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

Eike Schneiders<EMAIL_ADDRESS>University of
NottinghamWollaton RoadNottinghamUKNG8 1BB ,  Christopher Fourie
<EMAIL_ADDRESS>Massachusetts Institute of TechnologyCambridge, MAUSA ,
Stanley Celestin<EMAIL_ADDRESS>Cornell University107 Hoy RoadIthaca,
NYUSA14853 ,  Julie Shah<EMAIL_ADDRESS>Massachusetts Institute
of TechnologyCambridge, MAUSA and  Malte Jung<EMAIL_ADDRESS>Cornell
University107 Hoy RoadIthaca, NYUSA14853

(2024)

###### 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.

entrainment in dyads and triads, temporal synchronisation, collaboration in
groups, non-dyadic human-robot interaction

††journalyear: 2024††copyright: rightsretained††conference: Proceedings of the
CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Proceedings of the CHI Conference on Human
Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI,
USA††doi: 10.1145/3613904.3642427††isbn: 979-8-4007-0330-0/24/05††ccs: Human-
centered computing Laboratory experiments††ccs: Human-centered computing User
studies††ccs: Human-centered computing Empirical studies in collaborative and
social computing Figure 1. The four frames (left to right) show a snapshot of
the triadic collaboration (T4) from iteration 24, 44, 64, and 84. It shows the
consistency of the position of the bowl, i.e., the point of assembly, across
these 60 iterations.

##  1\. Introduction

The industrial sector remains one of the fastest growing application areas for
collaborative robotics (Sanneman et al., 2021). No other domain has benefited
to the same extent from the introduction of robotic automation as the
industrial sector has. Given the complexity of modern manufacturing processes,
these tasks often require collaboration between multiple, human and non-human,
actors. Yet, most studies within the field of Human-Robot Interaction (HRI)
emphasise the investigation of dyadic interaction (Schneiders et al., 2022),
i.e., the investigation of the interaction between one human and one robot.
However, group-based collaboration and entrainment has received little
attention. We argue, that efficient collaboration around a given tasks becomes
even more relevant given complex group configurations. Prior research has
highlighted the importance of temporal synchronisation between collaborators,
i.e., rhythms between interaction partners, for efficient collaboration (Park
et al., 2013; Robinson et al., 2020; Tarr et al., 2018). To achieve this
temporal synchronisation, it is vital that collaborators entrain with one
another, thereby achieving better coordination. As Cross et al. (Cross et al.,
2019) state: “Entrainment refers to temporally coupled or synchronised
systems, and it is the process of things moving in time together”. In other
words, entrainment refers to the process of moving in temporal
synchronisation. In this paper, the term ‘system’ refers to the collaborating
partners. Furthermore, while multiple types of entrainment exist, this study
focuses exclusively on physical entrainment between different actors, referred
to as interpersonal motor synchronisation (IMS) (Rinott and Tractinsky, 2021),
thereby excluding other forms of entrainment, such as lexical entrainment.

Achieving temporal synchronisation during collaboration through the process of
entrainment leads to a multitude of benefits: it fosters a stronger sense of
togetherness and connection (Hove and Risen, 2009), enhances the likeability
between collaborators (Hove and Risen, 2009; Launay et al., 2014), and
promotes a willingness to cooperate (Wiltermuth and Heath, 2009; Rinott and
Tractinsky, 2021). Entrainment, its occurrence, and its effects on human
collaborators have been explored in various contexts, including dancing (Tarr
et al., 2016; Woolhouse et al., 2016), singing (Anshel and Kipper, 1988; von
Zimmermann and Richardson, 2016), walking (van Ulzen et al., 2008), and body
movement (Tarr et al., 2015; Reddish et al., 2013). The focus on collaborative
automation, with an emphasis on human-robot collaboration (HRC) over full
automation, has recently received more attention (Comission, 2020; Breque et
al., 2021). To fully utilise the strengths of both human and automation
technology, such as robots, a deeper understanding of how humans and robots
can collaborate efficiently is essential. In our study, we adopt a human-
centred approach. Specifically, we investigate human entrainment in dyads and
triads to understand the entrainment process among humans, thereby informing
design considerations for future research and design of collaborative robots
for human-robot collaboration. Enhancing our understanding of how humans
achieve collaborative rhythms will pave the way for more effective and natural
collaborations within mixed human-robot teams.

