Abstract
Few years old children lift and manipulate unfamiliar objects more dexterously than today’s robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. The estimation of the friction coefficient of the object’s material is a crucial information in a human dexterous manipulation. Humans estimate the friction coefficient based on the responses of their tactile mechanoreceptors. In this paper, finite element analysis was used to model a finger and an object. Simulated human afferent responses were then obtained for different friction coefficients. Multiple multilayer perceptrons that received as input simulated human afferent responses, and gave as output an estimation of the friction coefficient, were trained and tested. A performance analysis was carried out to verify the influence of the following factors: number of hidden neurons, compression ratio of the input pattern, partitions of the input pattern.
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References
Johansson, R.: Tactile sensibility in the human hand: receptive field characteristics of mechanoreceptive units in the glabrous skin area. Journal of Physiology 281, 101–123 (1978)
Johansson, R.: Sensory and memory information in the control of dexterous manipulation. In: Neural Bases of Motor Behaviour, pp. 205–260. Kluwer Academic Publishers, Dordrecht (1996)
Johnson, K.: The roles and functions of cutaneous mechanoreceptors. Curr. Opin. Neurobiol. 11, 455–461 (2001)
Israelsson, A.: Simulation of responses in afferents from the glabrous skin during human manipulation. Master’s thesis, Master thesis in Cognitive Science, Umeå University, Umeå, Sweden (2002)
Johansson, R., Westling, G.: Signals in tactile afferents from the fingers eliciting adaptive motor responses during precision grip. Exp. Brain Res. 66, 141–154 (1987)
Matuk Herrera, R.: A bio-inspired method for friction estimation. In: Proc. of MICAI 2007. IEEE CS Press, Los Alamitos (2007)
Maeno, T., Kawamura, T., Cheng, S.: Friction estimation by pressing an elastic finger-shaped sensor against a surface. IEEE Transactions on Robotics and Automation 20(2), 222–228 (2004)
North, J.F., Gibson, F.: Volume compressibility of human abdominal skin. J. Biomech. (11), 203–207 (1978)
Srinivasan, M.A., Gulati, R.J., Dandekar, K.: In vivo compressibility of the human fingertip. Adv. Bioeng. (22), 573–576 (1992)
Dandekar, K., Raju, B., Srinivasan, M.: 3-d finite-element models of human and monkey fingertips to investigate the mechanics of tactile sense. Journal of Biomechanical Engineering 125(5), 682–691 (2003)
Westling, G., Johansson, R.: Responses in glabrous skin mechanoreceptors during precision grip in humans. Exp. Brain Res. 66, 128–140 (1987)
Moller, M.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525–533 (1993)
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Matuk Herrera, R. (2008). Multilayer Perceptrons for Bio-inspired Friction Estimation. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_79
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DOI: https://doi.org/10.1007/978-3-540-69731-2_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
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