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Center for Autonomy
Enabling high-impact research in autonomous system design
About the Center for Autonomy
Located at the University of Illinois at Urbana-Champaign, the Center for Autonomy will enable high-impact research and develop new educational programs for students and professionals. The Center will play an important role in designing innovative systems that can function autonomously, or without human intervention, in a safe and reliable way.
From self-driving cars to intelligent robotic assistants to remote surgical systems, autonomous technology will revolutionize the way we live, work, and play. In order to enable this revolution, however, advancements in foundational research and workforce development must first take place to provide assured and certified-safe performance.
Application areas include:
UPCOMING SEMINAR
On Human-Machine Interaction Games
Friday, April 24, 2026
2:00pm Central Time
Talks are held virtually through Zoom
Speaker: Sam Burden (University of Washington)
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, where they interact 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.