UofT Robotics Institute Seminar: Mo Chen on Control, Learning, and Multi-Agent RL скачать в хорошем качестве

UofT Robotics Institute Seminar: Mo Chen on Control, Learning, and Multi-Agent RL

Title: Control, learning, and multi-agent RL 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 this talk, we first present computational advances in control-theoretic safety verification that enable real-time least-restrictive collision avoidance. Then, we examine connections between traditional, analytical control techniques and more recent data-driven, learning-based for controlling robots, and how these connections can improve data efficiency and generalization. Finally, we discuss recent developments in hierarchical multi-agent reinforcement learning. 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 is also a CIFAR AI Chair, Amii Fellow, and NCRN Distal Fellow. Mo completed his PhD in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley in 2017, and received his BASc in Engineering Physics from the University of British Columbia in 2011. From 2017 to 2018, He was a postdoctoral researcher in the Aeronautics and Astronautics Department at Stanford University. Mo’s research interests include optimal control, multi-agent systems, safety-critical systems, reinforcement learning, and human-robot interactions.