01775nam 2200361z- 450 991034688940332120231214133223.01000031356(CKB)4920000000101628(oapen)https://directory.doabooks.org/handle/20.500.12854/51483(EXLCZ)99492000000010162820202102d2013 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierLearning Dynamic Systems for Intention Recognition in Human-Robot-CooperationKIT Scientific Publishing20131 electronic resource (XIV, 210 p. p.)Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory3-86644-952-6 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.Intention RecognitionDynamic Systems(Conditional) Density EstimationRegularizationHuman-Robot-CooperationKrauthausen Peterauth1305984BOOK9910346889403321Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation3028101UNINA