Moment Latent Reinforcement Learning for Pattern Control in Swarm Robotic Systems
Wei Zhang, Haoyu Quan, Jr-Shin Li
AI summary
Problem
Existing swarm control methods require comprehensive per-robot measurements and fail to scale with increasing swarm sizes, while data-driven approaches suffer from the curse of dimensionality and high computational costs.
Approach
The authors model the swarm as a parameterized ensemble system and map its spatial distribution to a reduced moment latent space via a moment kernel transform. Reinforcement learning is then applied in this latent space, with episodic data exchange between the latent and physical workspaces to ensure unbiased and efficient training.
Key results
- Formulation of parameterized ensemble systems for arbitrarily large robot swarms
- Development of a moment kernel transform mapping swarm patterns to a reduced RKHS
- Design of an episodic moment latent RL architecture for scalable pattern control
- Validation via numerical and 3D Gazebo simulations demonstrating robust performance and training efficiency
Why it matters
Provides a scalable, sensing-efficient control paradigm for large-scale heterogeneous robot swarms in applications ranging from precision agriculture to medical micromanipulation.
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 comprehen- sive 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 dis- tributions. 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.