In this paper, we investigate how human dyads and triads entrain with each
other to improve collaboration during the completion of a fast-paced, short-
cycle repetitive task. To investigate this, we conducted a controlled mixed-
method laboratory study in which ten dyads and ten triads completed a
collaborative task inspired by industrial work. We collected a variety of data
streams, including motion tracking data, video recordings, and semi-structured
post-task interviews. Using video inspection, thematic analysis, as well as
trajectory analysis of the motion tracking data, we identified when
collaborators achieved a collaborative rhythm and what led to this. Lastly, we
discuss the implications of the presented findings and propose three design
considerations for future Human-Robot Collaboration design and research,
increasing the potential for efficient entrainment and ultimately improving
human-robot collaboration.

##  2\. Related Work

This section will present existing research on entrainment during human
collaboration, benefits of temporally synchronised collaboration, and physical
entrainment in human-robot collaboration.

###  2.1. Entrainment in Human Collaboration

The theory of joint action, a field of cognitive psychology, studies how human
agents collaborate in dyadic and group interactions. Entrainment is
highlighted as a core process, often described as an unconscious
synchronisation between agents across various contexts. Previous research has
investigated how pairs and groups of humans physically entrain with each other
during task completion and what effects this has on interpersonal relations.
Examples include walking in groups (Wiltermuth and Heath, 2009), finger
tapping to an external stimuli (Hove and Risen, 2009), observation of animated
movement synchronicity (Lakens, 2010), collaborative biking (Rinott and
Tractinsky, 2021), hand-over tasks (Roy and Edan, 2020), as well as literature
reviews investigating several aspects of human-human entrainment (Cross et
al., 2019; Rinott and Tractinsky, 2021). Through an extensive literature
review, Cross et al. (Cross et al., 2019) identify 48 studies focusing on the
effects of pro-group behaviour caused by interpersonal entrainment. Various
experimental tasks (e.g., dancing, tapping, arm movement) and experiment
durations (from 11 seconds to 1 hour) have been conducted to investigate
interpersonal entrainment. As summarised by Cross et al. (Cross et al., 2019),
the investigation of entrainment has received extensive focus in interactions
such as body limb movement (Tarr et al., 2015; Reddish et al., 2013), dancing
in groups (Tarr et al., 2016; Woolhouse et al., 2016) or singing (Anshel and
Kipper, 1988; von Zimmermann and Richardson, 2016).

Roy and Edan (2020) conducted a lab-based study to investigate how pairs of
human shelf fillers entrain with each other. This study was inspired by the
working context observed at supermarkets. They conducted a mixed-method study
utilising field studies to better understand supermarket employees’ tasks and
re-create those in a controlled lab environment. Using both subjective data,
such as a verbal description of the experience during task completion or post-
session questionnaires, as well as quantitative performance metrics, including
‘the number of bottles for each 10-second interval’ or ‘level of coordination
amongst team members’, Roy and Edan identified several factors related to
dyadic entrainment in short-cycle repetitive tasks. They observed different
behaviour, as shelving bottles included two clear roles (giver and receiver).
For instance, while the giver uses visual cues to move the bottles towards the
receiver, the receiver does not often look away from the shelf, simply
assuming that the point-of-handover will be the same for every shelving cycle.
Furthermore, the authors could observe a clear improvement over time in both
the consistency of working speed and coordination, which indicates a better
synchronisation achieved through entrainment. While Roy and Edan’s study
investigated dyadic entrainment in one particular setting (bottle shelving),
these lessons might be transferable to other tasks and group formations.

