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

Understand

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.