Title: Interpretable Reinforcement Learning through Control 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 this talk, we will examine connections between traditional control and more recent data-driven, learning-based methods for controlling robots; in particular, we will investigate how learned linear system representations can help enable more interpretable reinforcement learning. We will also briefly discuss human pose and trajectory prediction and its applications in human-robot interactions.
Speaker 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 holds a CIFAR AI Chair and is an Amii 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 in Stanford University. Mo’s research interests include multi-agent systems, safety-critical systems, human-robot interactions, control theory, and reinforcement learning.
Date: Monday, September 18th, 2023
This seminar is virtual.