Neural Network Models for Reaching and Dexterous Manipulation in Humans and Anthropomorphic Robotic Systems

  • Rodolphe J. Gentili
  • Hyuk Oh
  • Javier Molina
  • José L. Contreras-Vidal
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS)


One fundamental problem for the developing brain as well as for any artificial system aiming to control a complex kinematic mechanism, such as a redundant anthropomorphic limb or finger, is to learn internal models of sensorimotor transformations for reaching and grasping. This is a complex problem since the mapping between sensory and motor spaces is generally highly nonlinear and depends of the constraints imposed by the changing physical attributes of the limb and hand and the changes in the developing brain. Previous computational models suggested that the development of visuomotor behavior requires a certain amount of simultaneous exposure to patterned proprioceptive and visual stimulation under conditions of self-produced movement—referred to as ‘motor babbling.’ However, the anthropomorphic geometrical constraints specific to the human arm and finger have not been incorporated in these models for performance in 3D. Here we propose a large scale neural network model composed of two modular components. The first module learns multiple internal inverse models of the kinematic features of an anthropomorphic arm and fingers having seven and four degree of freedom, respectively. Once the 3D inverse kinematics of the limb/finger are learned, the second module learns a simplified control strategy for the whole hand shaping during grasping tasks that provides a realistic coordination among fingers. These two bio-inspired neural models functionally mimic specific cortical features and are able to reproduce reaching and grasping human movements. The high modularity of this neural model makes it well suited as a high-level neuro-controller for planning and control of grasp motions in actual anthropomorphic robotic system.


Hide Layer Neural Network Model Motor Command Inverse Kinematic Neural Model 
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This work was supported in part by the Office of Naval Research (N000140910126) and the National Institutes of Health (PO1HD064653). Rodolphe J. Gentili would like to sincerely thank La Fondation Motrice, Paris, France, for the continued support of his research.


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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Rodolphe J. Gentili
  • Hyuk Oh
  • Javier Molina
  • José L. Contreras-Vidal
    • 1
    • 2
    • 3
  1. 1.School of Public Health, Department of KinesiologyUniversity of MarylandCollege ParkUSA
  2. 2.Graduate Program in Neuroscience and Cognitive Science (NACS)University of MarylandCollege ParkUSA
  3. 3.Graduate Program in BioengineeringUniversity of Maryland, School of Public HealthCollege ParkUSA

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