Moment Latent Reinforcement Learning for Pattern Control in Swarm Robotic Systems

Abstract

Targeted coordination of swarm robotic systems is an emerging robot control task arising from numerous applications across diverse domains, ranging from medicine and agriculture to cyber-physical systems. However, state-of-the-art control techniques for robot swarms often require comprehensive measurement data for each robot and are not scalable with the growth of the swarm size. To address these issues, in this work, we develop a latent space control architecture for robust manipulation of patterns in arbitrarily large, potentially infinite, robot swarms using only partial measurements. In particular, we model such a swarm as a parameterized control system and formulate its patterns in terms of probability distributions. We then develop a moment kernel transform, which generates a reduced latent space representation for the pattern dynamics of the robot swarm over a reproducing kernel Hilbert space. The moment representation of the robot swarm can be learned using partial measurements of the swarm. Building on this, we propose a reinforcement learning (RL)-based pattern control framework operating on the moment latent space. In this framework, the data is organized to flow between the workspace and moment latent space episodically to achieve both robust control performance and high training efficiency. The proposed moment latent RL framework is validated by various pattern control tasks involving wheeled robot swarms, using both numerical simulations and TurtleBot3 swarms in the Gazebo simulator.