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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Acknowledgements
I would like to thank Benjamin Ward-Cherrier for providing the training and test dataset used in this paper. This work was supported by the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE) at Bristol Robotics Laboratory. This work was also supported by a grant from the Engineering and Physical Sciences Research Council (EPSRC) on ‘Tactile Superresolution Sensing’ (EP/M02993X/1).
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Aquilina, K., Barton, D.A.W., Lepora, N.F. (2016). Gaussian Process Regression for a Biomimetic Tactile Sensor. In: Lepora, N., Mura, A., Mangan, M., Verschure, P., Desmulliez, M., Prescott, T. (eds) Biomimetic and Biohybrid Systems. Living Machines 2016. Lecture Notes in Computer Science(), vol 9793. Springer, Cham. https://doi.org/10.1007/978-3-319-42417-0_36
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DOI: https://doi.org/10.1007/978-3-319-42417-0_36
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