Joint Attention Estimator
HRI '20: ACM/IEEE International Conference on Human-Robot Interaction Cambridge United Kingdom March, 2020(2020)
Abstract
Joint attention has been identified as a critical component of successful human machine teams. Teaching robots to develop awareness of human cues is an important first step towards attaining and maintaining joint attention. We present a joint attention estimator that creates many possible candidates for joint attention and chooses the most likely object based on a human teammate's hand cues. Our system works within natural human interaction time (< 3 seconds) and above 80% accuracy. Our joint attention estimator provides a meaningful step towards ensuring robots enable human social skills for successful human machine teaming.
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Key words
human robot interaction, object detection, novel object finding
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