Disney Research


When deploying social robots in the wild, it is crucial for developers to gain an understanding of how the interactions between the robot and its human conversational partners are progressing. Unlike in traditional task-based settings in which a human and a robot work on a tangible outcome that can serve as a proxy for how well a conversation is going, social settings require a deeper understanding of the underlying interaction dynamics. In this paper, we assess a set of recorded features of a robot having social conversations in a multi-party, multi-session setting and correlate them with how people rated their interaction. We then propose a framework that combines the features into a model that can automatically assess an ongoing conversation and determine its performance.

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