4 citations · 6 across the 10 of their papers we have counts for
11 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,…
SOCC-ICP: Semantics-Assisted Odometry based on Occupancy Grids and ICP
Johannes Scherer, Sebastian Hirt, Henri Meeß
Reliable pose estimation in previously unseen environments is a fundamental capability of autonomous systems. Existing LiDAR odometry methods typically employ point-, surfel-, or N…
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…
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…
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…
Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging
Sebastian Hirt, Andreas Höhl, Johannes Pohlodek +4
Model predictive control (MPC) is a powerful tool for controlling complex nonlinear systems under constraints, but often struggles with model uncertainties and the design of suitab…
Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Control
Sebastian Hirt, Maik Pfefferkorn, Rolf Findeisen
Safe learning of control policies remains challenging, both in optimal control and reinforcement learning. In this article, we consider safe learning of parametrized predictive con…
Stability-informed Bayesian Optimization for MPC Cost Function Learning
Sebastian Hirt, Maik Pfefferkorn, Ali Mesbah +1
Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predicti…
Learning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Charging
Sebastian Hirt, Andreas Höhl, Joachim Schaeffer +3
Tuning parameters in model predictive control (MPC) presents significant challenges, particularly when there is a notable discrepancy between the controller's predictions and the a…