Attentional regulations in a situated human-robot dialogue

We propose a framework where the human-robot interaction is modeled as a multimodal dialogue which is regulated by an attentional system that guides the robot towards the execution of structured tasks. Specifically, we propose an approach where the dialogue between the human and the robot is represented as a Partially Obervable Markov Decision Process (POMDP), while the associated dialogue policy is enhanced by top-down attentional mechanisms that provide contextual and task-related contents. We introduce simple case studies that illustrate the system at work in different conditions considering top-down regulations and dialogue flows in synergistic and conflicting situations.