Postural Synergies and Neural Network for Autonomous Grasping: A Tool for Dextrous Prosthetic and Robotic Hands

  • Fanny Ficuciello
  • Gianluca Palli
  • Claudio Melchiorri
  • Bruno Siciliano
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 1)

Abstract

In this paper, a neural network model has been designed for planning grasps of a cybernetic hand prototype by means of postural synergies.The synergies subspace is derived by means of a joint-to-joint mapping from a human hand set of grasps. A library of motor primitives of the hand in a synergy based rendering has been built for a number of selected objects and tasks. The requirement of the task in a simplified approach is specified by the type of grasp, such as precision or power. A feed forward neural network has been trained using the grasping data from the library and running the Levenberg-Marquadt algorithm. By combining postural synergies and neural network the nonlinear relationship between the object geometric features and the hand configuration during grasping can be easily found with a good approximation. The experiments have been performed on the DEXMART hand prototype and the results demonstrate that integration of postural synergies and neural network is a promising tool toward simplified and autonomous grasping for artificial hands, such as anthropomorphic robotic hands and prostheses.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fanny Ficuciello
    • 1
  • Gianluca Palli
    • 2
  • Claudio Melchiorri
    • 2
  • Bruno Siciliano
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversità degli Studi di Napoli Federico IINapoliItaly
  2. 2.Dipartimento di Elettronica, Informatica e SistemisticaAlma Mater Studiorum Università di BolognaBolognaItaly

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