AEM Seminar: TODAY, October 31st - Prof Shaoshuai Mou, School of Aeronautics and Astronautics, Purdue University

[Molly Schmitz]

University of Minnesota

Aerospace Engineering and Mechanics
Fall 2025 Seminar Series

Friday, October 31, 2025

209 Akerman Hall

2:30pm-4:30pm

AEM Seminar: 

Control and Learning for Autonomous Systems

Abstract: 

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 seminar we will discuss our recent research in integration of optimization, networks and learning to address fundamental challenges in enabling autonomous systems to be optimal, adaptive, cooperative and swarming.  Especially we will discuss 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-robot teaming, especially in enabling an autonomous system to take guidance from human operators, which is usually sparse and vague. In addition, we will briefly introduce our recent progress in autonomy in space.

Biography:

Shaoshuai Mou is the Elmer Bruhn 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, and then worked as a postdoc researcher at MIT for a year. He joined Purdue University as a tenure-track assistant professor in 2015, and was promoted to be Associate Professor with Tenure in 2021. His research group Autonomous & Intelligent Multi-agent Systems (AIMS) lab has been focusing on advancing control theory with recent progress in optimization, networks and machine learning for autonomous and robotics systems, with particular research interest in inverse optimal control for learning-from-demonstrations in robotics, parameter adaptation in optimal control, integration of control with learning, human-
robot teaming, and distributed algorithms for control and optimization in multi-agent systems. Mou co-directs Purdue’s Institute for Control, Optimization and Networks (ICON) , consisting of more than 100 faculty members from more than 15 departments across Purdue University, which aims to provide a research and education platform for control of autonomous and robotics systems.

*Refreshments to follow in 209 Akerman Hall 

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Molly Schmitz (She/Her/Hers)

Principal Accountant, Purchasing & Payroll Specialist, Graduate Program Coordinator

Department of Aerospace Engineering & Mechanics, University of Minnesota - Twin Cities