Date & Time:
Thursday, October 13, 2022; 1:30pm-2:30pm
Modern society has been relying more and more on engineering advance of autonomous systems, ranging from individual systems (such as a robotic arm for manufacturing, a self-driving car, or an autonomous vehicle for planetary exploration) to cooperative systems (such as a human-robot team, swarms of drones, etc). In this talk we will present our most recent progress in developing a fundamental framework for learning and control in autonomous systems. The framework comes from a differentiation of Pontryagin’s Maximum Principle and is able to provide a unified solution to three classes of learning/control tasks, i.e. adaptive autonomy, inverse optimization, and system identification. We will also present applications of this framework into human-autonomy teaming, especially in enabling an autonomous system to take guidance from human operators, which is usually sparse and vague.
Prof. Shaoshuai Mou
Dr. Shaoshuai Mou is an associate professor in the School of Aeronautics and Astronautics at Purdue University. He received a Ph.D. in Electrical Engineering at Yale University in 2014, worked as a postdoc researcher at MIT for a year, and then joined Purdue University as a tenure-track assistant professor in Aug. 2015. His research has been focusing on advancing control theories with recent progress in optimization, networks and learning to address fundamental challenges in autonomous systems, with particular research interests in multi-agent systems, control of autonomous systems, learning and adaptive systems, cybersecurity and resilience. Dr. Mou co-directs Purdue’s research Center for Innovation in Control, Optimization and Networks (ICON) launched in 2020 consisting of more than 70 faculty from 12 departments across Purdue University. Dr. Mou also served as interim co-chair for Purdue University Strategic Initiative: Autonomous and Connected Systems Initiative (ACSI) in Spring/Summer in 2022. For more information: https://engineering.purdue.edu/ICON