Unity Hall

Neural Certificates for Safe Robotic System Planning and Control Abstract: Achieving safety, scalability, and high performance in complex systems, such as multi-agent systems (MAS) control, is a central challenge in many real-world robotic deployments due to its computational complexity as a large-scale constrained optimal control problem. To address this, we introduce a novel graph control barrier function (GCBF) as a core tool for large-scale distributed safe control, which guarantees safety for arbitrarily large MAS with only local observations. For MAS with known dynamic models, we present a self-supervised learning framework that can jointly learn GCBF and distributed control policies that consider actuation limits. For MAS with unknown dynamics, we discuss how to blend GCBF in multi-agent reinforcement learning (MARL) to achieve high-performance and safe distributed policies. Bio: Chuchu Fan is an Associate Professor (pre-tenure) in the Department of Aeronautics and Astronautics (AeroAstro) and Laboratory for Information and Decision Systems (LIDS) at MIT. Before that, she was a postdoc researcher at Caltech and got her Ph.D. at the University of Illinois at Urbana-Champaign. She earned her bachelor’s degree from Tsinghua University. Her research group, the Realm at MIT, works on developing computational tools that integrate rigorous mathematics into machine learning and AI for the design, analysis, and verification of safe, large-scale, and complex systems. Chuchu is the recipient of an NSF CAREER Award, an AFOSR Young Investigator Program (YIP) Award, an ONR YIP Award, and the 2020 ACM Doctoral Dissertation Award.