pyrobolearn

PyRoboLearn: a Python framework for Robot Learning

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PyRoboLearn

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.

Requirements

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.

Installation

There are two ways to install the framework:

  1. using a virtual environment and pip
  2. using a Docker

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.

1. Install Docker and nvidia-docker

.. 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

2. Build the image

.. code-block:: bash

    docker build -t pyrobolearn .


3. Launch


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


4. nvidia-docker
if the GPU is not recognized in the interpreter, you can install nvidia-docker

.. 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.

  1. 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.

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