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

TL;DR

This paper introduces a 2D particle robot simulator and benchmarks for applying deep reinforcement learning to control particle robots, highlighting current challenges and the need for further algorithm development.

Contribution

It presents a new simulation environment and benchmark tasks for DRL in particle robot systems, addressing unique challenges of low degrees of freedom and coordination.

Findings

Baseline DRL algorithms partially succeed in tasks.

Current algorithms do not match hand-crafted policy performance.

New challenges identified for DRL in particle robotics.

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.