4 citations · 5 across the 6 of their papers we have counts for
7 papers
Exploiting Hierarchical Controller Structure in Contextual Parameter Learning for Humanoid Loco-Manipulation
Sebastian Hirt, Lukas Theiner, Jan Peters +1
Hierarchical control architectures are widely used to decompose complex control problems into interacting control levels and are particularly important in robotics, where planning,…
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…
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…
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…
Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification
Hendrik Alsmeier, Lukas Theiner, Anton Savchenko +2
This paper presents a framework for bounding the approximation error in imitation model predictive controllers utilizing neural networks. Leveraging the Lipschitz properties of the…
Time-Series-Informed Closed-loop Learning for Sequential Decision Making and Control
Sebastian Hirt, Lukas Theiner, Rolf Findeisen
Closed-loop performance of sequential decision making algorithms, such as model predictive control, depends strongly on the choice of controller parameters. Bayesian optimization a…