Online Action Learning using Kernel Density Estimation for Quick Discovery of Good Parameters for Peg-in-Hole Insertion

Lars Carøe Sørensen, Jacob Pørksen Buch, Henrik Gordon Petersen, Dirk Kraft

2016

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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