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 several of our recent efforts that combine 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.
Chuchu Fan is the Wilson assistant professor of Aeronautics and Astronautics at MIT. Before joining MIT in 2020, she was a postdoctoral researcher in the Department of Computing and Mathematical Sciences at the California Institute of Technology. She received her Ph.D. in 2019 from the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. She received her Bachelor's degree from Tsinghua University, Department of Automation in 2013. Her research interests are in the areas of formal methods, machine learning, and control for safe autonomy.