Transferring human grasping synergies to a robot

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dc.contributor.author Geng, Tao
dc.contributor.author Lee, Mark
dc.contributor.author Hülse, Martin
dc.date.accessioned 2011-02-22T14:40:10Z
dc.date.available 2011-02-22T14:40:10Z
dc.date.issued 2011-02
dc.identifier.citation Geng , T , Lee , M & Hülse , M 2011 , ' Transferring human grasping synergies to a robot ' Mechatronics , vol 21 , no. 1 , pp. 272-284 . en
dc.identifier.other PURE: 157909
dc.identifier.other dspace: 2160/6135
dc.identifier.uri http://hdl.handle.net/2160/6135
dc.description Geng, T., Lee, M., Huelse, M. (2011). Transferring human grasping synergies to a robot. Mechatronics, 21 (1), 272-284. Sponsorship: EC en
dc.description.abstract In this paper, a system for transferring human grasping skills to a robot is presented. In order to reduce the dimensionality of the grasp postures, we extracted three synergies from data on human grasping experiments and trained a neural network with the features of the objects and the coefficients of the synergies. Then, the trained neural network was employed to control robot grasping via an individually optimized mapping between the human hand and the robot hand. As force control was unavailable on our robot hand, we designed a simple strategy for the robot to grasp and hold the objects by exploiting tactile feedback at the fingers. Experimental results demonstrated that the system can generalize the transferred skills to grasp new objects. en
dc.format.extent 13 en
dc.language.iso eng
dc.relation.ispartof Mechatronics en
dc.title Transferring human grasping synergies to a robot en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/j.mechatronics.2010.11.003
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Intelligent Robotics Group en
dc.description.status Peer reviewed en


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