A Deep Reinforcement Learning Environment for Particle Robot Navigation and Object Manipulation
release_hyto64ijf5fr3f3njjwbddxu2a
by
Jeremy Shen, Erdong Xiao, Yuchen Liu, Chen Feng
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) |
2203.06464v1
access all versions, variants, and formats of this works (eg, pre-prints)