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 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.
KeywordsIntention Recognition; Dynamic Systems; (Conditional) Density Estimation; Regularization; Human-Robot-Cooperation
PublisherKIT Scientific Publishing
Publication date and place2013
SeriesKarlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory,