About the Seminar

In this talk AI-based methods have tremendous potential for impacting the performance of autonomous aerospace and robotic systems. Such systems include drones, ground- and water-based vehicles, and limbed robots for instance. A barrier to the wide deployment of AI-powered methods in such applications is the risk or unpredictability of algorithm performance. In this presentation we consider the development of safe machine learning (ML) methods for control that provide guarantees about their convergence and performance. Specifically, the presentation will focus on two distinct topics involving the application of learning techniques to analysis of dynamical systems. First: we present an algorithm and a tool for statistical model checking (SMC) of continuous state space Markov chains initialized to a prescribed set of states. We observe that it can be formulated as an X-armed bandit problem, and therefore, can be solved using hierarchical optimistic optimization. Our experiments, using our tool HooVer, suggest that the approach scales to realistic-sized problems and is often more sample-efficient compared to other existing tools. Second: we present recent results on the global convergence of policy gradient methods for quadratic optimal control of discrete-time Markovian jump linear systems (MJLS); switching is a common feature in systems that are comprised of interacting software and physical processes, and MJLS are models in which discrete states evolve according to a finite Markov chain and continuous states evolve according to linear dynamics specified by these discrete states. We study the optimization landscape of direct policy optimization for MJLS. Numerical examples are presented to illustrate the application of this theory. This work brings new insights for understanding the performance of policy gradient methods on the Markovian jump linear quadratic control problem. Also presented will be the HoTDeC multi-vehicle testbed, which consists of indoor airborne and ground-based vehicles.

About the Speaker
Dr. Dr. Geir E. Dullerud is the W. Grafton and Lillian B. Wilkins Professor in Mechanical Engineering at the University of Illinois at Urbana-Champaign. He is the Director of the new Illinois Center for Autonomy. He is also a member of the Coordinated Science Laboratory, and is an Affiliate Professor of both Computer Science, and Electrical and Computer Engineering. He has held visiting positions in Electrical Engineering KTH, Stockholm (2013), and Aeronautics and Astronautics, Stanford University (2005-2006). Earlier he was on faculty in Applied Mathematics at the University of Waterloo (1996-1998), after being a Research Fellow at the California Institute of Technology (1994-1995), in the Control and Dynamical Systems Department. He holds a PhD in Engineering from Cambridge University. He has published two books: “A Course in Robust Control Theory”, Texts in Applied Mathematics, Springer, and “Control of Uncertain Sampled-data Systems”, Birkhauser. His areas of current research interest include autonomy and cooperative robotics, convex optimization in control, cyber-physical system security, stochastic simulation, and hybrid dynamical systems. In 1999 he received the CAREER Award from the National Science Foundation, and in 2005 the Xerox Faculty Research Award at UIUC. In 2018 he was awarded the UIUC Engineering Council Award for Excellence in Advising. He is a Fellow of both IEEE (2008) and ASME (2011). He was the General Chair of the recent IFAC workshop Distributed Estimation and Control in Networked Systems (NECSYS2019).