Abstract
Biomimetic tactile sensors often need a large amount of training to distinguish between a large number of different classes of stimuli. But when stimuli vary in one continuous property such as sharpness, it is possible to reduce training by using a discrimination approach rather than a classification approach. By presenting a biomimetic tactile sensing device, the TacTip, with a single exemplar of edge sharpness, the sensor was able to discriminate between unseen stimuli by comparing them to the trained exemplar. This technique reduces training time and may lead to more biologically relevant models of perceptual learning and discrimination.
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Acknowledgements
We thank Benjamin Ward-Cherrier and Luke Cramphorn for their help setting up the TacTip and stimuli. This work was supported by a Wellcome Trust PhD studentship.
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© 2016 Springer International Publishing Switzerland
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Roscow, E., Kent, C., Leonards, U., Lepora, N.F. (2016). Discrimination-Based Perception for Robot Touch. 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_53
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DOI: https://doi.org/10.1007/978-3-319-42417-0_53
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