To enable situated human–robot interaction (HRI), an autonomous robot must both understand and control proxemics—the social use of space—to employ natural communication mechanisms analogous to those used by humans. This work presents a computational framework of proxemics based on data-driven probabilistic models of how social signals (speech and gesture) are produced (by a human) and perceived (by a robot). The framework and modelswereimplementedasautonomousproxemicbehavior systems for sociable robots, including: (1) a sampling-based methodforrobotproxemicgoalstateestimationwithrespect to human–robot distance and orientation parameters, (2) a reactive proxemic controller for goal state realization, and (3) a cost-based trajectory planner for maximizing automated robot speech and gesture recognition rates along a pathtothegoalstate.Evaluationresultsindicatethatthegoal state estimation and realization significantly improve upon past work in human–robot proxemics with respect to “interaction potential”—predicted automated speech and gesture recognitionratesastherobotentersintoandengagesinfaceto-face social encounters with a human user—illustrating their efficacy to support richer robot perception and autonomy in HRI.

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• Autonomous human–robot proxemics: socially aware navigation based on interaction potential