Resilient Safety Assurance for Robotic Systems: Staying Safe Even When Models Are Wrong
In this talk I will present recent contributions to safety assurance for autonomous systems. I will first discuss new advances in efficient safety computation, and demonstrate their use in large-scale unmanned air traffic. Next, I will present a general safety framework enabling the use of learning control schemes (e.g. reinforcement learning) for safety-critical robotic systems in uncertain environments. I will then turn my attention to the important problem of safe human-robot interaction, and introduce a real-time Bayesian method to monitor the reliability of predictive human models. The talk will end with a discussion of challenges and opportunities ahead, including the introduction of game-theoretic planning in autonomous driving and the bridging of safety analysis and deep reinforcement learning.
Bio:
Jaime Fernández Fisac is a Ph.D. candidate in Electrical Engineering and Computer Sciences at UC Berkeley. His research interests lie in control theory and artificial intelligence, with a focus on safety assurance for autonomous systems. He works to enable robotic systems to safely interact with uncertain environments and human beings despite using inaccurate models. Jaime received a B.S./M.S. degree in Electrical Engineering from the Universidad Politécnica de Madrid, Spain, in 2012, and a M.Sc. in Aeronautics from Cranfield University, U.K., in 2013. He is a recipient of the La Caixa Foundation fellowship.