16 citations · 17 across the 25 of their papers we have counts for
10 papers · 1 filter
Shaping Wind-Tunnel Airflow for Unmanned Aerial Vehicles using Online Learning
Ghadeer Elmkaiel, Michael Muehlebach
The development and testing of advanced aerial robots require experiments in controlled environments with tailored airflow profiles. This paper presents an online learning algorith…
Foundations of Reinforcement Learning and Control:Connections and New Perspectives
Claire Vernade, Onno Eberhard, Martha White +4
Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields…
Learning Dynamic Swing-Up of an Inverted Pendulum using Remote Magnetic Actuation
Viacheslav Sydora, Jasan Zughaibi, Denis von Arx +2
Electromagnetic Navigation Systems (eMNS) have gained considerable attention for minimally invasive surgery and targeted drug delivery. While most of the literature relies on quasi…
Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing
Kijung Jeon, Michael Muehlebach, Molei Tao
Generative modeling within constrained sets is essential for scientific and engineering applications involving physical, geometric, or safety requirements (e.g., molecular generati…
Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning
Yike Zhao, Onno Eberhard, Malek Khammassi +2
The family of linear recurrent neural networks has shown strong performance as recurrent memory units in partially observable reinforcement learning. We provide a theoretical justi…
Adaptive Inverted-Index Routing for Granular Mixtures-of-Experts
Klaus-Rudolf Kladny, Maximilian Mordig, Bernhard Schölkopf +1
Mixture-of-experts (MoE) models enable scalable transformer architectures by activating only a subset of experts per token. Recent evidence suggests that performance improves with…