Robots are increasingly introduced to work in concert with people in high-intensity domains, such as manufacturing, space exploration and hazardous environments. Tasks in these domains are often well-defined, but involve complex coordination under constraints and are performed under time pressure. Although numerous studies on human teamwork and coordination in these settings, very little prior work exists on applying these models to human-robot interaction. In this paper we propose a methodology for applying prior art in Shared Mental Models (SMMs) to promote effective human-robot teaming. SMMs are measurable models developed among team members prior to task execution and are strongly correlated to team performance.
