Human and robot behavior modeling for probabilistic cognition of an autonomous service robot

This paper presents an approach to model multi-modal human-robot interaction as partially observable Markov decision processes (POMDPs) for a service robot in realistic settings. Interaction modalities include spoken dialog and non-verbal human activities like gestures and general body postures. By using POMDPs which can model uncertainties in robot perception as well as human behavior, robustness and flexibility concerning autonomous decision making are improved in real world settings. This paper presents strategies to express perception uncertainties, stochastic human behavior and typical mission objectives in explicit POMDP models. Additionally, a system is presented to compile models from more compact representations. Finally, models are actually evaluated on a physical, autonomous service robot, controlled by POMDP decision making and compared to a classical baseline controller in typical domestic missions.