Towards Stochastic Fault-Tolerant Control Using Precision Learning and Active Inference

Mohamed Baioumy*, Corrado Pezzato, Carlos Hernández Corbato, Nick Hawes, Riccardo Ferrari

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference. In the majority of existing schemes a binary decision of whether a sensor is healthy (functional) or faulty is made based on measured data. The decision boundary is called a threshold and it is usually deterministic. Following a faulty decision, fault recovery is obtained by excluding the malfunctioning sensor. We propose a stochastic fault-tolerant scheme based on active inference and precision learning which does not require a priori threshold definitions to trigger fault recovery. Instead, the sensor precision, which represents its health status, is learned online in a model-free way allowing the system to gradually, and not abruptly exclude a failing unit. Experiments on a robotic manipulator show promising results and directions for future work are discussed.

Original languageEnglish
Title of host publicationMachine Learning and Principles and Practice of Knowledge Discovery in Databases
Subtitle of host publicationProceedings of the International Workshops of ECML PKDD 2021
EditorsMichael Kamp, Michael Kamp, Irena Koprinska, et. al.
PublisherSpringer
Pages681-691
ISBN (Print)978-3-030-93735-5
DOIs
Publication statusPublished - 2022
Event21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online
Duration: 13 Sep 202117 Sep 2021

Publication series

NameCommunications in Computer and Information Science
Volume1524 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
CityVirtual, Online
Period13/09/2117/09/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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