AI Seminar Series: Mo Chen, Optimal Control and Machine Learning in Robotics (Jan 8)

Mo Chen presents "Optimal Control and Machine Learning in Robotics" at the AI Seminar (January 8, 2021).

The Artificial Intelligence (AI) Seminar is a weekly meeting at the University of Alberta where researchers interested in AI can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems, are explored.

Bio:
Mo Chen is an Assistant Professor in the School of Computing Science at Simon Fraser University, Burnaby, BC, Canada, where he directs the Multi-Agent Robotic Systems Lab. He completed his PhD in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley with Claire Tomlin in 2017, and received his BASc in Engineering Physics from the University of British Columbia in 2011. From 2017 to 2018, Mo was a postdoctoral researcher in the Aeronautics and Astronautics Department at Stanford University with Marco Pavone. His research interests include multi-agent systems, safety-critical systems, reinforcement learning, and human-robot interactions.

Abstract:
Autonomous mobile robots are becoming pervasive in everyday life, and hybrid approaches that merge traditional control theory and modern data-driven methods are becoming increasingly important. In the first half of seminar, we begin with a discussion of safety verification methods, and their computational and practical challenges. In the second half, we examine connections between optimal control and reinforcement learning, and between optimal control and visual navigation.