg.ipynb]

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 xref:CONTRIBUTING.adoc[contributors guide] and xref:STYLEGUIDE.adoc[style guide].

Questions and requests for help are welcome as a GitHub https://github.com/google-deepmind/mujoco/discussions/categories/asking-for-help["Asking for Help" Discussion] and should focus on a specific problem or question.

GitHub https://github.com/google-deepmind/mujoco/issues[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.

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 (https://github.com/google-deepmind/mujoco/blob/main/src/engine/engine_collision_box.c[`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.