Mechanical and Civil Engineering Seminar

Title: " Reasoning Models for Physical AI"

Abstract: Reasoning models for Physical AI aim to enable embodied agents—such as robots and autonomous vehicles—to perceive, understand, and act in the real world through contextual and causal reasoning. In this talk, I'll provide an overview of our recent research in reasoning-centric AI for physical systems, highlighting the emergence of chain-of-thought reasoning in autonomous driving models such as NVIDIA's Alpamayo family, which bridges human-like reasoning with trajectory planning to improve handling of complex scenarios. I will discuss model design principles, training strategies that promote causal reasoning, tools and datasets for development and evaluation, and open challenges at the intersection of physical reasoning, safety, and real-world deployment.

Bio:

Dr. Marco Pavone is a Professor of Aeronautics and Astronautics at Stanford University, where he directs the Autonomous Systems Laboratory. He also leads autonomous systems and Physical AI research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA's Jet Propulsion Laboratory. He received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on Physical AI—the development of intelligent systems grounded in physics, perception, and control that can operate safely and robustly in the real world. His work spans a broad range of applications, including self-driving vehicles, autonomous aerospace systems, and general-purpose robots. He has received numerous honors for his research and contributions to the field, including the Presidential Early Career Award for Scientists and Engineers (PECASE) from the White House.