Rinott and Tractinsky (2021) present a literature review on interpersonal
motor synchronisation-based literature. They propose a framework mapping out
different types of Joint-Action—which does not require any type of temporal
synchronisation as long as all actors work towards the same goal. As part of
this framework, the authors classify ‘Synchronisation’ as the result of
‘Entrainment’: “Entrainment is a process of reaching the same rhythm and is a
required part of synchronising with someone else.” (Rinott and Tractinsky,
2021, Section 2.1). Furthermore, based on existing literature, they synthesise
eleven dimensions (e.g., temporality, information exchange, or the number of
participants) relevant to the study of IMS. Specifically, an important aspect
during the investigation of IMS is the temporal aspect. IMS can be
investigated using external entrainment, such as a metronome (e.g., (Hove and
Risen, 2009)) providing a rhythm, or using ‘Mutual entrainment’, in which
participants entrain with each other. Alternative names for the same phenomena
have been used, including ‘Social entrainment’ and ‘Mutual social entrainment’
(Phillips-Silver et al., 2010).

Previous research has investigated a variety of tasks—such as shelf filling,
walking, or dancing—and how human dyads and groups entrain in these. We argue
that studying tasks resembling industrial context is important to understand
entrainment in this specific context. Furthermore, concrete and context-
specific design considerations can be presented by relating the study of
group-based entrainment to one specific context, thereby optimising human-
robot collaboration for industrial tasks.

###  2.2. Temporally Synchronised Collaboration

Prior research has highlighted positive effects achieved through successful
temporal synchronisation, as a direct result of entrainment (Rinott and
Tractinsky, 2021), amongst human collaborators. Examples include an increased
sense of togetherness (Hove and Risen, 2009), optimised task performance
(Valdesolo et al., 2010), higher likeability amongst collaborators (Hove and
Risen, 2009; Launay et al., 2014), willingness to cooperate (Wiltermuth and
Heath, 2009; Rinott and Tractinsky, 2021), trust (Rinott and Tractinsky,
2021), the sense of connectedness (Wiltermuth and Heath, 2009; Rinott and
Tractinsky, 2021), and rapport (Lakens, 2010; Miles et al., 2009; Lakens and
Stel, 2011).

The desire for increased efficiency during task completion is one of the
primary motivations for collaboration. While this can be achieved through
temporal synchronisation (Valdesolo et al., 2010), efficient collaboration
benefits from other aspects such as a higher willingness to cooperate (e.g.,
(Wiltermuth and Heath, 2009; Rinott and Tractinsky, 2021)). To identify ways
to prevent the free-rider problem during group collaboration, i.e., members of
the group who do not contribute a fair share to the collective effort,
Wiltermuth and Heath (Wiltermuth and Heath, 2009) have investigated the
relationship between the willingness to cooperate and temporal
synchronisation. The authors completed three studies in which participants i)
walked in synchrony, ii) were addressed as a group instead of individuals, and
iii) moved in synchrony. Following each activity, participants cooperated
during experimental tasks. Results showed that in all three studies, the
participants who, prior to the experimental task, were in the synchrony
condition performed better as a group in contrast to the control group.
Thereby, Wilterumth and Heath showed a clear indication of the beneficial
effect on the willingness to cooperate, leading to better results when acting
in synchrony.

Numerous studies have shown that synchronisation with interaction partners
increases the sentiment toward each other. For example, Valdesolo et al.
(Valdesolo et al., 2010) investigated if being synchronised not just
influences perception but also improves performance on specific tasks.
Participants completed a joint action coordination task, requiring the
anticipation of the collaborator’s movement, after rocking on rocking chairs
either synchronously or asynchronously. Following this, participants
collaboratively navigated a steel ball through a labyrinth. Results showed
that pairs rocking in synchrony had a significantly higher sense of similarity
and connectedness but were also more efficient at task completion.

Launey et al. (Launay et al., 2014) present a study in which participants tap
to the beat of sounds, either synchronously or asynchronously. Even though the
partner was not physically present but shown as a video recording,
participants had a higher degree of likeability towards the ‘virtual partner’
when tapping in synchronisation compared to tapping asynchronous.
Interestingly, the study showed that when participants believed that a
collaborator did not make the tapping sound but by a computer, no difference
in likeability was detected depending on if the tapping was synchronous or
asynchronous.

