Understanding Entrainment in Human Groups: Optimising Human-Robot Collaboration from Lessons Learned during Human-Human Collaboration
<|reference_start|>Understanding Entrainment in Human Groups: Optimising Human-Robot Collaboration from Lessons Learned during Human-Human Collaboration: 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.<|reference_end|>
@article{schneiders2024understanding,
    title={Understanding Entrainment in Human Groups: Optimising Human-Robot
  Collaboration from Lessons Learned during Human-Human Collaboration},
    author={Eike Schneiders, Christopher Fourie, Stanley Celestin, Julie Shah,
  Malte Jung},
    journal={arXiv preprint arXiv:2402.15427},
    year={2024},
    doi={10.1145/3613904.3642427},
    archivePrefix={arXiv},
    eprint={2402.15427},
    primaryClass={cs.HC cs.AI cs.RO}
}