CMS Special Seminar with Prof. SooJean Han

Uncertainty is unavoidable in the real world, and like humans, autonomous cyberphysical systems must be designed to make reliable decisions in spite of it. A central challenge is bridging the gap between model-based methods, which provide structure, interpretability, and rigorous guarantees, and model-free methods, which offer strong representation-learning capabilities and flexibility in complex systems; both properties are crucial to enabling reliable and adaptive stochastic control. This talk provides a general overview of the work that we have done and are currently doing at ACSS Lab towards this broad goal. We present three representative research thrusts: 1) autonomous decision-making under system and environment uncertainties, and 2) data-efficient learning for adaptive autonomy under uncertainty, and 3) large-scale estimation/control for networked multiagent systems. These directions span applications in motion planning, fault/OOD tolerance, and generative AI-based robotics.

The talk will be held in Guggenheim 232.