Chao, Crystal
Turn-Taking Based on Information Flow for Fluent Human-Robot Interaction
Thomaz, Andrea L. (Georgia Institute of Technology) | Chao, Crystal (Georgia Institute of Technology)
Turn-taking is a fundamental part of human communication. Our goal is to devise a turn-taking framework for human-robot interaction that, like the human skill, represents something fundamental about interaction, generic to context or domain. We propose a model of turn-taking, and conduct an experiment with human subjects to inform this model. Our findings from this study suggest that information flow is an integral part of human floor-passing behavior.
Turn-Taking Based on Information Flow for Fluent Human-Robot Interaction
Thomaz, Andrea L. (Georgia Institute of Technology) | Chao, Crystal (Georgia Institute of Technology)
Turn-taking is a fundamental part of human communication. Our goal is to devise a turn-taking framework for human-robot interaction that, like the human skill, represents something fundamental about interaction, generic to context or domain. We propose a model of turn-taking, and conduct an experiment with human subjects to inform this model. Our findings from this study suggest that information flow is an integral part of human floor-passing behavior. Following this, we implement autonomous floor relinquishing on a robot and discuss our insights into the nature of a general turn-taking model for human-robot interaction.
Turn Taking for Human-Robot Interaction
Chao, Crystal (Georgia Institute of Technology) | Thomaz, Andrea Lockerd ( Georgia Institute of Technology )
Applications in Human-Robot Interaction (HRI) in the not-so-distant future include robots that collaborate with factory workers or serve us as caregivers or waitstaff. When offering customized functionality in these dynamic environments, robots need to engage in real-time exchanges with humans. Robots thus need to be capable of participating in smooth turn-taking interactions. The research goal in HRI of unstructured dialogic interaction would allow communication with robots that is as natural as communication with other humans. Turn-taking is the framework that provides structure for human communication. Consciously or subconsciously, humans are able to communicate their understanding and control of the turn structure to a conversation partner by using syntax, semantics, paralinguistic cues, eye gaze, and body language in a socially intelligent way. Our research aims to show that by implementing these turn-taking cues within a interaction architecture that is designed fundamentally for turn-taking, a robot becomes easier and more efficient for a human to interact with. This paper outlines our approach and initial pilot study into this line of research.