Keynote Speeches

Keynote Speech I

Empowering Multi-Robot Flocking in Complex Environments via Effective Communication: A Deep Reinforcement Learning Approach

Sam Tak Wu Kwong
Lingnan University

Multi-robot flocking is crucial for safe and cooperative navigation, with wide applications in logistics, service delivery, and mobile surveillance. Despite significant progress, developing effective flocking strategies under complex conditions remains challenging. Communication is a vital technique for multi-robot coordination. In this paper, we propose REIN, a novel deep reinforcement learning-based framework designed to improve communication effectiveness in leader-follower flocking systems through the Refinement and Enhancement of communication INformation. Firstly, regarding information refinement, a graph-based information refiner, integrating directed graph-structured communication with an innovative edge filter, is developed for selective multi-robot interaction. It helps robots adaptively focus on relevant neighbors, considerably alleviating information overload. Secondly, for information enhancement, a cognition-aligned information enhancer is designed that boosts information expressiveness by encouraging team consensus. It utilizes two cascaded leader-related objectives to optimize information towards cognitive alignment among decentralized followers. Extensive comparisons with state-of-the-art approaches and ablation versions demonstrate the superiority of our framework. Physical experiments are also conducted to validate its practicality.

Biosketch

KWONG Sam Tak Wu is the Associate Vice-President (Strategic Research), J.K. Lee Chair Professor of Computational Intelligence, the Dean of the School of Graduate Studies, and the Acting Dean of the School of Data Science of Lingnan University. Professor Kwong is a distinguished scholar in evolutionary computation, artificial intelligence (AI) solutions, and image/video processing, with a strong record of scientific innovations and real-world impacts. Professor Kwong is one of the most highly cited researchers by Clarivate in 2022, 2023 and 2024. He has also been actively engaged in knowledge transfer between academia and industry. He was elevated to IEEE Fellow in 2014 for his contributions to optimization techniques in cybernetics and video coding. He was the President of the IEEE Systems, Man, and Cybernetics Society (SMCS) in 2021-22. He is a fellow of the US National Academy of Inventors (NAI), the Canadian Academy of Engineering, and the Hong Kong Academy of Engineering (HKAE). Professor Kwong has a prolific publication record with over 350 journal articles and 160 conference papers with an h-index of 96 based on Google Scholar. He is currently the associate editor of several leading IEEE transaction journals.