A System for Affordance Based learning of Object Grasping in a Robot

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dc.contributor.author Wilson, James
dc.date.accessioned 2010-05-19T09:04:09Z
dc.date.available 2010-05-19T09:04:09Z
dc.date.issued 2010-05-19
dc.identifier.citation Wilson , J 2010 , ' A System for Affordance Based learning of Object Grasping in a Robot ' . en
dc.identifier.other PURE: 149888
dc.identifier.other dspace: 2160/4647
dc.identifier.uri http://hdl.handle.net/2160/4647
dc.description null Sponsorship: ROSSI en
dc.description.abstract A system is described which takes synergies extracted from human grasp experiments and maps these onto a robot vision and hand-arm platform to facilitate the transfer of skills \cite{tao2010}. This system forms part of a framework which is extended by adding a self organizing map based affordance learning system. This affordance system learns the correlations between perceived object features and relevant motor outputs expressed in the form of synergies, and comes to guide grasping of an object by predicting the appropriate synergy outputs for a given object. It does so online and autonomously. Preliminary results test its effectiveness in this role and show that it is capable of learning fast and in spite of noise. en
dc.language.iso eng
dc.title A System for Affordance Based learning of Object Grasping in a Robot en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Intelligent Robotics Group en
dc.description.status Non peer reviewed en


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