Scaling Safety Analysis for Robotics: High-Dimensional Systems to Real-Time Computation

April 22, 2022, zoom / ESB 2001

Somil Bansal

USC, Electrical and Computer Engineering


Hamilton-Jacobi Reachability is a powerful safety analysis tool for robotic systems. However, it is challenging to scale the reachability analysis to the scenarios where the underlying autonomous system is high-dimensional, multiple agents are operating in the same environment, or when a real-time update of the safe set is required, all of which are commonly occurring scenarios in robotics. We present new methods for computing the reachable set, based on a functional approximation that has the potential to broadly alleviate its computational complexity. In the second part of the talk, we will present a toolbox of methods that can leverage previously computed solutions to update the safety guarantees online within a fraction of milliseconds, as new environment information is obtained. We will illustrate these methods on various robotic platforms, including demonstrations of motion planning around people, and navigating in a priori unknown environments.

Speaker's Bio

Somil Bansal is an Assistant Professor at the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Southern California, Los Angeles. 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 the Indian Institute of Technology, Kanpur, and an M.S. in Electrical Engineering and Computer Sciences from UC Berkeley in 2012 and 2014, respectively. Between August 2020 and August 2021, 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 Skydio, Google, Waymo, Boeing, as well as NASA Ames. Somil is broadly interested in developing mathematical tools and algorithms for the control and analysis of autonomous systems, with a focus on bridging learning and control-theoretic approaches for safety-critical autonomous systems. Somil has received several awards, most notably the Eli Jury Award at UC Berkeley for his doctoral research, the outstanding graduate student instructor award at UC Berkeley, and the academic excellence award at IIT Kanpur.