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Learning of Generalized Manipulation Strategies in Service Robotics

Jäkel, Rainer

Abstract:
This thesis makes a contribution to autonomous robotic manipulation. The core is a novel constraint-based representation of manipulation tasks suitable for flexible online motion planning. Interactive learning from natural human demonstrations is combined with parallelized optimization to enable efficient learning of complex manipulation tasks with limited training data. Prior planning results are encoded automatically into the model to reduce planning time and solve the correspondence problem.

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Volltext §
DOI: 10.5445/IR/1000033899
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Hochschulschrift
Publikationsjahr 2013
Sprache Englisch
Identifikator urn:nbn:de:swb:90-338993
KITopen-ID: 1000033899
Verlag Karlsruher Institut für Technologie (KIT)
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik (IFA)
Prüfungsdaten 29.01.2013
Referent/Betreuer Prof. R. Dillmann
Schlagwörter Motion planning, Autonomous robotic manipulation, Machine learning, Programming by Demonstration, Service Robotics
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