7 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…
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…
A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Control
Sebastian Hirt, Lukas Theiner, Maik Pfefferkorn +1
Many control problems require repeated tuning and adaptation of controllers across distinct closed-loop tasks, where data efficiency and adaptability are critical. We propose a hie…
Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
Yongpeng Zhao, Maik Pfefferkorn, Maximilian Templer +1
Parameter tuning for vehicle controllers remains a costly and time-intensive challenge in automotive development. Traditional approaches rely on extensive real-world testing, makin…
Probabilistically Input-to-State Stable Stochastic Model Predictive Control
Maik Pfefferkorn, Rolf Findeisen
Employing model predictive control to systems with unbounded, stochastic disturbances poses the challenge of guaranteeing safety, i.e., repeated feasibility and stability of the cl…