Search Results for author: Friedrich Solowjow

Found 12 papers, 5 papers with code

Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test

no code implementations8 Mar 2023 Behnam Khojasteh, Friedrich Solowjow, Sebastian Trimpe, Katherine J. Kuchenbecker

Machine learning and deep learning have been used extensively to classify physical surfaces through images and time-series contact data.

Benchmarking Time Series Analysis +2

Data-Driven Observability Analysis for Nonlinear Stochastic Systems

1 code implementation23 Feb 2023 Pierre-François Massiani, Mona Buisson-Fenet, Friedrich Solowjow, Florent Di Meglio, Sebastian Trimpe

Establishing these properties is challenging, especially when no analytical model is available and they are to be inferred directly from measurement data.

Improving the Performance of Robust Control through Event-Triggered Learning

1 code implementation28 Jul 2022 Alexander von Rohr, Friedrich Solowjow, Sebastian Trimpe

However, in practice, many systems also exhibit uncertainty in the form of changes over time, e. g., due to weight shifts or wear and tear, leading to decreased performance or instability of the learning-based controller.

Parameter Filter-based Event-triggered Learning

no code implementations5 Apr 2022 Sebastian Schlor, Friedrich Solowjow, Sebastian Trimpe

However, they usually come with a critical assumption - access to an accurate model of the system.

Active Learning

Safe Value Functions

1 code implementation25 May 2021 Pierre-François Massiani, Steve Heim, Friedrich Solowjow, Sebastian Trimpe

Although it is often not possible to compute the minimum required penalty, we reveal clear structure of how the penalty, rewards, discount factor, and dynamics interact.

Structure-preserving Gaussian Process Dynamics

no code implementations2 Feb 2021 Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche, Michael Tiemann, Sebastian Trimpe

On the other hand, classical numerical integrators are specifically designed to preserve these crucial properties through time.

Variational Inference

A Kernel Two-sample Test for Dynamical Systems

no code implementations23 Apr 2020 Friedrich Solowjow, Dominik Baumann, Christian Fiedler, Andreas Jocham, Thomas Seel, Sebastian Trimpe

Evaluating whether data streams are drawn from the same distribution is at the heart of various machine learning problems.

Anomaly Detection Feature Engineering +1

Actively Learning Gaussian Process Dynamics

1 code implementation L4DC 2020 Mona Buisson-Fenet, Friedrich Solowjow, Sebastian Trimpe

Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient way.

Active Learning regression

A Local Information Criterion for Dynamical Systems

no code implementations27 May 2018 Arash Mehrjou, Friedrich Solowjow, Sebastian Trimpe, Bernhard Schölkopf

Apart from its application for encoding a sequence of observations, we propose to use the compression achieved by this encoding as a criterion for model selection.

Model Selection

Event-triggered Learning for Resource-efficient Networked Control

no code implementations5 Mar 2018 Friedrich Solowjow, Dominik Baumann, Jochen Garcke, Sebastian Trimpe

Common event-triggered state estimation (ETSE) algorithms save communication in networked control systems by predicting agents' behavior, and transmitting updates only when the predictions deviate significantly.

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