Multi-robot systems have many appealing applications such as shape formation, search and rescue, surveillance and reconnaissance, cooperative target tracking, and collective transport. However, even the presence of a few faulty or malicious robots can easily disrupt the overall function and safety of the swarm. In this talk, I will present a novel method for achieving Byzantine resilience in large robot swarms. We consider Byzantine robots which are an unknown subset of robots that are allowed to have arbitrarily different behaviors relative to cooperative robots in terms of physical actions and communication. I will show that by leveraging the cyber-physical characteristics of the robots, we can design a decentralized blocklist protocol (DBP) based on inter-robot accusations to guarantee the removal of Byzantine robots' influence on the swarm. Compared with the state-of-the-art Weighted-Mean Subsequence Reduced (W-MSR) algorithm, DBP can generalize to applications not implemented via the Linear Consensus Protocol, can automatically adapt to the (unknown) number of Byzantine robots, and reduces the connectivity requirement of W-MSR from (2F+1)-connected to (F+1)-connected (where F is the number of Byzantine robots). I will demonstrate that DBP scales to swarms with hundreds of robots across a set of tasks including target tracking, time synchronization, and localization. I will conclude the talk by giving an overview of related research endeavors in my group, particularly those that aim at verifying and enhancing the robustness of A.I. and A.I.-enabled systems, and discuss future directions.
Wenchao Li is an Assistant Professor in the Department of Electrical and Computer Engineering and directs the Dependable Computing Laboratory at Boston University. Prior to joining BU, he was a Computer Scientist at SRI International, Menlo Park. He received his Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 2013. His research sits at the intersection of formal methods and machine learning, with a focus on building safe and trustworthy autonomous systems.