Stanford AA289/ENGR319 - Robotics and Autonomous Systems Seminar - Stanford Seminar - Control-Oriented Learning for Dynamical Systems

June 2, 2023 Spencer M. Richards of Stanford University Robots are inherently nonlinear dynamical systems, for which synthesizing a stabilizing feedback controller with a known system model is already a difficult task. When learning a nonlinear model and controller from data, naive regression can produce a closed-loop model that is poorly conditioned for stable operation over long time horizons. In this talk, I will present our work on control-oriented learning, wherein the model learning problem is augmented to be cognizant of the desire for a stable closed-loop system. I will discuss how principles from control theory inform such augmentation to produce performant closed-loop models in a data efficient manner. This will involve ideas from contraction theory, constrained optimization, structured learning, adaptive control, and meta-learning. Learn more about Spencer: https://stanfordasl.github.io//people/spencer-richards/