Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation
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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DOI: 10.5445/IR/1000031353
| Zugehörige Institution(en) am KIT | Fakultät für Informatik – Institut für Anthropomatik (IFA) |
| Publikationstyp | Hochschulschrift |
| Publikationsjahr | 2012 |
| Sprache | Englisch |
| Identifikator | urn:nbn:de:swb:90-313537 KITopen-ID: 1000031353 |
| Verlag | Karlsruher Institut für Technologie (KIT) |
| Art der Arbeit | Dissertation |
| Fakultät | Fakultät für Informatik (INFORMATIK) |
| Institut | Fakultät für Informatik – Institut für Anthropomatik (IFA) |
| Prüfungsdaten | 03.02.2012 |
| Schlagwörter | Model Identification, Conditional Density Estimation, Nonlinear Filtering, Intention Recognition, Human-Robot-Cooperation |
| Relationen in KITopen | |
| Referent/Betreuer | Hanebeck, U. D. |