Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 2: Optimization Theory

Lecture 2 — Optimization Theory Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com To learn more about enrolling in AA203 Optimal and Learning-Based Control, visit: https://online.stanford.edu/courses/aa203-optimal-and-learning-based-control Follow along with the course schedule and syllabus: https://stanfordasl.github.io/aa203/sp2526/ Lecture slides: https://stanfordasl.github.io/aa203/sp2526/pdfs/lecture/lecture_2.pdf Speaker: Prof. Marco Pavone Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation. Instructors: Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA Dr. Daniele Gammelli, Research Director at the Italian Institute of Artificial Intelligence (AI4I), Researcher at the Department of Aeronautics and Astronautics, Stanford University Full playlist: https://www.youtube.com/playlist?list=PLa9dmHsLK9dg #StanfordOnline #OptimalControl #Robotics #PhysicalAI