14 papers
Uncertainty-Aware Fusion for Resilient Distributed Radar Sensing
Christian Eckrich, Maik Pfefferkorn, Rolf Findeisen +2
Distributed radar sensing enables safe and resilient operation in mobile robotic and vehicular systems by combining multiple local views of the scene. In this paper, we consider a…
Real-time Gaussian Process based Approximate Model Predictive Trajectory Tracking Control for Autonomous Vehicles
Alexander Rose, Lukas Theiner, Rolf Findeisen
Applying model predictive control on embedded systems remains challenging due to the high computational cost of solving optimal control problems. To address this limitation, comput…
Bridging Data-Driven Reachability Analysis and Statistical Estimation via Constrained Matrix Convex Generators
Peng Xie, Zhen Zhang, Rolf Findeisen +1
Data-driven reachability analysis enables safety verification when first-principles models are unavailable. This requires constructing sets of system models consistent with measure…
Data-Driven Reachability Analysis with Optimal Input Design
Peng Xie, Davide M. Raimondo, Rolf Findeisen +1
This paper addresses the conservatism in data-driven reachability analysis for discrete-time linear systems subject to bounded process noise, where the system matrices are unknown…
Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models
Lukas Theiner, Maik Pfefferkorn, Yongpeng Zhao +2
Tuning control policies manually to meet high-level objectives is often time-consuming. Bayesian optimization provides a data-efficient framework for automating this process using…
High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Control
Sebastian Hirt, Valentinus Suwanto, Hendrik Alsmeier +2
Learning controller parameters from closed-loop data has been shown to improve closed-loop performance. Bayesian optimization, a widely used black-box and sample-efficient learning…