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

• Autore: Krauthausen Peter
• Pubblicazione: KIT Scientific Publishing, 2013
• Descrizione fisica: 1 electronic resource (XIV, 210 p. p.)
• Soggetto non controllato: Intention Recognition; Dynamic Systems; (Conditional) Density Estimation; Regularization; Human-Robot-Cooperation
• Sommario/riassunto: 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.
• Titolo autorizzato: Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation
• ISBN: 1000031356
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910346889403321
• Lo trovi qui: Univ. Federico II