3 citations · 3 across the 2 of their papers we have counts for
12 papers
Nonparametric Control Koopman Operators
Petar Bevanda, Bas Driessen, Lucian Cristian Iacob +3
This paper presents a novel Koopman composition operator representation framework for control systems in reproducing kernel Hilbert spaces (RKHSs) that is free of explicit dictiona…
SAD-Flower: Flow Matching for Safe, Admissible, and Dynamically Consistent Planning
Tzu-Yuan Huang, Armin Lederer, Dai-Jie Wu +6
Flow matching (FM) has shown promising results in data-driven planning. However, it inherently lacks formal guarantees for ensuring state and action constraints, whose satisfaction…
On the Existence of Quadratic Control Lyapunov Functions for Koopman-Operator based Bilinear Systems
Sami Leon Noel Aziz Hanna, Nicolas Hoischen, Sandra Hirche +1
Koopman operator-based methods enable data-driven bilinear representations of unknown nonlinear control systems. Accurate representations often demand significantly higher dimensio…
Operator Models for Continuous-Time Offline Reinforcement Learning
Nicolas Hoischen, Petar Bevanda, Max Beier +3
Continuous-time stochastic processes underlie many natural and engineered systems. In healthcare, autonomous driving, and industrial control, direct interaction with the environmen…
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
Nicolas Hoischen, Petar Bevanda, Stefan Sosnowski +2
This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where b…
Information-triggered Learning with Application to Learning-based Predictive Control
Kaikai Zheng, Dawei Shi, Sandra Hirche +1
Learning-based control has attracted significant attention in recent years, especially for plants that are difficult to model based on first-principles. A key issue in learning-bas…