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

This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian netw...

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Bibliographic Details
Main Author: Krauthausen, Peter (auth)
Format: Electronic Book Chapter
Language:English
Published: KIT Scientific Publishing 2013
Series:Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
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Online Access:DOAB: download the publication
DOAB: description of the publication
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245 1 0 |a Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation 
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520 |a This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference. 
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546 |a English 
653 |a Intention Recognition 
653 |a Dynamic Systems 
653 |a (Conditional) Density Estimation 
653 |a Regularization 
653 |a Human-Robot-Cooperation 
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