人間グループにおけるエントレインメントの理解：人間同士の協働から学ぶ人間-ロボット協働の最適化
Understanding Entrainment in Human Groups: Optimising Human-Robot Collaboration from Lessons Learned during Human-Human Collaboration
人間同士のペア・グループ協働における同期（エントレインメント）の特徴を運動計測とインタビューで分析し、ロボットとの協働設計への示唆を導出した研究。
著者: Eike Schneiders, Christopher Fourie, Stanley Celestin, Julie Shah, Malte Jung
分類: cs.HC, cs.AI, cs.RO
原文アブストラクト
Successful entrainment during collaboration positively affects trust, willingness to collaborate, and likeability towards collaborators. In this paper, we present a mixed-method study to investigate characteristics of successful entrainment leading to pair and group-based synchronisation. Drawing inspiration from industrial settings, we designed a fast-paced, short-cycle repetitive task. Using motion tracking, we investigated entrainment in both dyadic and triadic task completion. Furthermore, we utilise audio-video recordings and semi-structured interviews to contextualise participants' experiences. This paper contributes to the Human-Computer/Robot Interaction (HCI/HRI) literature using a human-centred approach to identify characteristics of entrainment during pair- and group-based collaboration. We present five characteristics related to successful entrainment. These are related to the occurrence of entrainment, leader-follower patterns, interpersonal communication, the importance of the point-of-assembly, and the value of acoustic feedback. Finally, we present three design considerations for future research and design on collaboration with robots.
