258 citations · 1.2k across the 64 of their papers we have counts for
11 papers · 1 filter
Event-Triggered Safe Bayesian Optimization on Quadcopters
Antonia Holzapfel, Paul Brunzema, Sebastian Trimpe
Bayesian optimization (BO) has proven to be a powerful tool for automatically tuning control parameters without requiring knowledge of the underlying system dynamics. Safe BO metho…
Mean field limits for discrete-time dynamical systems via kernel mean embeddings
Christian Fiedler, Michael Herty, Sebastian Trimpe
Mean field limits are an important tool in the context of large-scale dynamical systems, in particular, when studying multiagent and interacting particle systems. While the continu…
Learning Hybrid Dynamics Models With Simulator-Informed Latent States
Katharina Ensinger, Sebastian Ziesche, Sebastian Trimpe
Dynamics model learning deals with the task of inferring unknown dynamics from measurement data and predicting the future behavior of the system. A typical approach to address this…
Exact Inference for Continuous-Time Gaussian Process Dynamics
Katharina Ensinger, Nicholas Tagliapietra, Sebastian Ziesche +1
Physical systems can often be described via a continuous-time dynamical system. In practice, the true system is often unknown and has to be learned from measurement data. Since dat…
Scale-Preserving Automatic Concept Extraction (SPACE)
Andrés Felipe Posada-Moreno, Lukas Kreisköther, Tassilo Glander +1
Convolutional Neural Networks (CNN) have become a common choice for industrial quality control, as well as other critical applications in the Industry 4.0. When these CNNs behave i…
Scalable Concept Extraction in Industry 4.0
Andrés Felipe Posada-Moreno, Kai Müller, Florian Brillowski +3
The industry 4.0 is leveraging digital technologies and machine learning techniques to connect and optimize manufacturing processes. Central to this idea is the ability to transfor…
Robust Direct Data-Driven Control for Probabilistic Systems
Alexander von Rohr, Dmitrii Likhachev, Sebastian Trimpe
We propose a data-driven control method for systems with aleatoric uncertainty, for example, robot fleets with variations between agents. Our method leverages shared trajectory dat…
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control
Lukas Kesper, Sebastian Trimpe, Dominik Baumann
Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches requ…
Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test
Behnam Khojasteh, Friedrich Solowjow, Sebastian Trimpe +1
Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data. However, these methods rely on human expert…
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch +2
Modeling an unknown dynamical system is crucial in order to predict the future behavior of the system. A standard approach is training recurrent models on measurement data. While t…
Data-Driven Observability Analysis for Nonlinear Stochastic Systems
Pierre-François Massiani, Mona Buisson-Fenet, Friedrich Solowjow +2
Distinguishability and, by extension, observability are key properties of dynamical systems. Establishing these properties is challenging, especially when no analytical model is av…