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ESE PhD Seminar: “Learning Local Control Barrier Functions for Safety-Critical Hybrid Systems”
October 24 at 12:00 PM - 1:00 PM
Safety-critical control is one of the fundamental problems in autonomous systems. A special class of autonomous systems is the class of hybrid dynamical systems, which involves both continuous dynamic flow and discrete dynamical mode jumps for state evolution. I will introduce how to synthesize safe controllers for hybrid dynamical systems based on local control barrier functions (CBFs), and such a framework enjoys flexibility, non-conservativeness, and computational advantage compared with existing safety-critical methods. Then, I will show how to learn local CBFs for hybrid systems through self-supervision techniques. Finally, I will briefly share some ideas on learning safe and adaptive controllers in multi-agents systems.
Shuo Yang
ESE Ph.D. Candidate
Shuo Yang is a Ph.D. student at the University of Pennsylvania, where he is advised by Professor George J. Pappas. Shuo is affiliated with the GRASP Lab and PRECISE Center. Previously, he obtained his Bachelor’s degree (Summa Cum Laude) from Shanghai Jiao Tong University in 2021. He has also spent time at Toyota Research and Tencent AI. He is broadly interested in formal methods, machine learning, control theory, and algorithmic game theory, with their applications to robotic and multi-agents systems.