CoCoL: A Communication Efficient Decentralized Collaborative Method for Multi-Robot Systems

-- Collaborative learning enhances the performance and adaptability of multi-robot systems in complex tasks but faces significant challenges due to high communication overhead and data heterogeneity inherent in multi-robot tasks. T o this end, we propose CoCoL, a Co mmunication efficient decentralized Co llaborative L earning method tailored for multi-robot systems with heterogeneous local datasets. Leveraging a mirror descent framework, CoCoL achieves remarkable communication efficiency with approximate Newton-type updates by capturing the similarity between objective functions of robots, and reduces computational costs through inexact sub-problem solutions. Furthermore, the integration of a gradient tracking scheme ensures its robustness against data heterogeneity. Experimental results on three representative multi-robot collaborative learning tasks show that the proposed CoCoL can significantly reduce both the number of communication rounds and total bandwidth consumption while maintaining state-of-the-art accuracy. These benefits are particularly evident in challenging scenarios involving non-IID (non-independent and identically distributed) data distribution, streaming data, and time-varying network topologies. I. INTRODUCTION Multi-robot systems offer the ability to tackle complex tasks through proper collaboration with enhanced efficiency, robustness, and flexibility compared to single-robot systems [1]. By sharing information, a team of robots can leverage collective knowledge to make more informed decisions and accomplish tasks in a coordinated manner.