Learning to Communicate and Coordinate: Distributed Learning-based Control for Multi-Robot Systems

Multi-robot systems represent a key class of multi-agent systems where multiple autonomous agents cooperate to achieve tasks beyond the capabilities of a single robot. Their effectiveness relies on decentralized or distributed control, where collective behaviors emerge from local interactions under limited information and communication. Classical approaches have enabled significant progress in understanding coordination, information flow, and connectivity maintenance. However, these methods often depend on strong modeling assumptions and struggle to scale in dynamic, uncertain environments. Recent advances in machine learning provide promising alternatives by allowing agents to learn coordination strategies from data, enabling robustness and adaptability under partial observability and noisy sensing. Yet, existing learning-based frameworks rarely account for explicit communication, a critical factor in scalable multi-robot coordination that has been central to control-theoretic approaches. This dissertation addresses this gap by developing hybrid methods that integrate learning with communication-aware distributed control. By combining the generalization and flexibility of machine learning with the theoretical guarantees of control theory, this work advances the foundations of collective decision-making and contributes to the deployment of communication-aware, learning-based multi-robot systems in real-world applications.