4 citations · 6 across the 10 of their papers we have counts for
3 papers · 1 filter
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…
Exploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Styles
Lukas Theiner, Sebastian Hirt, Alexander Steinke +1
Trajectory planning for automated vehicles commonly employs optimization over a moving horizon - Model Predictive Control - where the cost function critically influences the result…