Stanford Seminar - Blending Data-Driven CBF Approximations with HJ Reachability

October 20, 2023 Sylvia Herbert of University of California, San Diego In this talk I will discuss recent joint work with Professor Sicun (Sean) Gao on using data-driven CBF approximations for safe control of autonomous systems. First I will discuss how we blend CBF approximations and HJ reachability for systems with modeled dynamics. The data-driven CBF approximation provides an efficient initial estimate of the true CBF, which is then refined using HJ reachability analysis. This work was presented at IROS 2022, with some new additions. Next I will discuss our recent work on how we use data-driven CBFs for hard-to-model dynamics (e.g. interaction behavior among pedestrians). Our approach exploits an important observation: the spatial interaction patterns of multiple dynamic obstacles can be decomposed and predicted through temporal sequences of states for each obstacle. Through decomposition, we can generalize control policies trained only with a small number of obstacles, to environments where the obstacle density can be 100x higher. We have no guarantees on safety (at least so far), but we empirically show significant improvements to dynamic collision avoidance (compared to other learning methods) without being overly conservative (compared to control theoretic methods). This work won the Robocup best paper award this month at IROS 2023. About the speaker: https://sylviaherbert.com/ ► Check out our entire catalog of courses and programs: https://online.stanford.edu/explore