Natural Face-to-Face Conversation with Socially Intelligent Robots

Abstract

When humans engage in face-to-face conversation, they use their voices, faces, and bodies together in a rich, multimodal, continuous, interactive process. For a robot to participate fully in this sort of natural, faceto-face conversation in the real world, it must also be able not only to understand the multimodal communicative signals of its human partners, but also to produce understandable, appropriate, and natural communicative signals in response. A robot capable of this form of interaction can be used in a large number of areas: for example, it could take the role of a home companion, a museum tour guide, a tutor, or a personal health coach. While a number of such robots have been successfully deployed, the full potential of socially interactive robots has not been realised, due both to incomplete models of human multimodal communication and to technical limitations. However, thanks to recent developments in a number of areas— including techniques for datadriven interaction models, methods of evaluating interactive robots in real-world contexts, and off-the-shelf component technology—the goal of developing a naturally interactive robot is now increasingly achievable