Managing Human-Robot Engagement with Forecasts and... um... Hesitations

We explore methods for managing conversational engagement in open-world, physically situated dialog systems. We investigate a self-supervised methodology for constructing forecasting models that aim to anticipate when participants are about to terminate their interactions with a situated system. We study how these models can be leveraged to guide a disengagement policy that uses linguistic hesitation actions, such as filled and non-filled pauses, when uncertainty about the continuation of engagement arises. The hesitations allow for additional time for sensing and inference, and convey the system's uncertainty. We report results from a study of the proposed approach with a directions-giving robot deployed in the wild.