Abstract: Our work is broadly motivated by the emergence of learning-based methods in control theory and robotics, with a specific focus on scenarios that have humans in-the-loop with control systems. For instance, learning algorithms are being deployed in semi-autonomous vehicles, robot assistants, brain-machine interfaces, and exoskeletons, interacting dynamically with a human partner to complete tasks. When learning algorithms are employed in this way, a dynamic game is created that is played between two intelligent agents (the human and machine learners), requiring new techniques to guarantee safety and performance.
We approach this class of problems using tools from control theory and game theory, and conduct human subjects experiments to validate theoretical assumptions and results. This talk will focus on two classes of experiments. In the first, participants perform a reference-tracking task while we apply disturbances and measure feedforward and feedback transformations. In the second, human and machine agents simultaneously adapt to minimize distinct cost functions, and different equilibrium outcomes are obtained based on the machine's learning algorithms. Our results expose limitations on the transformations people learn and level of reasoning they employ. Future work will use these findings to derive learning-based controllers that augment and amplify human ability.

Bio: Sam Burden earned his BS with Honors in Electrical Engineering from the University of Washington in Seattle in 2008. He earned his PhD in Electrical Engineering and Computer Sciences from the University of California in Berkeley in 2014, where he subsequently spent one year as a Postdoctoral Scholar. In 2015, he returned to UW EE (now ECE) as an Assistant Professor, where he received awards for research (Young Investigator Program, Army Research Office, 2016; CAREER, National Science Foundation, M3X program, 2021) and service (Junior Faculty Award, UW College of Engineering, 2021). Sam served as his Department’s Associate Chair for Diversity, Equity, and Inclusion in 2021–2022 and 2024–2025 and was promoted to Associate Professor with tenure in 2022. He is broadly interested in discovering and formalizing principles of sensorimotor control. Specifically, he focuses on applications in robotics, neuroengineering, and (human-)cyber-physical systems. Sam lives with chronic illness, and is happy to meet with anyone who identifies as disabled or chronically ill.