This is a fork of mujoco-py where we will apply necessary patches. Current patches are:
- Create lockfiles in
builder.pyin the current working directory instead of in the mujoco-py installation directory. This allows the code to run in a Singularity container, where the installation directory is read-only. Earlier, we were creating the lockfile in $HOME or /tmp, but on the cluster, this failed because the home directory filesystem was downgraded, which meant the lockfile would run into issues with "Stale file handles." Creating in the current directory ensures we usually use the best filesystem. - Update to suport MuJoCo 2.1 from
this fork by
nimrod-gileadi.
Status: Maintenance (expect bug fixes and minor updates)
MuJoCo 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.
The following platforms are currently supported:
- Linux with Python 3.6+. See the
Dockerfilefor 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. One known good past version is 1.50.1.68.
- Python 2 has been DEPRECATED and removed in 1.50.1.0. Python 2 users can stay on the
0.5branch. The latest release there is0.5.7which can be installed withpip install mujoco-py==0.5.7.
- Download the MuJoCo version 2.1 binaries for Linux or OSX.
- Extract the downloaded
mujoco210directory into~/.mujoco/mujoco210.
If you want to specify a nonstandard location for the package,
use the env variable MUJOCO_PY_MUJOCO_PATH.
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 for advanced usage.
If this happend during installation or just running python -c "import mujoco_py" then the issue seems to be related to this 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):
brew install llvm
brew install boost
brew install hdf5
# Add this to your .bashrc/.zshrc:
export PATH="/usr/local/opt/llvm/bin:$PATH"
export CC="/usr/local/opt/llvm/bin/clang"
export CXX="/usr/local/opt/llvm/bin/clang++"
export CXX11="/usr/local/opt/llvm/bin/clang++"
export CXX14="/usr/local/opt/llvm/bin/clang++"
export CXX17="/usr/local/opt/llvm/bin/clang++"
export CXX1X="/usr/local/opt/llvm/bin/clang++"
export LDFLAGS="-L/usr/local/opt/llvm/lib"
export CPPFLAGS="-I/usr/local/opt/llvm/include"Note: Don't forget to source your .bashrc/.zshrc after editing it and try to install mujoco-py again:
# Make sure your python environment is activated
pip install -U 'mujoco-py<2.2,>=2.1'A common error when installing is:
raise ImportError("Failed to load GLFW3 shared library.")
Which happens when the glfw python package fails to find a GLFW dynamic library.
MuJoCo ships with its own copy of this library, which can be used during installation.
Add the path to the mujoco bin directory to your dynamic loader:
LD_LIBRARY_PATH=$HOME/.mujoco/mujoco210/bin pip install mujoco-py
This is particularly useful on Ubuntu 14.04, which does not have a GLFW package.
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
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 bodiesdisco_fetch.py: shows howTextureModdercan be used to randomize object texturesinternal_functions.py: shows how to call raw mujoco functions likemjv_room2modelmarkers_demo.py: shows how to add visualization-only geoms to the viewerserialize_model.py: shows how to save and restore a modelsetting_state.py: shows how to reset the simulation to a given statetosser.py: shows a simple actuated object sorting robot application
See the full documentation for advanced usage.
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.
- 03/08/2018: We removed MjSimPool, because most of benefit one can get with multiple processes having single simulation.
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