Joint attention in human-robot interaction
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Joint attention, a crucial component in interaction and an important milestone in human development, has drawn a lot of attention from the robotics community recently. Robotics researchers have studied and implemented joint attention for robots for the purposes of achieving natural human-robot interaction and facilitating social learning. Most previous work on the realization of joint attention in the robotics community has focused only on responding to joint attention and/or initiating joint attention. Responding to joint attention is the ability to follow another's direction of gaze and gestures in order to share common experience. Initiating joint attention is the ability to manipulate another's attention to a focus of interest in order to share experience. A third important component of joint attention is ensuring, where by the initiator ensures that the responders has changed their attention. However, to the best of our knowledge, there is no work explicitly addressing the ability for a robot to ensure that joint attention is reached by interacting agents. We refer to this ability as ensuring joint attention and recognize its importance in human-robot interaction. We propose a computational model of joint attention consisting of three parts: responding to joint attention, initiating joint attention, and ensuring joint attention. This modular decomposition is supported by psychological findings and matches the developmental timeline of humans. Infants start with the skill of following a caregiver's gaze, and then they exhibit imperative and declarative pointing gestures to get a caregiver's attention. Importantly, as they aged and social skills matured, initiating actions often come with an ensuring behavior that is to look back and forth between the caregiver and the referred object to see if the caregiver is paying attention to the referential object. We conducted two experiments to investigate joint attention in human-robot interaction. The first experiment explored effects of responding to joint attention. We hypothesize that humans will find that robots responding to joint attention are more transparent, more competent, and more socially interactive. Transparency helps people understand a robot's intention, facilitating a better human-robot interaction, and positive perception of a robot improves the human-robot relationship. Our hypotheses were supported by quantitative data, results from questionnaire, and behavioral observations. The second experiment studied the importance of ensuring joint attention. The results confirmed our hypotheses that robots that ensure joint attention yield better performance in interactive human-robot tasks and that ensuring joint attention behaviors are perceived as natural behaviors by humans. The findings suggest that social robots should use ensuring joint attention behaviors.