lindockerryan/mujoco-py repository overview

⁠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 3.5.2. See the Dockerfile⁠ for the canonical list of system dependencies. Support for Python 3.6 is planned⁠.
• OS X with Python 3.5.2. Support for Python 3.6 is planned⁠.
• Windows (experimental) with Python 3.5.2. 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
• simpool.py⁠: shows how MjSimPool can be used to run a number of simulations in parallel
• 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.

⁠Credits

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

• Alex Ray
• Bob McGrew
• Jonas Schneider
• Jonathan Ho
• Peter Welinder
• Wojciech Zaremba