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State Representation Learning With Robotics Priors

Code accompanying the paper:

Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Nicolò Botteghi, Khaled Alaa, Mannes Poel, Beril Sirmacek, Christoph Brune, Abeje Mersha, Stefano Stramigioli The full paper, published at IROS-2021, can be found at: Link.

Low Dimensional State Representation Learning with Reward-Shaped Priors
Nicolò Botteghi, Ruben Obbink, Daan Geijs, Mannes Poel, Beril Sirmacek, Christoph Brune, Abeje Mersha, Stefano Stramigioli The full paper, published at ICPR-2021, can be found at: Link

Framework alt text

Abstract: Autonomous robots require high degrees of cognitive and motoric intelligence to come into our
everyday life. In non-structured environments and in the presence of uncertainties, such degrees of intelligence
are not easy to obtain. Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion without any need for hand-crafted features or policies. Especially in the
context of robotics, in which the cost of real-world data is usually extremely high, Reinforcement
Learning solutions achieving high sample efficiency are needed. In this paper, we propose a framework
combining the learning of a low-dimensional state representation, from high-dimensional observations coming
from the robot’s raw sensory readings, with the learning of the optimal policy, given the learned state representation. We evaluate our framework in the context of mobile robot navigation. Moreover, we study the problem of transferring what learned in the simulated
virtual environment to the real robot without further retraining using real-world data in the presence
of visual and depth distractors, such as lighting changes and moving obstacles. Videos of our experiments can be found at: Link and Link.

Getting Started

These instructions will get you a copy of the project up and running on your local machine

Prerequisites

  • Python 2.7
  • PyCharm

Installing

Pull the project

git clone [email protected]:nicob15/StateRepresentationWithRoboticsPriors.git

Libraries required for running the main code:

pip install tensorflow-gpu==1.14
pip install tensorflow-probability==0.7
pip install dm-sonnet==1.35

Libraries required for plotting:

pip install matplotlib
pip install multicoreTSNE
pip install sklearn

Running SRL_RoboticsPriors_ContinuousActions requires a data set of the type:

(o,a,r,d,s)
where
o --> observation (see our paper for more info)
a --> action
r --> reward
d --> done
s --> sample number

An example of data set can be downloaded from: https://surfdrive.surf.nl/files/index.php/s/34IjGfYd0IAr4Pn

Simulation Platform

The complete ROS-Gazebo simulation platform used in our experiments can be found at: Link

Cite

If you use this code in your own work, please cite our papers:

@INPROCEEDINGS{9635936,

  author={Botteghi, Nicolò and Alaa, Khaled and Poel, Mannes and Sirmacek, Beril and Brune, Christoph and Mersha, Abeje and Stramigioli, Stefano},

  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 

  title={Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces}, 

  year={2021},

  volume={},

  number={},

  pages={190-197},

  doi={10.1109/IROS51168.2021.9635936}}


@INPROCEEDINGS{9412421,

  author={Botteghi, Nicolò and Obbink, Ruben and Geijs, Daan and Poel, Mannes and Sirmacek, Beril and Brune, Christoph and Mersha, Abeje and Stramigioli, Stefano},

  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)}, 

  title={Low Dimensional State Representation Learning with Reward-shaped Priors}, 

  year={2021},

  volume={},

  number={},

  pages={3736-3743},

  doi={10.1109/ICPR48806.2021.9412421}}