Safe Learning-Enabled Robot Control

Title: Safe Learning-Enabled Robot Control

Abstract: No other technology has probably impacted robotics in the last decade as much as machine learning and AI. However, integrating data-driven models into the control loop introduces a critical challenge: how can we ensure the safety of learning-enabled robotic systems?

In this talk, we present a systematic approach to embedding safety guarantees throughout the entire lifecycle of learning in robotics – from training to deployment to real-time adaptation. First, we discuss physics-informed machine learning techniques that efficiently learn safe control policies for a wide range of autonomous systems. Next, we introduce fast adaptation methods that enable robots to refine safety policies on the fly, leveraging raw sensory information and language-based feedback. Finally, we discuss how these safety-aware techniques can enhance data-driven approaches, such as imitation learning and sampling-based MPC, improving both their data efficiency and robustness. Throughout the talk, we will demonstrate these methods on various safety-critical autonomous systems, including autonomous aircrafts, legged robots, and drones.

Bio: Somil Bansal is an assistant professor in the Department of Aeronautics and Astronautics at Stanford University. Before joining Stanford, he was an assistant professor in the ECE department at the University of Southern California. He received a Ph.D. in Electrical Engineering and Computer Sciences (EECS) from the University of California at Berkeley in 2020. Before that, he obtained a B.Tech. in Electrical Engineering from IIT Kanpur, and an M.S. in EECS from UC Berkeley in 2012 and 2014, respectively. After his PhD, he spent a year as a Research Scientist at Waymo (formerly known as the Google Self-Driving Car project). He has also collaborated closely with companies like NVIDIA, Skydio, Google, Boeing, as well as NASA JPL. Somil is broadly interested in developing mathematical tools and algorithms for the control and analysis of safety-critical autonomous systems. Somil has received several awards, most notably the NSF CAREER award, the Eli Jury Award for outstanding PhD dissertation, the RSS Pioneer Award, and the Outstanding Graduate Student Instructor Award.