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Multilayer Perceptrons for Bio-inspired Friction Estimation

  • Rosana Matuk Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5097)

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.

Keywords

Hide Layer Compression Ratio Spike Train Hide Neuron Grip Force 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rosana Matuk Herrera
    • 1
  1. 1.Department of Computer Science, Facultad de Ciencias Exactas y NaturalesUniversidad de Buenos AiresArgentina

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