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7 results (View BibTeX file of all listed publications)

2019

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Actively Learning Gaussian Process Dynamics

Buisson-Fenet, M., Solowjow, F., Trimpe, S.

2019 (techreport) Submitted

Abstract
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. We propose active learning strategies that leverage information-theoretical properties arising naturally during Gaussian process regression, while respecting constraints on the sampling process imposed by the system dynamics. Sample points are selected in regions with high uncertainty, leading to exploratory behavior and data-efficient training of the model. All results are verified in an extensive numerical benchmark.

ics

ArXiv [BibTex]

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Event-triggered Learning

Solowjow, F., Trimpe, S.

2019 (techreport) Submitted

ics

arXiv PDF [BibTex]

2016

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Supplemental material for ’Communication Rate Analysis for Event-based State Estimation’

Ebner, S., Trimpe, S.

Max Planck Institute for Intelligent Systems, January 2016 (techreport)

am ics

PDF [BibTex]

2016

am ics Ebner, S., Trimpe, S. Supplemental material for ’Communication Rate Analysis for Event-based State Estimation’ Max Planck Institute for Intelligent Systems, January 2016 (techreport)

PDF [BibTex]

2015

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Distributed Event-based State Estimation

Trimpe, S.

Max Planck Institute for Intelligent Systems, November 2015 (techreport)

Abstract
An event-based state estimation approach for reducing communication in a networked control system is proposed. Multiple distributed sensor-actuator-agents observe a dynamic process and sporadically exchange their measurements and inputs over a bus network. Based on these data, each agent estimates the full state of the dynamic system, which may exhibit arbitrary inter-agent couplings. Local event-based protocols ensure that data is transmitted only when necessary to meet a desired estimation accuracy. This event-based scheme is shown to mimic a centralized Luenberger observer design up to guaranteed bounds, and stability is proven in the sense of bounded estimation errors for bounded disturbances. The stability result extends to the distributed control system that results when the local state estimates are used for distributed feedback control. Simulation results highlight the benefit of the event-based approach over classical periodic ones in reducing communication requirements.

am ics

arXiv [BibTex]

2015

am ics Trimpe, S. Distributed Event-based State Estimation Max Planck Institute for Intelligent Systems, November 2015 (techreport)

arXiv [BibTex]

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Lernende Roboter

Trimpe, S.

In Jahrbuch der Max-Planck-Gesellschaft, Max Planck Society, May 2015, (popular science article in German) (inbook)

am ics

link (url) [BibTex]

am ics Trimpe, S. Lernende Roboter In Jahrbuch der Max-Planck-Gesellschaft, Max Planck Society, May 2015, (popular science article in German) (inbook)

link (url) [BibTex]

2013

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Animating Samples from Gaussian Distributions

Hennig, P.

(8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

ei pn

PDF [BibTex]

2013

ei pn Hennig, P. Animating Samples from Gaussian Distributions (8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

PDF [BibTex]