raisimGym is an example of a gym environment using raisim. It uses stable-baselines (https://github.com/hill-a/stable-baselines) for training and pybind11 (https://github.com/pybind/pybind11) for wrapping raisim in python.

• Linux only. support ubuntu 16.04 and 18.04 but might work on other distributions
• g++, gcc > 6 (how to install?)
• cmake > 3.10 (how to install?)

Please install/save everything locally to prevent corrupting your system files. We will assume that you have a single workspace where you save all repos related to raisim.

Here we introduce two variables

• WORKSPACE: workspace where you clone your git repos
• LOCAL_BUILD: build directory where you install exported cmake libraries

You can check out our docker file if you are stuck.

1. Install python3
   • For Ubuntu18.04 (sudo apt-get install -y cmake python3.6-dev python3-pip libpython3.6-dev)
   • For Ubuntu16.04 (sudo apt-get install -y cmake python3.5-dev python3-pip libpython3.5-dev)
2. Setup your python environment (virtualenv or conda) with Python > 3.5

Install the following dependencies

• Raisim (https://github.com/leggedrobotics/raisimLib)
• RaisimOgre (https://github.com/leggedrobotics/raisimOgre)
• yaml-cpp (sudo apt-get install libyaml-cpp-dev)

Now install pybind11 as following

cd $WORKSPACE
git clone https://github.com/pybind/pybind11.git
cd pybind11 && git checkout v2.2.4 && mkdir build && cd build
cmake .. -DCMAKE_INSTALL_PREFIX=$LOCAL_BUILD -DPYBIND11_TEST=OFF
make install -j4

You might have to add LOCAL_BUILD/lib to your $LD_LIBRARY_PATH to ensure that ld finds Ogre3D shared objects.

1. Compile your c++ environment (instructions followed)
2. Run your learning script (instructions followed)

or use the dockerfile provided (instruction below)

Set your compiler to g++>6.0 as following

export CXX=/usr/bin/g++-8 && export CC=/usr/bin/gcc-8

Now compile raisimGym as

python3 setup.py install --CMAKE_PREFIX_PATH LOCAL_BUILD --env /WHERE/YOUR/CUSTOM/ENVIRONMENT/IS

The "--env" directory should include a file called "Environment.hpp" which contains ENVIRONMENT class.

For the ANYmal example,

python3 setup.py install --CMAKE_PREFIX_PATH $LOCAL_BUILD --env anymal

For the Laikago example,

python3 setup.py install --CMAKE_PREFIX_PATH $LOCAL_BUILD --env laikago

You can make your own runner. To use the example runner,

python3 scripts/anymal_blind_locomotion.py

1. Install docker and Nvidia-docker2 (instruction).
2. Build an image docker build -t raisim_gym $WORKSPACE/raisimGym/dockers/gpu
3. Create a container using the provided docker runner $WORKSPACE/raisimGym/dockers/gpu/runner.bash
4. Inside the container, compile your environment and execute your runner

• Trained with RTX 2080 + Intel 8700K + PPO [1]
• 18 dof quadrupedal robot, 34-dimentional observation space

Initial policy	38 seconds of training

76 seconds of training	112 seconds of training

• Due to conversion between numpy and eigen, the interface class (e.g., VectorizedEnv) should use row major matrices only.

• When build fails, try again after deleting the build folder

raisimGym is meant to be a minimal example. Please do not submit a pull request that contains features targeting specific applications. Bug fix/code clean-up are welcome.

Fork the repo, make changes and then send a pull request. Instructions can be found here

[1] Schulman, John, et al. "Proximal policy optimization algorithms." arXiv preprint arXiv:1707.06347 (2017).