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Our human sense of touch enables us to manipulate our surroundings; therefore, complex robotic manipulation will require artificial tactile sensing. Typically tactile sensor arrays are used in robotics, implying that a straightforward way of interpreting multidimensional data is required. In this paper we present a simple visualisation approach based on applying principal component analysis (PCA) to systematically collected sets of tactile data. We apply the visualisation approach to 4 different types of tactile sensor, encompassing fingertips and vibrissal arrays. The results show that PCA can reveal structure and regularities in the tactile data, which also permits the use of simple classifiers such as k-NN to achieve good inference. Additionally, the Euclidean distance in principal component space gives a measure of sensitivity, which can aid visualisation and also be used to find regions in the tactile input space where the sensor is able to perceive with higher accuracy. We expect that these observations will generalise, and thus offer the potential for novel control methods based on touch.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Robotics and Automation (ICRA 2018)
Subtitle of host publicationProceedings of a meeting held 21-25 May 2018, Brisbane, Australia.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781538630815
ISBN (Print)9781538630822
Publication statusPublished - 13 Sept 2018

Publication series

NameIEEE International Conference on Robotics and Automation (ICRA)
ISSN (Print)1050-4729


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