MoveRL: To A Safer Robotic Reinforcement Learning Environment

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

The deployment of Reinforcement Learning (RL) on physical robots still stumbles on several challenges, such as sample-efficiency, safety, reproducibility, cost, and software platforms. In this paper, we introduce MoveRL, an environment that exposes a standard OpenAI Gym interface, and allows any off-the-shelf RL agent to control a robot built on ROS, the Robot OS. ROS is the standard abstraction layer used by roboticists, and allows to observe and control both simulated and physical robots. By providing a bridge between the Gym and ROS, our environment allows an easy evaluation of RL algorithms in highly-accurate simulators, or real-world robots, without any change of software. In addition to a Gym-ROS bridge, our environment also leverages MoveIt, a state-of-the-art collision-aware robot motion planner, to prevent the RL agent from executing actions that would lead to a collision. Our experimental results show that a standard PPO agent is able to control a simulated commercial robot arm in an environment with moving obstacles, while almost perfectly avoiding collisions even in the early stages of learning. We also show that the use of MoveIt slightly increases the sample-efficiency of the RL agent. Combined, these results show that RL on robots is possible in a safe way, and that it is possible to leverage state-of-the-art robotic techniques to improve how an RL agent learns. We hope that our environment will allow more (future) RL algorithms to be evaluated on commercial robotic tasks.
Original languageEnglish
Title of host publicationThe 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2021)
Subtitle of host publicationAI in ACTION Joint International Scientific Conferences on AI
EditorsLuis A. Leiva, Cédric Pruski, Réka Markovich, Amro Najjar, Christoph Schommer
Place of PublicationCCIS
PublisherSpringer
Chapter5
Pages239-253
Number of pages15
Volume1530
ISBN (Electronic)978-3-030-93842-0
ISBN (Print)978-3-030-93841-3
DOIs
Publication statusPublished - 2022
EventBNAIC/BeneLearn 2021: 33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning - Luxembourg
Duration: 10 Nov 202112 Nov 2021
https://bnaic2021.uni.lu/

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume1530
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

ConferenceBNAIC/BeneLearn 2021
Period10/11/2112/11/21
Internet address

Keywords

  • Robotic
  • Safe Reinforcement Learning
  • Path Planning

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