tamilselvanarjun / roboschool

NEWS

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.

Roboschool

Release blog post is here:

https://blog.openai.com/roboschool/

Roboschool is a long-term project to create simulations useful for research. The roadmap is as follows:

1. Replicate Gym MuJoCo environments.
2. Take a step away from trajectory-centric fragile MuJoCo tasks.
3. Explore multiplayer games.
4. Create tasks with camera RGB image and joints in a tuple.
5. Teach robots to follow commands, including verbal commands.

Some wiki pages:

Contributing New Environments

Help Wanted

Environments List

The list of Roboschool environments is as follows:

• RoboschoolInvertedPendulum-v0
• RoboschoolInvertedPendulumSwingup-v0
• RoboschoolInvertedDoublePendulum-v0
• RoboschoolReacher-v0
• RoboschoolHopper-v0
• RoboschoolWalker2d-v0
• RoboschoolHalfCheetah-v0
• RoboschoolAnt-v0
• RoboschoolHumanoid-v0
• RoboschoolHumanoidFlagrun-v0
• RoboschoolHumanoidFlagrunHarder-v0
• RoboschoolPong-v0

To obtain this list: import roboschool, gym; print("\n".join(['- ' + spec.id for spec in gym.envs.registry.all() if spec.id.startswith('Roboschool')])).

Installation

First, define a ROBOSCHOOL_PATH variable in the current shell. It will be used in this README but not anywhere in the Roboschool code.

ROBOSCHOOL_PATH=/path/to/roboschool

If you have both Python2 and Python3 on your system, use python3 and pip3 commands.

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.

• Ubuntu:

  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 pyopengl can make OpenGL errors go away, because it arranges OpenGL libraries in an Ubuntu system in the right way.

• Linuxbrew

  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.

Agent Zoo

We have provided a number of pre-trained agents in the agent_zoo directory.

To see a humanoid run towards a random varying target:

python $ROBOSCHOOL_PATH/agent_zoo/RoboschoolHumanoidFlagrun_v0_2017may.py

To see three agents in a race:

python $ROBOSCHOOL_PATH/agent_zoo/demo_race2.py