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A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Do, Martin Minh Thong

Abstract:
Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions.

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Volltext §
DOI: 10.5445/IR/1000047789
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Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Hochschulschrift
Jahr 2014
Sprache Englisch
Identifikator urn:nbn:de:swb:90-477899
KITopen-ID: 1000047789
Verlag KIT, Karlsruhe
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik und Robotik (IAR)
Prüfungsdaten 12.06.2014
Referent/Betreuer Prof. R. Dillmann
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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