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Perception-Action Cycle pp 187–217Cite as

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

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Part of the book series: Springer Series in Cognitive and Neural Systems ((SSCNS))

Abstract

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.

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Notes

  1. 1.

    The models described in this chapter focus on the inverse kinematic problems, ignoring the effects of inertial and interaction forces with external loads on planned prehension movements. Although the limb’s dynamic computations are very important for the execution of planned kinematic trajectories, their study is outside the scope of this chapter. The reader is referred to Contreras-Vidal et al. (1997) and Bullock et al. (1998) for models of voluntary arm movements under variable force conditions that are compatible with the proposed model in this chapter.

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Acknowledgments

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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Authors and Affiliations

  1. School of Public Health, Department of Kinesiology, University of Maryland, College Park, MD, 20742, USA

    José L. Contreras-Vidal

  2. Graduate Program in Neuroscience and Cognitive Science (NACS), University of Maryland, College Park, MD, 20742, USA

    José L. Contreras-Vidal

  3. Graduate Program in Bioengineering, University of Maryland, School of Public Health, College Park, MD, 20742, USA

    José L. Contreras-Vidal

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  1. Rodolphe J. Gentili
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  2. Hyuk Oh
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  3. Javier Molina
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  4. José L. Contreras-Vidal
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Correspondence to José L. Contreras-Vidal .

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Editors and Affiliations

  1. Department of Psychology, Boston University, Boston, 02215, USA

    Vassilis Cutsuridis

  2. Dept. Computing Science, University of Stirling, Stirling, FK9 4LA, United Kingdom

    Amir Hussain

  3. King's College London, Dept. Mathematics, University of London, London, WC2R 2LS, United Kingdom

    John G. Taylor

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Gentili, R.J., Oh, H., Molina, J., Contreras-Vidal, J.L. (2011). Neural Network Models for Reaching and Dexterous Manipulation in Humans and Anthropomorphic Robotic Systems. In: Cutsuridis, V., Hussain, A., Taylor, J. (eds) Perception-Action Cycle. Springer Series in Cognitive and Neural Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1452-1_6

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