Understanding Entrainment in Human Groups: Optimising Human-Robot Collaboration from Lessons Learned during Human-Human Collaboration

要旨

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 entrainment characteristics 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, based on our findings, we present three design considerations for future research and design on collaboration with robots.

著者
Eike Schneiders
University of Nottingham, Nottingham, United Kingdom
Christopher K. Fourie
Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
Stanley Celestin
Cornell University, Ithaca, New York, United States
Julie Shah
MIT, Cambridge, Massachusetts, United States
Malte F. Jung
Cornell University, Ithaca, New York, United States
論文URL

https://doi.org/10.1145/3613904.3642427

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Communication and Collaboration

320 'Emalani Theater
5 件の発表
2024-05-15 18:00:00
2024-05-15 19:20:00