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Publication - Dr Nathan Lepora

    Gaussian Process Regression for a Biomimetic Tactile Sensor


    Aquilina, K, Barton, D & Lepora, N, 2016, ‘Gaussian Process Regression for a Biomimetic Tactile Sensor’., pp. 393-399


    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.

    Full details in the University publications repository