Uncovering Structure in Human-Robot Systems
Robots and autonomous systems are becoming tangible technologies that will soon impact the human experience. However, the desirable impacts of autonomy are only achievable if the underlying algorithms can handle the unique challenges humans present: People tend to defy expected behaviors and do not conform to many of the standard assumptions made in robotics. To design safe, trustworthy autonomy, we must transform how intelligent systems interact, influence, and predict human agents. In this talk, we'll discuss open challenges in designing systems that work with, around, and for people. We'll explore the notion of structure of tasks in a variety of high impact application domains (e.g., transportation, manufacturing, agriculture) and consider how such structure impacts the system requirements, level of autonomy, and modes of interaction.
Katie Driggs-Campbell is currently an assistant professor at the University of Illinois at Urbana-Champaign in the Department of Electrical and Computer Engineering. Prior to that, she was a Postdoctoral Research Scholar at the Stanford Intelligent Systems Laboratory in the Aeronautics and Astronautics Department. She received a B.S.E. with honors from Arizona State University in 2012 and an M.S. from UC Berkeley in 2015. She earned her PhD in 2017 in Electrical Engineering and Computer Sciences from the University of California, Berkeley. Her lab works on human-centered autonomy, focusing on the integration of autonomy into human-dominated fields, merging ideas robotics, learning, human factors, and control.