The Dartmouth Autonomy Seminar Series

"From Generative Models to Control: Representation-based reinforcement learning in physical systems"

The explosive growth of machine learning and data-driven methodologies has revolutionized numerous fields. Yet, translating these successes to dynamical physical systems remains a significant challenge, hindered by the complexity, uncertainty, and safety-critical nature of such environments. In this talk, Li will present a unified framework that bridges this gap by introducing novel generative representations for reinforcement learning and control. She further demonstrates how these representations help close the sim-to-real gap, improve data efficiency in imitation learning, and enable scalable computation of localized policies for large-scale nonlinear networked systems, with applications including robotics and energy systems.

Light refreshments served.

Sponsored by the Neukom Institute and Thayer School of Engineering.

About the Speaker(s)

Na (Lina) Li
Winokur Family Professor of Electrical Engineering and Applied Mathematics, Harvard University

Contact

For more information, contact Harini Barath at harini.barath@dartmouth.edu .