Perceptual Models of Human-Robot Proxemics

To enable socially situated human-robot interaction, a robot must both understand and control proxemics—the social use of space—to employ communication mechanisms analogous to those used by humans. In this work, we considered how proxemic behavior is influenced by human speech and gesture production, and how this impacts robot speech and gesture recognition in face-to-face social interactions. We conducted a data collection to model these factors conditioned on distance. This resulting models of pose, speech, and gesture were consistent with related work in human-human interactions, but were inconsistent with related work in human-human interactions—participants in our data collection pos itioned themselves much farther away than has been observed in related work. These models have been integrated into a situated autonomous proxemic robot controller, in which the robot selects interagent pose parameters to maximize its expectation to recognize natural human speech and body gestures during an interaction. This work contributes to the understanding of the underlying per-cultural processes that govern human proxemic behavior, and has implications for the development of robust proxemic controllers for sociable and socially assistive robots situated in complex interactions (e.g., with multiple people or individuals with hearing/visual impairments) and environments (e.g., in which there is loud noise, reverberation, low lighting, or visual occlusion).