###  2.3. Physical Entrainment in HRC

The occurrence of entrainment has not just been observed in human-human teams.
Prior research has investigated if entrainment arises during collaborative
tasks with robots (e.g., (Breazeal, 2002; Lorenz et al., 2011; Iqbal et al.,
2016; Heijnen et al., 2019; Ansermin et al., 2017; Rea et al., 2019; Yasar and
Iqbal, 2022)).

A study by Lorenz et al. (Lorenz et al., 2011) investigated if the entrainment
occurring between dyads of humans also is observable in dyads of humans and
robots. They conducted two studies, one investigating human-human entrainment
in a simple ‘reach-forth-and-back’ task with human dyads, and a follow-up
study, producing similar results, in which one participant per dyad was
replaced with a robot. This suggests that cooperative task completion can
result in entrainment, even if one of the collaborators is a robot. These
findings are particularly interesting, given the movement speed of the robot.
In the human-human condition, participants entrained to one another and
reached temporal synchronisation, i.e., both partners reached start and end
points simultaneously. However, the robot’s movement speed was about a third
slower than the average movement speed during the human-human task.
Nevertheless, temporal synchronisation between humans and robots occurred,
indicating that the human interaction partner reduced their speed to
synchronise with the robot.

In line with Lorenz et al. (Lorenz et al., 2011), Ansermin et al. (Ansermin et
al., 2017) conducted a study to investigate further if entrainment occurs in
human-robot dyads. To this end, they conducted an entrainment study using the
NAO robot, in which the human interaction partner performs movements in three
conditions. During the first condition, three select movements were conducted
at the participant’s own pace. The second condition added the robot as a
movement partner. Here, the NAO would perform the movement while the
participants were still instructed to perform them at their own pace. Findings
show that all participants were influenced by the robot’s movement speed and
adapted to match it, leading to unidirectional entrainment. In the third
condition, the robot could detect the humans’ movement and adapt its movement
to it, i.e., bidirectional entrainment. These findings show that human-robot
entrainment occurs during body part movement tasks. This was observed for both
unidirectional (i.e., human adapting to robot) and bidirectional (i.e., both
adapting to each other) entrainment.

In contrast to existing work within human-robot entrainment, our study
contributes with a comparative study of pair and group-based (i.e., triads)
human-human entrainment, leading to design considerations for future cobot
design for improved human-robot collaboration.

Figure 2. Triadic setting \- Cubers are placed along the long sides of the
table with a bowl of cubes placed next to them. The bowler is placed at the
end of the table (position B) with the bowl and the collection bin next to
them. The cubers/bowler could choose to place the cubes/bin on their right or
left side. The right side image shows the cropped view of Camera 2 (T9).

##  3\. Methodology

This section describes the study as well as the utilised data collection and
analysis methods. The conducted study is inspired by previous work on human-
human entrainment to inform human-robot entrainment (e.g., (Roy and Edan,
2020; Schneiders and Celestin, 2022; Schneiders et al., 2023)).

###  3.1. Participants

We recruited 50 participants (38 female, 12 male, average age: 22.96, SD:
4.06) with varying educational backgrounds, including health, engineering, and
art, to mention a few. Participants were recruited using social media
postings, flyers on campus, as well as through a dedicated web page. The study
was approved by the Institutional Review Board for Human Participants at
Cornell University (IRB0010723). All participants were compensated with a 50$
Amazon gift card for their participation. We matched participants randomly
into ten triads (N = 30, 1–T10) and ten dyads (N = 20, D11–D20). Additional
participants were recruited for pilot testing.

