Talk 9

Abstract: The operational environment of autonomous robots is becoming more and more complex, with a lot of uncertainties, high dynamics, and potential adversaries. Such environment may lead to situations which are beyond the pre-programmed autonomy of robots. Human’s guidance and inputs are usually valuable to assist autonomous robots to complete tasks in such scenarios. In this talk, we will briefly discuss our recent progress on how to incorporate huamn’s inputs into the onboard planning and navigation of autonomous robots and these inputs are usually sparse, and vague.

Bio: Dr. Shaoshuai Mou is an Associate Professor in the School of Aeronautics and Astronautics at Purdue University. Before joining Purdue, he received a Ph.D. in Electrical Engineering at Yale University in 2014 and worked as a postdoc researcher at MIT for a year after that. His research interests include multi-agent autonomy and learning, distributed algorithms for control and optimization, human-machine teaming, resilience & cybersecurity, and also experimental research involving autonomous air and ground vehicles. Dr. Mou codirect Purdue’s new Center for Innovation in Control, Optimization and Networks (ICON), which aims to integrate classical theories in control/optimization/networks with recent advance in machine learning/AI/data science to address fundamental challenges in autonomous and connected systems. For more information, please refer to https://engineering.purdue.edu/ICON.