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Situation-Specific Intention Recognition for Human-Robot-Cooperation

Krauthausen, Peter; Hanebeck, Uwe D.


Recognizing human intentions is part of the decision process in many technical devices. In order to achieve natural interaction, the required estimation quality and the used computation time need to be balanced. This becomes challenging, if the number of sensors is high and measurement systems are complex. In this paper, a model predictive approach to this problem based on online switching of small, situation-specific Dynamic Bayesian Networks is proposed. The contributions are an efficient modeling and inference of situations and a greedy model predictive switching algorithm maximizing the mutual information of predicted situations. The achievable accuracy and computational savings are demonstrated for a household scenario by using an extended range telepresence system.

Volltext §
DOI: 10.5445/IR/1000035038
DOI: 10.1007/978-3-642-16111-7_48
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2010
Sprache Englisch
Identifikator ISBN: 978-3-642-16110-0
ISSN: 0302-9743
KITopen-ID: 1000035038
Erschienen in KI 2010: Advances in Artificial Intelligence 33rd Annual German Conference on AI Karlsruhe, Germany, September 21-24, 2010. Ed.: R. Dillmann
Verlag Springer-Verlag
Seiten 418-425
Serie Lecture Notes in Computer Science ; 6359
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