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Anticipatory grip force control using a cerebellar model

de Gruijl, J.R. and van der Smagt, Patrick and De Zeeuw, C.I. (2009) Anticipatory grip force control using a cerebellar model. Neuroscience, 162 (3), pp. 777-786. Elsevier Ltd.. DOI: 10.1016.

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Official URL: http://dx.doi.org/10.1016/j.neuroscience.2009.02.041

Abstract

Grip force modulation has a rich history of research, but the results remain to be integrated as a neurocomputational model and applied in a robotic system. Adaptive grip force control as exhibited by humans would enable robots to handle objects with sufficient yet minimal force, thus minimizing the risk of crushing objects or inadvertently dropping them. We investigated the feasibility of grip force control by means of a biological neural approach to ascertain the possibilities for future application in robotics. As the cerebellum appears crucial for adequate grip force control, we tested a computational model of the olivo-cerebellar system. This model takes into account that the processing of sensory signals introduces a 100 ms delay, and because of this delay, the system needs to learn anticipatory rather than feedback control. For training, we considered three scenarios for feedback information: (1) grip force error estimation, (2) sensory input on deformation of the fingertips, and (3) as a control, noise. The system was trained on a data set consisting of force and acceleration recordings from human test subjects. Our results show that the cerebellar model is capable of learning and performing anticipatory grip force control closely resembling that of human test subjects despite the delay. The system performs best if the delayed feedback signal carries an error estimation, but it can also perform well when sensory data are used instead. Thus, these tests indicate that a cerebellar neural network can indeed serve well in anticipatory grip force control not only in a biological but also in an artificial system.

Document Type:Article
Title:Anticipatory grip force control using a cerebellar model
Authors:
AuthorsInstitution or Email of Authors
de Gruijl, J.R.Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam
van der Smagt, PatrickUNSPECIFIED
De Zeeuw, C.I.Department of Neuroscience, Erasmus MC, Rotterdam
Date:26 February 2009
Journal or Publication Title:Neuroscience
Refereed publication:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:162
DOI:10.1016
Page Range:pp. 777-786
Publisher:Elsevier Ltd.
Series Name:New Insights in Cerebellar Function
Status:Published
Keywords:cerebellum; grip force; motor learning; computational model
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W SY - Technik für Raumfahrtsysteme
DLR - Research area:Space
DLR - Program:W SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):W - Weiterentwicklung Robotik - Mechatronik und Dynamik (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics > Robotic Systems
Deposited By: Gabriele Beinhofer
Deposited On:13 Jan 2010 13:20
Last Modified:12 Dec 2013 20:51

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