Control Barrier Functions for X
Abstract: Safety is central to autonomous systems and robots since a single failure could lead to catastrophic results. In unstructured complex environments where system states and environment information are not available, the safety-critical control problem is much more challenging. In this talk, I will first discuss safety from a control theoretic perspective with Control Barrier Functions (CBFs). CBFs capture the evolution of the safety requirements during the execution of a control system and can be used to guarantee safety for all times due to their forward invariance, and this method has been shown to be very effective and efficient for nonlinear systems and constraints. Then I will discuss how to apply CBFs to different areas, such as control systems, robotics (autonomous ground vehicles, surface vessels, and flight vehicles, legged robots, robot swarms, soft robots, and manipulators), and machine learning (sequential and generative AI models), both in simulation and in real world experiments.
Bio: Wei Xiao is currently an assistant professor at the WPI robotics engineering department. He was a postdoctoral associate at the Computer Science and Artificial Intelligence lab (CSAIL), Massachusetts Institute of Technology. Before that, he received his Ph.D. degree from the Boston University, Brookline, MA, USA in 2021. His research interests include safety-critical control theory and trustworthy machine learning, with a particular emphasis on robotics and multi-agent systems. He received an Outstanding Dissertation Award at Boston University, an Outstanding Student Paper Award at the 2020 IEEE Conference on Decision and Control, and a Best Paper Nomination at ACM/IEEE ICCPS 2021.