###  3.2. The Task

####  3.2.1. Task Development and Pilot testing

To identify suitable tasks in which entrainment has a high potential to
naturally occur, we compiled potential ideas resulting in a total of eight
different short-cycle repetitive tasks: envelope stamping, tower building,
food assembly, block sorting, domino brick placing, packing task, drawing
task, and pick-and-placing. After prototyping all eight tasks, we narrowed the
selection down to two viable candidates. The final two candidates were piloted
both in the dyadic as well as the triadic configuration. Based on our
observations as well as the feedback provided by the pilot participants, we
selected one final task. The final task (see Section 3.2.2) was chosen as it
was easy to learn and required collaboration amongst members of both dyads and
triads. Furthermore, the use of a single task was further inspired by existing
research (Roy and Edan, 2020; Lorenz et al., 2011).

####  3.2.2. The Task: Pick-and-Place

Inspired by industrial pick-and-placing, we designed a fast-paced repetitive
task to investigate entrainment between human collaborators. The goal of the
pick-and-placing task was to move small plastic cubes (1$\times$1$\times$1cm)
from a bowl (two bowls in the triadic setting) to a collection bin through
collaborative effort. To accomplish this task, we defined two distinct roles.
The ‘bowler’ and the ‘cuber(s)’. In the dyadic setting, these two roles would
be placed across from one another with a table in between them (position A and
C in Figure 2 left). It was the cuber’s task to pick up one cube at a time and
drop it in the bowl. When precisely one cube was in the bowl, it was the
bowler’s responsibility to move the bowl over the collection bin—placed to the
right or left of the bowler—and drop the cube from the bowl into the
collection bin. Following the emptying of the bowl, the task would repeat.

In the triadic setting, the bowler would sit at the end of the table in
position B (Figure 2 left) and the two cubers would sit to the right and left
of the bowler, respectively (see position A and C in Figure 2 left)111This
submission is accompanied by a video of the task completion by a dyad and a
triad. All participants, shown in the video, have given informed consent to
the use of the video material.. In the triadic condition, exactly one cube per
cuber had to be in the bowl before the bowler could move it to the collection
bin. Other than the inclusion of an additional cuber, the task remained the
same. For both the dyadic and triadic task completion, the division of roles
(who is a cuber and bowler) and the point-of-assembly (i.e., the location
where collaborators’ actions meet), were decided amongst participants. The
point-of-assembly (PoA) is the specific location in which the bowl is
positioned, i.e., placed or held, for the cube(s) to be dropped. For an
example task completion, please see the video provided in the supplementary
material accompanying this paper. The video demonstrates the dyadic and
triadic task completion ($\sim$1 minute each).

Category |  Example questions
---|---
1 |  How would you describe your overall experience with the task?
If we were to ask you to do it again, what would you do differently?
2 |  Did you like your role as either bowler or cuber?
Do you think your task was easier or more difficult compared to the other
role?
3 |  During this collaboration you frequently moved the bowl and the cubes. How did you agree on where your individual actions should meet?
Do you think that your way of coordinating worked well for you? If not – what
would you do differently?
4 |  Did you at any point in the interaction feel uncomfortable? If so – why?
If you had to do this again, would you prefer to work together with a robot or
a human next time? Why?
5 |  You just collectively performed several iterations of the task. Did you feel a change in performance?
Why do you think this change [if a change was described in the previous
questions] happened?
Table 1. Example questions for the post-task semi-structured interviews for
each of the five categories. The interviews were conducted as group-based
interviews including all collaborators.

###  3.3. Experimental Procedure

Upon entering the research lab, participants received a participant
information sheet providing them with key information about the study. This
included details about its duration and overall structure, purpose of the
study, as well as their right to withdraw. This information was then further
explained verbally, and participants were given the opportunity to ask any
questions they had before proceeding to sign the informed consent form.

The data collection process began with gathering demographic information such
as age, gender, and handedness, which was done through a Qualtrics
questionnaire. Next, each dyad or triad of participants was introduced to the
task, and they were given another chance to ask questions before commencing
the task. Participants were instructed to continue the task until interrupted.
As our goal was not to assess whether they performed the task slower or faster
over time, but rather to investigate fluctuations in its completion
consistency, participants were not required to achieve a specific number of
cubes. Instead, the emphasis was on completing the task as efficiently as
possible, with an undisclosed completion time. After four minutes, we
interrupted the participants for the post-task semi-structured interview. The
interview covered five specific topics: 1) their general experience during
task completion, 2) their experience and preference towards the roles they had
chosen, 3) their experiences and strategies for negotiation at the point-of-
assembly, 4) their level of trust towards the other participants, and 5) their
perceived performance. Example questions for each category can be seen in
Table 1. As we used a semi-structured interview approach, ad-hoc follow-up
questions occurred. The post-task interviews were conducted in dyads or triads
to foster insightful conversations about the participants’ experiences during
the task.

