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A Model-Predictive Switching Approach To Efficient Intention Recognition

Krauthausen, Peter; Hanebeck, Uwe D.

Estimating a user's intention is central to close human-robot cooperation. In this paper, the problem of per- forming intention recognition with tree-structured Dynamic Bayesian Networks for large environments with many features is addressed. The proposed approach reduces the computational complexity of inference O(b^s) for tree-structured measurement models with an average branching factor b and tree height s to O((b)s), where b~ << b. The key idea is to switch between a finite set of reduced system and measurement models in order to restrict inference to the most important features. A model predictive approach to online switching between the reduced models is proposed that exploits an upper bound of the distances of the reduced models to the full model. The effectiveness of the proposed algorithm is validated in the intention recognition for a humanoid robot using a telepresent household scenario.

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Volltext §
DOI: 10.5445/IR/1000035082
DOI: 10.1109/IROS.2010.5651951
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2010
Sprache Englisch
Identifikator ISBN: 978-1-4244-6674-0
KITopen-ID: 1000035082
Erschienen in Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October, 2010
Verlag IEEE, Piscataway
Seiten 4908-4913
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