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https://github.com/openai/mujoco-py

MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.
https://github.com/openai/mujoco-py

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MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.

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README 

        
**Status**: Deprecated



### `mujoco-py` does not support versions of MuJoCo after 2.1.0.



### New users should depend on the [official MuJoCo Python bindings](https://github.com/deepmind/mujoco/blob/main/python/README.md).



# mujoco-py [![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://openai.github.io/mujoco-py/build/html/index.html) [![Build Status](https://travis-ci.org/openai/mujoco-py.svg?branch=master)](https://travis-ci.org/openai/mujoco-py)



[MuJoCo](http://mujoco.org/) is a physics engine for detailed, efficient rigid body simulations with contacts.

`mujoco-py` allows using MuJoCo from Python 3.



This library has been updated to be compatible with MuJoCo version 2.1 released on 2021-10-18.



## Synopsis



### Requirements



The following platforms are currently supported:



- Linux with Python 3.6+. See [the `Dockerfile`](Dockerfile) for the canonical list of system dependencies.

- OS X with Python 3.6+.



The following platforms are DEPRECATED and unsupported:



- Windows support has been DEPRECATED and removed in [2.0.2.0](https://github.com/openai/mujoco-py/releases/tag/v2.0.2.0a1). One known good past version is [1.50.1.68](https://github.com/openai/mujoco-py/blob/9ea9bb000d6b8551b99f9aa440862e0c7f7b4191/README.md#requirements).

- Python 2 has been DEPRECATED and removed in [1.50.1.0](https://github.com/openai/mujoco-py/releases/tag/1.50.1.0). Python 2 users can stay on the [`0.5` branch](https://github.com/openai/mujoco-py/tree/0.5). The latest release there is [`0.5.7`](https://github.com/openai/mujoco-py/releases/tag/0.5.7) which can be installed with `pip install mujoco-py==0.5.7`.



### Install MuJoCo



1. Download the MuJoCo version 2.1 binaries for

   [Linux](https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz) or

   [OSX](https://mujoco.org/download/mujoco210-macos-x86_64.tar.gz).

1. Extract the downloaded `mujoco210` directory into `~/.mujoco/mujoco210`.



If you want to specify a nonstandard location for the package,

use the env variable `MUJOCO_PY_MUJOCO_PATH`.



### Install and use `mujoco-py`

To include `mujoco-py` in your own package, add it to your requirements like so:

```

mujoco-py<2.2,>=2.1

```

To play with `mujoco-py` interactively, follow these steps:

```

$ pip3 install -U 'mujoco-py<2.2,>=2.1'

$ python3

import mujoco_py

import os

mj_path = mujoco_py.utils.discover_mujoco()

xml_path = os.path.join(mj_path, 'model', 'humanoid.xml')

model = mujoco_py.load_model_from_path(xml_path)

sim = mujoco_py.MjSim(model)



print(sim.data.qpos)

# [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]



sim.step()

print(sim.data.qpos)

# [-2.09531783e-19  2.72130735e-05  6.14480786e-22 -3.45474715e-06

#   7.42993721e-06 -1.40711141e-04 -3.04253586e-04 -2.07559344e-04

#   8.50646247e-05 -3.45474715e-06  7.42993721e-06 -1.40711141e-04

#  -3.04253586e-04 -2.07559344e-04 -8.50646247e-05  1.11317030e-04

#  -7.03465386e-05 -2.22862221e-05 -1.11317030e-04  7.03465386e-05

#  -2.22862221e-05]

```



See the [full documentation](https://openai.github.io/mujoco-py/build/html/index.html) for advanced usage.



## Troubleshooting



### You're on MacOS and you see `clang: error: unsupported option '-fopenmp'`



If this happend during installation or just running `python -c "import mujoco_py"` then the issue seems to be related to [this](https://github.com/velocyto-team/velocyto.R/issues/2#issuecomment-341165967) and the TL;DR is that for macOS the default compiler Apple clang LLVM does not support openmp. So you can try to install another clang/llvm installation. For example (requires [brew](https://brew.sh/)):



```bash

brew install llvm

brew install boost

brew install hdf5



# Add this to your .bashrc/.zshrc:

export PATH="/usr/local/opt/llvm/bin:$PATH"



export CC="/usr/local/opt/llvm/bin/clang"

export CXX="/usr/local/opt/llvm/bin/clang++"

export CXX11="/usr/local/opt/llvm/bin/clang++"

export CXX14="/usr/local/opt/llvm/bin/clang++"

export CXX17="/usr/local/opt/llvm/bin/clang++"

export CXX1X="/usr/local/opt/llvm/bin/clang++"



export LDFLAGS="-L/usr/local/opt/llvm/lib"

export CPPFLAGS="-I/usr/local/opt/llvm/include"

```



**Note:** Don't forget to source your `.bashrc/.zshrc` after editing it and try to install `mujoco-py` again:



```bash

# Make sure your python environment is activated

pip install -U 'mujoco-py<2.2,>=2.1'

```



### Missing GLFW



A common error when installing is:



    raise ImportError("Failed to load GLFW3 shared library.")



Which happens when the `glfw` python package fails to find a GLFW dynamic library.



MuJoCo ships with its own copy of this library, which can be used during installation.



Add the path to the mujoco bin directory to your dynamic loader:



    LD_LIBRARY_PATH=$HOME/.mujoco/mujoco210/bin pip install mujoco-py



This is particularly useful on Ubuntu 14.04, which does not have a GLFW package.



### Ubuntu installtion troubleshooting



Because `mujoco_py` has compiled native code that needs to be linked to a supplied MuJoCo binary, it's installation

on linux can be more challenging than pure Python source packages.



To install mujoco-py on Ubuntu, make sure you have the following libraries installed:



    sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3



If you installed above libraries and you still see an error that `-lGL` cannot be found, most likely you need

to create the symbolic link directly:



    sudo ln -s /usr/lib/x86_64-linux-gnu/libGL.so.1 /usr/lib/x86_64-linux-gnu/libGL.so



## Usage Examples



A number of examples demonstrating some advanced features of `mujoco-py` can be found in [`examples/`](/./examples/). These include:

- [`body_interaction.py`](./examples/body_interaction.py): shows interactions between colliding bodies

- [`disco_fetch.py`](./examples/disco_fetch.py): shows how `TextureModder` can be used to randomize object textures

- [`internal_functions.py`](./examples/internal_functions.py): shows how to call raw mujoco functions like `mjv_room2model`

- [`markers_demo.py`](./examples/markers_demo.py): shows how to add visualization-only geoms to the viewer

- [`serialize_model.py`](./examples/serialize_model.py): shows how to save and restore a model

- [`setting_state.py`](./examples/setting_state.py):  shows how to reset the simulation to a given state

- [`tosser.py`](./examples/tosser.py): shows a simple actuated object sorting robot application



See the [full documentation](https://openai.github.io/mujoco-py/build/html/index.html) for advanced usage.



## Development



To run the provided unit and integrations tests:



```

make test

```



To test GPU-backed rendering, run:



```

make test_gpu

```



This is somewhat dependent on internal OpenAI infrastructure at the moment, but it should run if you change the `Makefile` parameters for your own setup.



## Changelog



- 03/08/2018: We removed MjSimPool, because most of benefit one can get with multiple processes having single simulation.



## Credits



`mujoco-py` is maintained by the OpenAI Robotics team. Contributors include:



- Alex Ray

- Bob McGrew

- Jonas Schneider

- Jonathan Ho

- Peter Welinder

- Wojciech Zaremba

- Jerry Tworek