Oldenburger Lecture

Naomi Ehrich Leonard
Princeton University

Abstract: When multiple agents face decision-making problems with factors that put them in competition, such as congestion limitations, shared resources, or coordination constraints, actions are not isolated alternatives. For example, actions that share downstream paths or bottlenecks may have coupled payoffs. Existing approaches to multiagent control typically rely on well-constructed objective functions and constraints. Relational structure among actions can be represented in the objective function if they are known in advance. However, these approaches leave no room for real-time reasoning about information inferred from hard-to-anticipate, evolving interactions, and thus can fall short in meeting performance goals. 

I will present new ideas based on an internal nonlinear state-space process that address open challenges in scalable, distributed multiagent control in contested environments. By modeling the decision-making process as a neuromorphic dynamical system, agents can reason and react in real-time to available cues and quickly adapt to changing contexts, even when sensing, communication, and compute are limited. Our approach also encodes a mechanism for resolving near-ties in decision-making and ensuring that commitments persist under transient perturbation. I will illustrate with two multirobot problems: congestion-free persistent environmental monitoring and safe, deadlock-free social navigation in crowded environments.

Bio: Naomi Ehrich Leonard is Chair and Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering, associated faculty with the Program in Applied and Computational Mathematics and the Biophysics Graduate Program, and affiliated faculty with the Princeton Neuroscience Institute at Princeton University. She is Founding Editor of Annual Review of Control, Robotics, and Autonomous Systems, and Founding Director of creativeX, a Princeton engineering-and-the-arts collective. Leonard received her B.S.E. in Mechanical Engineering from Princeton University and her Ph.D. in Electrical Engineering from the University of Maryland. She is a MacArthur Fellow, member of the American Academy of Arts and Sciences, Fellow of the ASME, IEEE, IFAC, and SIAM, and recipient of the 2023 IEEE Control Systems Award, 2024 Richard E. Bellman Control Heritage Award, 2025 IEEE George S. Axelby Outstanding Paper Award, and 2026 ASME Rufus Oldenburger Medal. Her current research focuses on dynamics, control, and learning for multiagent systems with application to robotics, collective behavior, and other networked systems in technology, nature, and the arts.