3 citations · 4 across the 10 of their papers we have counts for
14 papers
Demystifying Linear Operator Learning for Control Systems
Max Beier, Nicolas Hoischen, Sandra Hirche +1
This paper proposes a structured approach to learning linear operators for control systems from data. We address both structural and learning-theoretic aspects of the problem. To d…
Task-Oriented Active Learning of Residual Dynamics for Model Predictive Path Integral Control
Nobuaki Aoki, Hojin Lee, Stefan Sosnowski +1
Online residual learning can reduce model mismatch in predictive control, but passive data collection may fail to adequately cover states that become important later in the task. T…
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…
Infinitesimal Operator World Models: Finite-Sample Guarantees for 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…
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…
Risk-Aware Trajectory Optimization and Control for an Underwater Suspended Robotic System
Yuki Origane, Nicolas Hoischen, Tzu-Yuan Huang +3
This paper focuses on the trajectory optimization of an underwater suspended robotic system comprising an uncrewed surface vessel (USV) and an uncrewed underwater vehicle (UUV) for…
Koopman-Equivariant Gaussian Processes
Petar Bevanda, Max Beier, Armin Lederer +3
Credible forecasting and representation learning of dynamical systems are of ever-increasing importance for reliable decision-making. To that end, we propose a family of Gaussian p…
Toward Near-Globally Optimal Nonlinear Model Predictive Control via Diffusion Models
Tzu-Yuan Huang, Armin Lederer, Nicolas Hoischen +4
Achieving global optimality in nonlinear model predictive control (NMPC) is challenging due to the non-convex nature of the underlying optimization problem. Since commonly employed…
Kernel-Based Optimal Control: An Infinitesimal Generator Approach
Petar Bevanda, Nicolas Hoischen, Tobias Wittmann +3
This paper presents a novel operator-theoretic approach for optimal control of nonlinear stochastic systems within reproducing kernel Hilbert spaces. Our learning framework leverag…
Reinforcement Learning with Lie Group Orientations for Robotics
Martin Schuck, Jan Brüdigam, Sandra Hirche +1
Handling orientations of robots and objects is a crucial aspect of many applications. Yet, ever so often, there is a lack of mathematical correctness when dealing with orientations…
Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation
Jan Brüdigam, Ali-Adeeb Abbas, Maks Sorokin +7
Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks re…
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…
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…
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…