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update README
This commit is contained in:
Brian Delhaisse
@@ -1,147 +0,0 @@
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# PyRoboLearn
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This repository contains the code for the *PyRoboLearn* (PRL) framework: a Python framework for Robot Learning.
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This framework revolves mainly around 7 axes: simulators, worlds, robots, interfaces, learning tasks (= environment and policy), learning models, and learning algorithms.
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**Warning**: The development of this framework is ongoing, and thus some substantial changes might occur. Sorry for the inconvenience.
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## Requirements
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The framework has been tested with Python 2.7 and Ubuntu 16.04 and 18.04. We also tested parts of it with Python 3.5 on Ubuntu 16.04 and so far so good, but there might be some errors that escaped my attention.
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## Installation
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### Docker
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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.
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In the future, ROS may be splitted in another container and linked to this one.
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1. Install Docker and nvidia-docker
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```bash
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$ sudo apt-get update
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$ sudo apt install apt-transport-https ca-certificates curl software-properties-common
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$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
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$ sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu bionic stable # you should replace bionic by your version
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$ sudo apt update
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$ sudo apt install docker-ce
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$ sudo systemctl status docker # check that docker is active
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```
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2. Build the image
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```bash
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$ docker build -t pyrobolearn .
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```
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3. Launch
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You can now start the python interpreter with every library already installed
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```bash
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$ docker run -p 11311:11311 -v $PWD/dev:/pyrobolearn/dev/:rw -ti pyrobolearn python3
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```
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To open an interactive terminal in the docker image use:
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```bash
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$ docker run -p 11311:11311 -v $PWD/dev:/pyrobolearn/dev/:rw -ti pyrobolearn /bin/bash
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```
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4. nvidia-docker
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if the GPU is not recognized in the interpreter, you can install nvidia-docker
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```bash
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$ curl -sL https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
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$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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$ curl -sL https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
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$ sudo apt-get update
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$ sudo apt-get install nvidia-docker2
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sudo pkill -SIGHUP dockerd
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```
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And use:
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```bash
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$ nvidia-docker run -p 11311:11311 -v $PWD/dev:/pyrobolearn/dev/:rw -ti pyrobolearn
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```
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### Ubuntu
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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/
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On Ubuntu, you can install `pip` and `virtualenv` by typing in the terminal:
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- In Python 2.7:
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```bash
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sudo apt install python-pip
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sudo pip install virtualenv
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```
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- In Python 3.5:
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```bash
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sudo apt install python3-pip
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sudo pip install virtualenv
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```
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You can then create the virtual environment by typing:
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```bash
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virtualenv -p /usr/bin/python<version> <virtualenv_name>
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# activate the virtual environment
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source <virtualenv_name>/bin/activate
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```
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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`.
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To deactivate the virtual environment, just type:
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```bash
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deactivate
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```
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2. clone this repository and install the requirements by executing the setup.py
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In Python 2.7:
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```bash
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git clone https://github.com/robotlearn/pyrobolearn
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cd pyrobolearn
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pip install numpy cython
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pip install http://github.com/cornellius-gp/gpytorch/archive/alpha.zip # this is for Python 2.7
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pip install -e . # this will install pyrobolearn as well as the required packages (so no need for: pip install -r requirements.txt)
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```
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In Python 3.5:
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```bash
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git clone https://github.com/robotlearn/pyrobolearn
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cd pyrobolearn
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pip install numpy cython
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pip install gpytorch # this is for Python 3.5
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pip install -e . # this will install pyrobolearn as well as the required packages (so no need for: pip install -r requirements.txt)
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```
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Depending on your computer configuration and the python version you use, you might need to install also the following packages through `apt-get`:
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```bash
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sudo apt install python-tk # if python 2.7
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sudo apt install python3-tk # if python 3.5
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```
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## How to use it?
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Check the `README.md` file in the `examples` folder.
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## Citation
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```
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@misc{delhaisse2019pyrobolearn,
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author = {Delhaisse, Brian and Xin, Songyan and Rozo, Leonel, and Caldwell, Darwin},
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title = {PyRoboLearn: A Python Framework for Robot Learning Practitioners},
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howpublished = {\url{https://github.com/robotlearn/pyrobolearn}},
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year=2019,
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}
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```
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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.
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## Acknowledgements
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Currently, we mainly use the PyBullet simulator.
