CISE Seminar: Florian Dörfler, ETH Zürich

Learning Pipelines for Adaptive Control 2.0

The adjacent fields of reinforcement learning (RL) and adaptive control share the same objectives, yet they are separated by a wide cultural gap. In this presentation, I attempt to bridge this gap for the linear quadratic regulator (LQR) problem, which serves as a cornerstone and the benchmark for both fields. I begin by discussing different learning pipelines, including direct and indirect (model-based) approaches, as well as episodic and online (adaptive) approaches. Despite the extensive literature spanning several decades, numerous problems remain unsolved. For instance, RL methods are seldom concerned with closed-loop stability certificates or efficient implementations, while the adaptive control community has dedicated minimal effort to optimality. We address the data-driven LQR problem in an adaptive setting, which entails online recursive algorithms and closed-loop data, and we seek both algorithmic as well as closed-loop certificates. Our approach encompasses different variations of policy gradient methods and employs a novel covariance parameterization of the LQR problem. Finally, all our theoretical results are validated through simulations and experiments in diverse domains, demonstrating the computational and sample efficiency of our method.

Flörian Dörfler Florian Dörfler is a Professor at the Automatic Control Laboratory at ETH Zürich. He received his Ph.D. degree in Mechanical Engineering from the University of California at Santa Barbara in 2013, and a Diplom degree in Engineering Cybernetics from the University of Stuttgart in 2008. From 2013 to 2014 he was an Assistant Professor at the University of California Los Angeles. He has been serving as the Associate Head of the ETH Zürich Department of Information Technology and Electrical Engineering from 2021 until 2022. His research interests are centered around automatic control, system theory, optimization, and learning. His particular foci are on network systems, data-driven settings, and applications to power systems. He is a recipient of the 2025 Rössler Prize, the highest scientific award across all disciplines at ETH Zürich, as well as the distinguished career awards by IFAC (Manfred Thoma Medal 2020) and EUCA (European Control Award 2020). He and his team received best paper distinctions in the top venues of control, machine learning, power systems, power electronics, circuits and systems. They were recipients of the 2011 O. Hugo Schuck Best Paper Award, the 2012-2014 Automatica Best Paper Award, the 2016 IEEE Circuits and Systems Guillemin-Cauer Best Paper Award, the 2022 IEEE Transactions on Power Electronics Prize Paper Award, the 2024 Control Systems Magazine Outstanding Paper Award, and multiple Best PhD thesis awards at UC Santa Barbara and ETH Zürich. They were further winners or finalists for Best Student Paper awards at the European Control Conference (2013, 2019), the American Control Conference (2010,2016,2024), the Conference on Decision and Control (2020), the PES General Meeting (2020), the PES PowerTech Conference (2017,2025), the International Conference on Intelligent Transportation Systems (2021), the IEEE CSS Swiss Chapter Young Author Best Journal Paper Award (2022,2024), the IFAC Conferences on Nonlinear Model Predictive Control (2024) and Cyber-Physical-Human Systems (2024), and NeurIPS Oral (2024). He is currently serving on the council of the European Control Association and as a senior editor of Automatica.

Faculty Hosts: Emiliano Dall'Anese and Christos Cassandras

Student Host: Beste Oztop

When 3:00 pm - 4:00 pm on 13 November 2025
Building 665 Commonwealth Ave., CDS 1101