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2016 | OriginalPaper | Chapter

Gaussian Process Regression for a Biomimetic Tactile Sensor

Authors : Kirsty Aquilina, David A. W. Barton, Nathan F. Lepora

Published in: Biomimetic and Biohybrid Systems

Publisher: Springer International Publishing

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Abstract

The aim of this paper is to investigate a new approach to decode sensor information into spatial information. The tactile fingertip (TacTip) considered in this work is inspired from the operation of dermal papillae in the human fingertip. We propose an approach for interpreting tactile data consisting of a preprocessing dimensionality reduction step using principal component analysis and subsequently a regression model using a Gaussian process. Our results are compared with a classification method based on a biomimetic approach for Bayesian perception. The proposed method obtains comparable performance with the classification method whilst providing a framework that facilitates integration with control strategies, for example to perform controlled manipulation.

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Metadata
Title
Gaussian Process Regression for a Biomimetic Tactile Sensor
Authors
Kirsty Aquilina
David A. W. Barton
Nathan F. Lepora
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-42417-0_36

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