01792nam 2200373z- 450 9910346889403321202102111000031356(CKB)4920000000101628(oapen)https://directory.doabooks.org/handle/20.500.12854/51483(oapen)doab51483(EXLCZ)99492000000010162820202102d2013 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierLearning Dynamic Systems for Intention Recognition in Human-Robot-CooperationKIT Scientific Publishing20131 online 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.(Conditional) Density EstimationDynamic SystemsHuman-Robot-CooperationIntention RecognitionRegularizationKrauthausen Peterauth1305984BOOK9910346889403321Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation3028101UNINA