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.