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Structuring of tactile sensory information for category formation in robotics palpation

Published version
Peer-reviewed

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Authors

Maiolino, Perla 
Bray, Ed 
Iida, Fumiya 

Abstract

Abstract: This paper proposes a framework to investigate the influence of physical interactions to sensory information, during robotic palpation. We embed a capacitive tactile sensor on a robotic arm to probe a soft phantom and detect and classify hard inclusions within it. A combination of PCA and K-Means clustering is used to: first, reduce the dimensionality of the spatiotemporal data obtained through the probing of each area in the phantom; second categorize the re-encoded data into a given number of categories. Results show that appropriate probing interactions can be useful in compensating for the quality of the data, or lack thereof. Finally, we test the proposed framework on a palpation scenario where a Support Vector Machine classifier is trained to discriminate amongst different types of hard inclusions. We show the proposed framework is capable of predicting the best-performing motion strategy, as well as the relative classification performance of the SVM classifier, solely based on unsupervised cluster analysis methods.

Description

Keywords

Article, Robotic palpation, Tactile sensing, Physical sensing, Sensory–motor coordination

Journal Title

Autonomous Robots

Conference Name

Journal ISSN

0929-5593
1573-7527

Volume Title

44

Publisher

Springer US
Sponsorship
Horticultural Development Company (CP 172)
Engineering and Physical Sciences Research Council (EP/N03211X/2)