mujoco-py

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

Synopsis

Requirements

The following platforms are currently supported:

• Linux with Python 3.6. See the Dockerfile for the canonical list of system dependencies.
• OS X with Python 3.6.
• Windows (experimental) with Python 3.6. See the Appveyor file for the canonical list of dependencies.

Python 2 has been desupported since 1.50.1.0. Python 2 users can stay on the 0.5 branch. The latest release there is 0.5.7 which can be installed with pip install mujoco-py==0.5.7.

Install MuJoCo

1. Obtain a 30-day free trial on the MuJoCo website or free license if you are a student. The license key will arrive in an email with your username and password.
2. Download the MuJoCo version 1.50 binaries for Linux, OSX, or Windows.
3. Unzip the downloaded mjpro150 directory into ~/.mujoco/mjpro150, and place your license key (the mjkey.txt file from your email) at ~/.mujoco/mjkey.txt.

Install and use mujoco-py

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

mujoco-py<1.50.2,>=1.50.1

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

$ pip3 install -U 'mujoco-py<1.50.2,>=1.50.1'
$ python3
import mujoco_py
from os.path import dirname
model = mujoco_py.load_model_from_path(dirname(dirname(mujoco_py.__file__))  +"/xmls/claw.xml")
sim = mujoco_py.MjSim(model)

print(sim.data.qpos)
# [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]

sim.step()
print(sim.data.qpos)
# [  2.09217903e-06  -1.82329050e-12  -1.16711384e-07  -4.69613872e-11
#   -1.43931860e-05   4.73350204e-10  -3.23749942e-05  -1.19854057e-13
#   -2.39251380e-08  -4.46750545e-07   1.78771599e-09  -1.04232280e-08]

See the full documentation for advanced usage.

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/mjpro150/bin pip install mujoco-py

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

Usage Examples

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

• body_interaction.py: shows interactions between colliding bodies
• disco_fetch.py: shows how TextureModder can be used to randomize object textures
• internal_functions.py: shows how to call raw mujoco functions like mjv_room2model
• markers_demo.py: shows how to add visualization-only geoms to the viewer
• serialize_model.py: shows how to save and restore a model
• setting_state.py: shows how to reset the simulation to a given state
• tosser.py: shows a simple actuated object sorting robot application

See the full documentation 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