人間-ロボット対話における曖昧な指示の視覚化手法の比較
A Comparison of Visualisation Methods for Disambiguating Verbal Requests in Human-Robot Interaction
YuMiロボットを用いた実験で、言葉による指示の対象を特定するための3つの視覚化手法（複合現実・拡張現実・モニタ）を比較し、精度と没入感に差があることを示した。
著者: Elena Sibirtseva, Dimosthenis Kontogiorgos, Olov Nykvist, Hakan Karaoguz, Iolanda Leite, Joakim Gustafson, Danica Kragic
分類: cs.RO, cs.HC
原文アブストラクト
Picking up objects requested by a human user is a common task in human-robot interaction. When multiple objects match the user's verbal description, the robot needs to clarify which object the user is referring to before executing the action. Previous research has focused on perceiving user's multimodal behaviour to complement verbal commands or minimising the number of follow up questions to reduce task time. In this paper, we propose a system for reference disambiguation based on visualisation and compare three methods to disambiguate natural language instructions. In a controlled experiment with a YuMi robot, we investigated real-time augmentations of the workspace in three conditions -- mixed reality, augmented reality, and a monitor as the baseline -- using objective measures such as time and accuracy, and subjective measures like engagement, immersion, and display interference. Significant differences were found in accuracy and engagement between the conditions, but no differences were found in task time. Despite the higher error rates in the mixed reality condition, participants found that modality more engaging than the other two, but overall showed preference for the augmented reality condition over the monitor and mixed reality conditions.
