Status: Maintenance (expect bug fixes and minor updates)
2018 July 9
Hi everyone! We wanted to let you know why we haven't pushed changes into this repo for a while: we're working on roboschool2, a new codebase with different priorities, which we hope will further accelerate robotics research. We continue to recommend the use of roboschool1 Hopper, Ant, Humanoid and Flagrun for evaluation and testing of algorithms. If you have fixes to make installation easier we'll be happy to merge it. We'll have more to share about roboschool2 in a while.
2017 July 17, Version 1.1
- All envs version bumped to “-v1", due to stronger stuck joint punishment, that improves odds of getting a good policy.
- Flagrun-v1 is much more likely to develop a symmetric gait,
- FlagrunHarder-v1 has new "repeat-underlearned" learning schedule, that allows it to be trained to stand up, walk and turn without falling.
- Atlas robot model, modified (empty links removed, overly powerful feet weakaned).
- All -v1 envs are shipped with better zoo policies, compared to May versions.
- Keyboard-controlled humanoid included.
Release blog post is here:
Roboschool is a long-term project to create simulations useful for research. The roadmap is as follows:
- Replicate Gym MuJoCo environments.
- Take a step away from trajectory-centric fragile MuJoCo tasks.
- Explore multiplayer games.
- Create tasks with camera RGB image and joints in a tuple.
- Teach robots to follow commands, including verbal commands.
Some wiki pages:
The list of Roboschool environments is as follows:
To obtain this list:
import roboschool, gym; print("\n".join(['- ' + spec.id for spec in gym.envs.registry.all() if spec.id.startswith('Roboschool')])).
First, define a
ROBOSCHOOL_PATH variable in the current shell. It will be used in this README but not anywhere in the Roboschool code.
If you have both Python2 and Python3 on your system, use
The dependencies are gym, Qt5, assimp, tinyxml, and bullet (from a branch). For the non-bullet deps, there are several options, depending on what platform and package manager you are using.
apt install cmake ffmpeg pkg-config qtbase5-dev libqt5opengl5-dev libassimp-dev libpython3.5-dev libboost-python-dev libtinyxml-dev
Users report in issue #15 that
sudo pip3 install pyopenglcan make OpenGL errors go away, because it arranges OpenGL libraries in an Ubuntu system in the right way.
brew install boost-python --without-python --with-python3 --build-from-source export C_INCLUDE_PATH=/home/user/.linuxbrew/include:/home/user/.linuxbrew/include/python3.6m export CPLUS_INCLUDE_PATH=/home/user/.linuxbrew/include:/home/user/.linuxbrew/include/python3.6m export LIBRARY_PATH=/home/user/.linuxbrew/lib export PKG_CONFIG_PATH=/home/user/.linuxbrew/lib/pkgconfig:/usr/lib/pkgconfig:/usr/lib/x86_64-linux-gnu/pkgconfig
(still use Qt from Ubuntu, because it's known to work)
Mac, homebrew python:
# Will not work on Mavericks: unsupported by homebrew, some libraries won't compile, upgrade first brew install python3 brew install cmake tinyxml assimp ffmpeg qt brew reinstall boost-python --without-python --with-python3 --build-from-source export PATH=/usr/local/bin:/usr/local/opt/qt5/bin:$PATH export PKG_CONFIG_PATH=/usr/local/opt/qt5/lib/pkgconfig
Mac, Anaconda with Python 3
brew install cmake tinyxml assimp ffmpeg brew reinstall boost-python --without-python --with-python3 --build-from-source conda install qt export PKG_CONFIG_PATH=$(dirname $(dirname $(which python)))/lib/pkgconfig
Compile and install bullet as follows. Note that
make install will merely copy files into the roboschool directory.
git clone https://github.com/olegklimov/bullet3 -b roboschool_self_collision mkdir bullet3/build cd bullet3/build cmake -DBUILD_SHARED_LIBS=ON -DUSE_DOUBLE_PRECISION=1 -DCMAKE_INSTALL_PREFIX:PATH=$ROBOSCHOOL_PATH/roboschool/cpp-household/bullet_local_install -DBUILD_CPU_DEMOS=OFF -DBUILD_BULLET2_DEMOS=OFF -DBUILD_EXTRAS=OFF -DBUILD_UNIT_TESTS=OFF -DBUILD_CLSOCKET=OFF -DBUILD_ENET=OFF -DBUILD_OPENGL3_DEMOS=OFF .. make -j4 make install cd ../..
Finally, install project itself:
pip3 install -e $ROBOSCHOOL_PATH
Now, check to see if it worked by running a pretrained agent from the agent zoo.
We have provided a number of pre-trained agents in the
To see a humanoid run towards a random varying target:
To see three agents in a race: