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Towards Grasping with Spiking Neural Networks for Anthropomorphic Robot Hands

  • J. Camilo Vasquez TieckEmail author
  • Heiko Donat
  • Jacques Kaiser
  • Igor Peric
  • Stefan Ulbrich
  • Arne Roennau
  • Marius Zöllner
  • Rüdiger Dillmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10613)

Abstract

Representation and execution of movement in biology is an active field of research relevant to neurorobotics. Humans can remember grasp motions and modify them during execution based on the shape and the intended interaction with objects. We present a hierarchical spiking neural network with a biologically inspired architecture for representing different grasp motions. We demonstrate the ability of our network to learn from human demonstration using synaptic plasticity on two different exemplary grasp types (pinch and cylinder). We evaluate the performance of the network in simulation and on a real anthropomorphic robotic hand. The network exposes the ability of learning finger coordination and synergies between joints that can be used for grasping.

Keywords

Grasp motion representation Spiking networks Neurorobotics Motor primitives 

Notes

Acknowledgments

The research leading to these results has received funding from the European Union Horizon 2020 Programme under grant agreement n.720270 (Human Brain Project SGA1).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • J. Camilo Vasquez Tieck
    • 1
    Email author
  • Heiko Donat
    • 1
  • Jacques Kaiser
    • 1
  • Igor Peric
    • 1
  • Stefan Ulbrich
    • 1
  • Arne Roennau
    • 1
  • Marius Zöllner
    • 1
  • Rüdiger Dillmann
    • 1
  1. 1.FZI Research Center for Information TechnologyKarlsruheGermany

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