A Bio-inspired Method for Incipient Slip Detection

  • Rosana Matuk Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4830)


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. In a human dexterous manipulation a crucial event is the detection of incipient slips. Humans detect the incipient slips based on the responses of their tactile mechanoreceptors. In this paper, we propose a method to detect the incipient slips using artificial neural networks that receive as input simulated human afferent responses. This method is strongly inspired on neurophysiological studies of the afferent responses during the human dexterous manipulation. Finite element analysis was used to model two fingers and an object, and simulated experiments using the proposed method were done. To the best of our knowledge, this is the first time that simulated human afferent signals are combined with finite element analysis and artificial neural networks, to detect the incipient slips.


Neural networks Dexterous manipulation Robotics 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

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

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