Bio: Guanya Shi received a B.E. in mechanical engineering (summa cum laude) from Tsinghua University in 2017. He is currently working toward a Ph.D. degree in computing and mathematical sciences at the California Institute of Technology. He was a deep learning research intern at NVIDIA in 2020. His research interests are centered around the intersection of machine learning and control theory, spanning the entire spectrum from theory and foundation, algorithm design, to solve cutting-edge problems and demonstrate new capabilities in robotics and autonomy. Guanya was the recipient of several awards, including the Simoudis Discovery Prize and the WAIC Yunfan Award.
Talk Title: Safety-Critical Learning and Control in Dynamic Environments: Towards Unified Theory and Learned Robotic Agility
Talk Abstract: Deep-learning-based methods have made exciting progress in many decision-making problems such as playing complicated strategy games. However, for complex real-world settings, such as agile robotic control in hazardous or poorly-sensed environments (e.g., autonomous driving), end-to-end deep-learning-based methods are often unreliable. In this talk, I will first present the Neural-Control Family, which is a family of nonlinear deep-learning-based control methods with stability, safety, and robustness guarantees. The Neural-Control Family bridges learning and control theory in a unified framework, and demonstrates new capabilities in agile robot control (e.g., agile flight maneuvers in unknown strong wind conditions). In the second part, I will discuss progress towards establishing clean interfaces that fundamentally connect learning and control. A strong focus will be on non-asymptotic analysis for online learning and control. In particular, we will discuss the intersection of representation learning and adaptive control, no-regret and competitive control, and safe exploration in dynamical systems.