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.
Original languageEnglish
Pages393-399
Number of pages7
DOIs
Publication statusPublished - 2016
Event5th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2016 - Edinburgh, United Kingdom
Duration: 19 Jul 201622 Jul 2016

Conference

Conference5th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2016
CountryUnited Kingdom
CityEdinburgh
Period19/07/1622/07/16

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    Aquilina, K., Barton, D., & Lepora, N. (2016). Gaussian Process Regression for a Biomimetic Tactile Sensor. 393-399. Abstract from 5th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2016, Edinburgh, United Kingdom. https://doi.org/10.1007/978-3-319-42417-0_36