This repository contains the code for the PyRoboLearn (PRL) framework: a Python framework for Robot Learning. This framework revolves mainly around 7 axes: simulators, worlds, robots, interfaces, learning tasks (= environment and policy), learning models, and learning algorithms.
Warning: The development of this framework is ongoing, and thus some substantial changes might occur. Sorry for the inconvenience.
The framework has been tested with Python 2.7, 3.5 and 3.6, on Ubuntu 16.04 and 18.04. The installation on other OS is experimental.
There are two ways to install the framework:
Virtualenv & Pip
1. First download the ``pip`` Python package manager and create a virtual environment for Python as described in the following link: https://packaging.python.org/guides/installing-using-pip-and-virtualenv/
On Ubuntu, you can install ``pip`` and ``virtualenv`` by typing in the terminal:
- In Python 2.7:
.. code-block:: bash
sudo apt install python-pip
sudo pip install virtualenv
- In Python 3.5:
.. code-block:: bash
sudo apt install python3-pip
sudo pip install virtualenv
You can then create the virtual environment by typing:
.. code-block:: bash
virtualenv -p /usr/bin/python<version> <virtualenv_name>
# activate the virtual environment
source <virtualenv_name>/bin/activate
where ``<version>`` is the python version you want to use (select between ``2.7`` or ``3.5``), and ``<virtualenv_name>`` is a name of your choice for the virtual environment. For instance, it can be ``py2.7`` or ``py3.5``.
To deactivate the virtual environment, just type:
.. code-block:: bash
deactivate
2. clone this repository and install the requirements by executing the ``setup.py``
In Python 2.7:
.. code-block:: bash
git clone https://github.com/robotlearn/pyrobolearn
cd pyrobolearn
pip install numpy cython
pip install http://github.com/cornellius-gp/gpytorch/archive/alpha.zip # this is for Python 2.7
pip install -e . # this will install pyrobolearn as well as the required packages (so no need for: pip install -r requirements.txt)
In Python 3.5:
.. code-block:: bash
git clone https://github.com/robotlearn/pyrobolearn
cd pyrobolearn
pip install numpy cython
pip install gpytorch # this is for Python 3.5
pip install -e . # this will install pyrobolearn as well as the required packages (so no need for: pip install -r requirements.txt)
Depending on your computer configuration and the python version you use, you might need to install also the following packages through ``apt-get``:
.. code-block:: bash
sudo apt install python-tk # if python 2.7
sudo apt install python3-tk # if python 3.5
Docker
At the moment the docker is a self contained Ubuntu image with all the libraries installed. When launched we have access to a Python3.6 interpreter and we can import pyrobolearn directly. In the future, ROS may be splitted in another container and linked to this one.
.. code-block:: bash
sudo apt-get update
sudo apt install apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu bionic stable # you should replace bionic by your version
sudo apt update
sudo apt install docker-ce
sudo systemctl status docker # check that docker is active
.. code-block:: bash
docker build -t pyrobolearn .
You can now start the python interpreter with every library already installed
.. code-block:: bash
docker run -p 11311:11311 -v $PWD/dev:/pyrobolearn/dev/:rw -ti pyrobolearn python3
To open an interactive terminal in the docker image use:
.. code-block:: bash
docker run -p 11311:11311 -v $PWD/dev:/pyrobolearn/dev/:rw -ti pyrobolearn /bin/bash
.. code-block:: bash
curl -sL https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -sL https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install nvidia-docker2
sudo pkill -SIGHUP dockerd
And use:
.. code-block:: bash
nvidia-docker run -p 11311:11311 -v $PWD/dev:/pyrobolearn/dev/:rw -ti pyrobolearn
Other Operating Systems
Note that some interfaces (like game controllers, depth camera, etc) might not be available on other OS, however the
main robotic framework should work.
1. Windows: You will have to install first PyBullet and NLopt beforehand.
For nlopt, install first ``conda``, then type:
.. code-block:: bash
conda install -c conda-forge nlopt
If Pybullet doesn't install on Windows (using visual studio), you might have to copy ``rc.exe`` and ``rc.dll`` from
``C:\Program Files (x86)\Windows Kits\10\bin\<xx.x.xxxx.x>\x64``
to
``C:\Program Files (x86)\Windows Kits\10\bin\x86``
And add the last folder to the Windows environment path (Go to ``System Properties`` > ``Advanced`` > ``Environment Variables`` > ``Path``
> ``Edit``).
Finally, remove the nlopt package from the ``requirements.txt``. The rest of the installation should be straightforward.
2. Mac OSX: We managed to install the PyRoboLearn framework on MacOSX (Mojave) by following the procedures explained in the section
"Virtualenv & Pip". You can replace the ``sudo apt install`` by ``brew install`` (after installing `Homebrew <https://brew.sh/>`_).
How to use it?
--------------
Check the ``README.rst`` file in the ``examples`` folder.
License
-------
PyRoboLearn is currently released under the `GNU GPLv3 <https://choosealicense.com/licenses/gpl-3.0/>`_ license.
Citation
--------
For how to cite this repository, please refer to the ``CITATION.rst`` file.
If you use a specific learning model, algorithm, robot, controller, and so on, please cite the corresponding paper. The reference(s) can usually be found in the class documentation (at the end), and sometimes in the README file in the corresponding folder.
Acknowledgements
----------------
Currently, we mainly use the PyBullet simulator.
- *PyBullet, a Python module for physics simulation for games, robotics and machine learning*, Erwin Coumans and
Yunfei Bai, 2016-2019
- References for each robot, model, and others can be found in the corresponding class documentation
- Locomotion controllers were provided by Songyan Xin
- We thanks Daniele Bonatto for providing the Docker file, and test the installation on Windows.