A Deep Reinforcement Learning Environment for Particle Robot Navigation and Object Manipulation release_hyto64ijf5fr3f3njjwbddxu2a

by Jeremy Shen, Erdong Xiao, Yuchen Liu, Chen Feng

Released as a article .

2022  

Abstract

Particle robots are novel biologically-inspired robotic systems where locomotion can be achieved collectively and robustly, but not independently. While its control is currently limited to a hand-crafted policy for basic locomotion tasks, such a multi-robot system could be potentially controlled via Deep Reinforcement Learning (DRL) for different tasks more efficiently. However, the particle robot system presents a new set of challenges for DRL differing from existing swarm robotics systems: the low degrees of freedom of each robot and the increased necessity of coordination between robots. We present a 2D particle robot simulator using the OpenAI Gym interface and Pymunk as the physics engine, and introduce new tasks and challenges to research the underexplored applications of DRL in the particle robot system. Moreover, we use Stable-baselines3 to provide a set of benchmarks for the tasks. Current baseline DRL algorithms show signs of achieving the tasks but are yet unable to reach the performance of the hand-crafted policy. Further development of DRL algorithms is necessary in order to accomplish the proposed tasks.
In text/plain format

Archived Files and Locations

application/pdf  1.4 MB
file_fhvkqfw5hnf3zio6fd7tjaoauu
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2022-03-12
Version   v1
Language   en ?
arXiv  2203.06464v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 4aecc62d-7969-441b-a725-508f96d2fafc
API URL: JSON