人間とロボットの対話収集における効率と網羅性の両立
Balancing Efficiency and Coverage in Human-Robot Dialogue Collection
ウィザード・オブ・オズ方式で協調探索タスクの対話データを収集し、GUI導入により対話速度と応答網羅性を両立できることを示した研究。
著者: Matthew Marge, Claire Bonial, Stephanie Lukin, Cory Hayes, Ashley Foots, Ron Artstein, Cassidy Henry, Kimberly Pollard, Carla Gordon, Felix Gervits, Anton Leuski, Susan Hill, Clare Voss, David Traum
分類: cs.RO, cs.HC
原文アブストラクト
We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automated robot dialogue system to support collaborative exploration tasks. In the first phase, a wizard freely typed robot utterances to human participants. For the second phase, this data was used to design a GUI that includes buttons for the most common communications, and templates for communications with varying parameters. Comparison of the data gathered in these phases show that the GUI enabled a faster pace of dialogue while still maintaining high coverage of suitable responses, enabling more efficient targeted data collection, and improvements in natural language understanding using GUI-collected data. As a promising first step towards interactive learning, this work shows that our approach enables the collection of useful training data for navigation-based HRI tasks.
