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Title: Building Dependable Autonomous Systems through Learning Certified Decisions and Control
Abstract: The introduction of machine learning (ML) and artificial intelligence (AI) creates unprecedented opportunities for achieving full autonomy. However, learning-based methods in building autonomous systems can be extremely brittle in practice and are not designed to be verifiable. In this talk, I will present our recent progress on combining ML with formal methods and control theory to enable the design of provably dependable and safe autonomous systems. I will introduce our techniques to generate safety certificates and certified decision and control for complex autonomous systems, even when the systems have multiple agents, follow nonlinear and nonholonomic dynamics, and need to satisfy high-level specifications.