Learning controllers for machines: Paradigms and recent results
Date: February 14, 2025 @ 3:00-4:00PM | Location: Gates B03 | Speaker: Sebastian Trimpe | Affiliation: RWTH Aachen University
Abstract:
Fast dynamics, nonlinearities, and tedious tuning are just a few of the many reasons why we are interested in leveraging learning for control—challenges that are ubiquitous in robotics and other physical machines. In this talk, we will explore the problem of learning controllers through three paradigms, organized from general to structured learning problems: deep reinforcement learning, automatic imitation learning from optimal control, and auto-tuning via Bayesian optimization. I will highlight some of our recent results addressing key challenges faced in practice, such as enhanced uncertainty quantification for improved data efficiency and reliability in model-based reinforcement learning, as well as parameter-adaptive approximate model predictive control for imitation learning without retraining. By discussing these advancements alongside applications—demonstrated through hardware experiments on unicycle robots, quadcopters, and cars—I aim to develop an understanding of the potential of these paradigms in both research and current practice.
Bio:
Sebastian Trimpe is a Full Professor (W3) at RWTH Aachen University, where he heads the Institute for Data Science in Mechanical Engineering (DSME) since May 2020. Additionally, he is a founding director of the RWTH Center for Artificial Intelligence and, as of 2023, serves as one of its two Executive Directors. Sebastian's research focuses on fundamental questions at the crossroads of machine learning, control, networked systems, and robotics, with innovative applications thereof. Prior to his role at RWTH, he was a Max Planck Research Group Leader (W2) at the Max Planck Institute for Intelligent Systems in Tübingen/Stuttgart. Sebastian earned his Ph.D. degree in 2013 from ETH Zurich, working with Raffaello D'Andrea at the Institute for Dynamic Systems and Control. His academic journey includes a B.Sc. in General Engineering Science (2005), an M.Sc. (Dipl.-Ing.) in Electrical Engineering (2007), and an MBA degree in Technology Management (2007), all from Hamburg University of Technology. Sebastian is recipient of the triennial IFAC World Congress Interactive Paper Prize (2011), the Klaus Tschira Award for achievements in public understanding of science (2014), the Best Paper Award of the International Conference on Cyber-Physical Systems (2019), and the Future Prize by the Ewald Marquardt Stiftung for innovations in control engineering (2020).