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URN: urn:nbn:de:swb:90-446660

Sensorimotor Learning for an Artificial Body Schema on Humanoid Robots

Ulbrich, Stefan

Abstract:
The body schema in humans is a cerebral representation of the current state of the own body. Its plasticity and adaptability allows tool use and the adaption to an ever-changing body. This thesis is about representations for sensorimotor learning on humanoid robots that enable efficient learning for an artificial body schema. Two novel techniques are proposed: The Kinematic Bézier Maps for the representation of kinematic and dynamic models and a decomposition method for accelerating learning.


Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Hochschulschrift
Jahr 2014
Sprache Englisch
Identifikator KITopen ID: 1000044666
Verlag Karlsruhe
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik und Robotik (IAR)
Prüfungsdaten 10.02.2014
Referent/Betreuer Prof. R. Dillmann
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