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Authors: Lars Carøe Sørensen ; Jacob Pørksen Buch ; Henrik Gordon Petersen and Dirk Kraft

Affiliation: University of Southern Denmark, Denmark

ISBN: 978-989-758-198-4

Keyword(s): Learning and Adaptive Systems, Compliant Assembly, Intelligent and Flexible Manufacturing.

Related Ontology Subjects/Areas/Topics: Engineering Applications ; Industrial Automation and Robotics ; Industrial Engineering ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Optimization Algorithms ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control

Abstract: Learning action parameters is becoming an ever more important topic in industrial assembly with tendencies towards smaller batch sizes, more required flexibility and process uncertainties. This paper presents a statistical online learning method capable of handling these issues. The method uses elimination of unpromising parameter sets to reduce the elements of the discretised sample space (inspired by Action Elimination) based on regression uncertainty. Kernel Density Estimation and Wilson Score are explored as internal representations. Based on a dynamic simulator setup for a real world Peg-in-Hole problem, it is shown that the presented method can drastically reduce the number of samples needed. Furthermore, it is also shown that the solution obtained in simulation by our learning method succeeds when executed on the corresponding real world setup.

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Paper citation in several formats:
Sørensen, L.; Buch, J.; Petersen, H. and Kraft, D. (2016). Online Action Learning using Kernel Density Estimation for Quick Discovery of Good Parameters for Peg-in-Hole Insertion.In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-198-4, pages 166-177. DOI: 10.5220/0005958801660177

@conference{icinco16,
author={Lars Carøe Sørensen. and Jacob Pørksen Buch. and Henrik Gordon Petersen. and Dirk Kraft.},
title={Online Action Learning using Kernel Density Estimation for Quick Discovery of Good Parameters for Peg-in-Hole Insertion},
booktitle={Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2016},
pages={166-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005958801660177},
isbn={978-989-758-198-4},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Online Action Learning using Kernel Density Estimation for Quick Discovery of Good Parameters for Peg-in-Hole Insertion
SN - 978-989-758-198-4
AU - Sørensen, L.
AU - Buch, J.
AU - Petersen, H.
AU - Kraft, D.
PY - 2016
SP - 166
EP - 177
DO - 10.5220/0005958801660177

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