In this talk, we present recent progress towards developing learning-based control strategies for the design of safe and robust autonomous systems. Our approach is to recognize that machine learning algorithms produce inherently uncertain estimates or predictions, and that this uncertainty must be explicitly quantified (e.g., using non-asymptotic guarantees of contemporary high-dimensional statistics) and accounted for (e.g., using robust control/optimization) when designing safety critical systems. We focus on the optimal control of unknown systems, and show that by integrating modern tools from high-dimensional statistics and robust control, we can provide, to the best of our knowledge, the first end-to-end finite data robustness, safety, and performance guarantees for learning and control. We also briefly highlight how these ideas can be extended to the large-scale distributed setting by similarly integrating tools from structured linear inverse problems with tools from distributed robust and optimal control. As a whole, these results provide a rigorous and contemporary perspective on safe reinforcement learning as applied to continuous control. We conclude with our vision for a general theory of safe learning and control, with the ultimate goal being the design of robust and high performing data-driven autonomous systems.
Nikolai Matni is an assistant professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania, where he is also a member of the GRASP Lab, PRECISE Center, and Applied Mathematics and Computational Science graduate group. Prior to joining Penn, Nikolai was a postdoctoral scholar in EECS at UC Berkeley. He has also held a position as a postdoctoral scholar in the Computing and Mathematical Sciences at Caltech. He received his Ph.D. in Control and Dynamical Systems from Caltech in June 2016. He also holds B.A.Sc. and M.A.Sc. in Electrical Engineering from the University of British Columbia, Vancouver, Canada. His research interests broadly encompass the use of learning, optimization, and control in the design and analysis of safety-critical and data-driven cyber-physical systems. Nikolai was awarded the IEEE CDC 2013 Best Student Paper Award (first ever sole author winner) and the IEEE ACC 2017 Best Student Paper Award (as co-advisor).