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

TL;DR

This paper addresses how group entrainment emerges in human teams to optimize human-robot collaboration. It employs a mixed-method laboratory study with motion capture, video, and interviews across ten dyads and ten triads performing a fast industrial-inspired task to identify when entrainment occurs and to extract five high-level characteristics. The authors derive three design considerations for cobots—adaptation to human fluctuations, strategic use of acoustic feedback, and short-term iteration-level consistency—to foster bidirectional entrainment and efficient coordination. The findings highlight the central roles of the point-of-assembly, multisensory cues, and communication patterns in enabling smooth, trustworthy human-robot group work, with practical implications for industrial cobot design.

Abstract

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.

Figures (3)

• Figure 1: Raw data plotted for dyads (N = 10) and Triads (N = 8) showing the fluctuation times in iteration between each set of consecutive iterations. Furthermore, it plots the average (as a solid red line), the standard deviation (green envelope), as well as the initial period of low temporal fluctuations, suggesting entrainment, period after starting the task (red overlay).
• Figure 2: Pareto diagrams for the dyads (left) and triads (right) respectively. The data visualised is the topic of communication. The graphs show, that while both dyads and triads discussed the same topic categories, the first two columns 'Small talk' and 'General task related talk' were inverted. This means that the dyads focused on task unrelated conversation without a decrease in task performance (average iterations pr. 10 sec interval, see \ref{['tab:DyadTriad']}) compared to the triads who focused conversation on the task at hand. This could be indicative of the dyadic collaboration requiring less mental workload.
• Figure 3: This figure shows 20 frames (5 columns, 4 rows). The first two rows represent the x-z perspective while the second two rows represent the x-y perspective (all for the same triad T3). Each frame presents six lines, two for each collaborator (right hand/left hand). Each frame plots the trajectory for each of the six collaborating hands for 10% of the task duration (i.e., 24 seconds). As clearly visible, the consistency of trajectory increases which is indicative of spatial synchronisation. Colour coding: Yellow ([brightYellow]110) and purple ([purple]116): left and right hand of the bowler. Light blue ([lightBlue]115) and green ([green]119): left and right hand of cuber one. Dark blue ([darkBlue]169) and orange ([orange]58): left and right hand of cuber two.