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- *PyBullet, a Python module for physics simulation for games, robotics and machine learning*,
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Erwin Coumans and Yunfei Bai, 2016-2019
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- references for each robot, model, and others can be found in the corresponding class documentation
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- Locomotion controllers were provided by Songyan Xin (see `pyrobolearn/controllers/locomotion`).
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+177
@@ -0,0 +1,177 @@
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PyRoboLearn
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===========
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This repository contains the code for the *PyRoboLearn* (PRL) framework: a Python framework for Robot Learning.
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This framework revolves mainly around 7 axes: simulators, worlds, robots, interfaces, learning tasks (= environment and policy), learning models, and learning algorithms.
|
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**Warning**: The development of this framework is ongoing, and thus some substantial changes might occur. Sorry for the inconvenience.
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.. toctree::
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:maxdepth: 2
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Requirements
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------------
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The framework has been tested with Python 2.7 and Ubuntu 16.04 and 18.04. We also tested parts of it with Python 3.5 on Ubuntu 16.04 and so far so good, but there might be some errors that escaped my attention.
|
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|
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|
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Installation
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------------
|
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|
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Docker
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~~~~~~
|
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|
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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.
|
||||
|
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1. Install Docker and nvidia-docker
|
||||
|
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.. code-block:: bash
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|
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sudo apt-get update
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sudo apt install apt-transport-https ca-certificates curl software-properties-common
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curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
|
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sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu bionic stable # you should replace bionic by your version
|
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sudo apt update
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sudo apt install docker-ce
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sudo systemctl status docker # check that docker is active
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2. Build the image
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.. code-block:: bash
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docker build -t pyrobolearn .
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3. Launch
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|
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You can now start the python interpreter with every library already installed
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.. code-block:: bash
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docker run -p 11311:11311 -v $PWD/dev:/pyrobolearn/dev/:rw -ti pyrobolearn python3
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|
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To open an interactive terminal in the docker image use:
|
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|
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.. code-block:: bash
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docker run -p 11311:11311 -v $PWD/dev:/pyrobolearn/dev/:rw -ti pyrobolearn /bin/bash
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4. nvidia-docker
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if the GPU is not recognized in the interpreter, you can install nvidia-docker
|
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|
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.. code-block:: bash
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curl -sL https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
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distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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curl -sL https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
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sudo apt-get update
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sudo apt-get install nvidia-docker2
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sudo pkill -SIGHUP dockerd
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And use:
|
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|
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.. code-block:: bash
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nvidia-docker run -p 11311:11311 -v $PWD/dev:/pyrobolearn/dev/:rw -ti pyrobolearn
|
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|
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|
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Ubuntu
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~~~~~~
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|
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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
|
||||
|
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sudo apt install python-pip
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sudo pip install virtualenv
|
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|
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- In Python 3.5:
|
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|
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.. code-block:: bash
|
||||
|
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sudo apt install python3-pip
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sudo pip install virtualenv
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|
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You can then create the virtual environment by typing:
|
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|
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.. code-block:: bash
|
||||
|
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virtualenv -p /usr/bin/python<version> <virtualenv_name>
|
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# activate the virtual environment
|
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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:
|
||||
|
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.. code-block:: bash
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||||
|
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deactivate
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|
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2. clone this repository and install the requirements by executing the ``setup.py``
|
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|
||||
In Python 2.7:
|
||||
|
||||
.. code-block:: bash
|
||||
|
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git clone https://github.com/robotlearn/pyrobolearn
|
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cd pyrobolearn
|
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pip install numpy cython
|
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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:
|
||||
|
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.. code-block:: bash
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git clone https://github.com/robotlearn/pyrobolearn
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cd pyrobolearn
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pip install numpy cython
|
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pip install gpytorch # this is for Python 3.5
|
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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
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sudo apt install python3-tk # if python 3.5
|
||||
|
||||
|
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How to use it?
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--------------
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Check the ``README.rst`` file in the ``examples`` folder.
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Citation
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--------
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.. code-block:: latex
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@misc{delhaisse2019pyrobolearn,
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author = {Delhaisse, Brian and Xin, Songyan and Rozo, Leonel, and Caldwell, Darwin},
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title = {PyRoboLearn: A Python Framework for Robot Learning Practitioners},
|
||||
howpublished = {\url{https://github.com/robotlearn/pyrobolearn}},
|
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year=2019,
|
||||
}
|
||||
|
||||
|
||||
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 (see ``pyrobolearn/controllers/locomotion``)
|
||||
Reference in New Issue
Block a user