SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus

Stephanie M. Lukin, Claire Bonial, Matthew Marge, Taylor Hudson, Cory J Hayes, Kimberly A. Pollard, Anthony L Baker, Ashley N Foots, Ron Artstein, Felix Gervits, Mitchell Abrams, Cassidy Rae Henry, Lucia Donatelli, Anton Leuski, Susan G. Hill, David Traum, Clare R. Voss

Abstract:

We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot
dialogue in the task domain of collaborative exploration. The corpus was constructed from multi-phased
Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move
and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278
dialogues averaging 320 utterances. The dialogues are aligned with the multi-modal data streams available during
the experiments: 5,785 images and a subset of 30 maps. The corpus has been annotated with Abstract Meaning
Representation and Dialogue-AMR to identify the speaker’s intent and meaning within an utterance, and with
Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue
Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot
systems and enable research in open questions of how humans speak to robots. We release this corpus to
accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks
where details about the environment need to be discovered.