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Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation

Krauthausen, Peter

This thesis is concerned with intention recognition for non-verbal human-robot-cooperation. The problems of intention recognition based on uncertain and incomplete observations with detailed models in real-time are addressed by a consistent uncertainty processing, automatic model identification of the employed nonlinear stochastic dependencies and situation-specific inference in large dynamic Bayesian networks.

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Volltext §
DOI: 10.5445/IR/1000031353
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Hochschulschrift
Jahr 2012
Sprache Englisch
Identifikator urn:nbn:de:swb:90-313537
KITopen-ID: 1000031353
Verlag KIT, Karlsruhe
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik (IFA)
Prüfungsdaten 03.02.2012
Referent/Betreuer Prof. U. D. Hanebeck
Schlagworte Model Identification, Conditional Density Estimation, Nonlinear Filtering, Intention Recognition, Human-Robot-Cooperation
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