April 9, 2021 – 2:00 pm in Bluejeans: https://bluejeans.com/851024140
Autonomous systems especially those driven under optimal control are usually associated with objective functions to describe their goals/tasks in specific missions. Since they are usually unknown in practice especially for complicated missions, learning such objective functions is significant to autonomous systems especially in their imitation learning and teaming with human. In this talk we will introduce our recent progress in objective learning based on inverse optimal control and inverse optimization, especially their applications in human motion segmentation, learning from sparse demonstrations, and learning with directional corrections. We will also present an end-to-end learning framework based on Pontryagin Principle, feedbacks and optimal control, which is able to treat solving inverse optimization, system identification, and some control/planning tasks as its special modes.
Dr. Shaoshuai Mou is an Assistant 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 co-direct Purdue’s new Center for Innovation in Control, Optimization and Networks (ICON), which consists of more than 50 faculty and 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