Attentional supervision of human-robot collaborative plans

Abstract

A human-robot interaction system should be capable of adapting the execution of cooperative plans with respect to complex human activities and interventions. In this paper, we present an integrated framework that exploits attentional supervision and contention scheduling to combine human-aware planning, plan execution, and natural human-robot interaction. Specifically, in the proposed approach, hierarchical cooperative plans are exploited as top-down attentional guidance for the robotic executive system, which can flexibly orchestrate the task activities while reacting to environmental stimuli and human behaviors. We describe the overall framework discussing some case studies in human-robot collaborative scenarios.