###  3.4. Data Collection and Analysis

For this study, we utilised a mixed-method approach combining qualitative and
quantitative measurements. Specifically, we used an OptiTrack camera setup
with 18 cameras for motion tracking of participants’ hands and the position of
the bowl. However, given that the bowl was at all times in the bowlers hand,
we did not use its position for the data analysis and visualisation.
Additionally, we used two video cameras (see Figure 2) to ensure audio-video
recordings of participants for later video inspection related to interpersonal
communication. Following the task completion, we conducted post-task semi-
structured group-based interviews (see Table 1 for example questions).

The first author analysed the interview data using thematic analysis in order
to identify themes relevant to the investigation of entrainment. Using the
motion tracking data, we detected the completion of each iteration of the
task. This was done by detecting the point of rotation of the bowlers hand,
i.e., the moment the cubes were dropped into the collection bin. Furthermore,
the motion tracking data allowed us to visualise dyads and triads performance
consistency. This approach allowed us to identify the number of task
iterations completed during each 10-second interval (Rinott and Tractinsky,
2021). Furthermore, we used motion-tracking to identify shifts in temporal and
spatial variations between task iterations as indicative of successful
entrainment. Lastly, we used the video recordings, for visual inspection using
ELAN (The Language Archive, 2022), to contextualise participants’ reported
experiences.

##  4\. Results

We begin by comparing task performance and demographic measures between dyads
and triads to establish a suitable basis through which to investigate
characteristics of entrainment. Subsequently, this section presents five
findings related to i) indication of synchronisation, ii) different leader and
follower patterns, iii) interpersonal communication, iv) the point-of-
assembly, as well as v) the importance of sensory information.

###  4.1. Comparability: Dyads and Triads

As some of the findings presented are of comparative nature, i.e.,
highlighting differences in dyadic and triadic task completion, it is
important to investigate if dyads and triads are comparable in terms of
performance (i.e., completed a similar amount of iterations) and demographic
distribution. We started by comparing their respective performance (see Table
2), measured using average iterations per group as well as average iterations
per 10 second intervals, as inspired by Roy and Edan (2020). Table 2 shows the
comparison of dyadic to triadic performance. For both dyads and triads, an
iteration was counted from the time the cube(s) were dropped into the
collection bin by the bowler until the next cube was dropped. Two triads where
removed from the data analysis due to technical errors during the collection
of the motion tracking data. With this analysis, we initially aimed to examine
whether there was a performance difference between dyads and triads.

| Average (SD) | Avg. iterations per 10s (SD)
---|---|---
Dyad (N = 10) | 101.9 (27.847) | 4.430 (1.211)
Triad (N = 8) | 99 (25.890) | 4.363 (1.084)
Table 2. Average performance for the dyads (N = 10) and the triads (N = 8)
during task completion. Furthermore, it shows the number of average iterations
for each 10 second interval. In none of the two metrics, a significant
difference could be identified, which is indicative of the groups being
comparable in relation to performance.

Following the performance metrics, we tested whether the distribution of
participants into the two conditions resulted in groups with significant
differences in relation to demographic representation. Results showed that
there were no significant differences among the two groups in terms of gender
($\chi^{2}$(1, N = 44) = 0.489, p = .484), age (F(1,42) = 1.029, p = .316), or
handedness ($\chi^{2}$(1, N = 44) = 0.5843, p = .445). Lastly, we compared the
task performance rate, measured using average completed task iterations pr. 10
second interval (Roy and Edan, 2020) as shown in Table 2, between dyadic (M =
4.430, SD = 1.211) and triadic groups (M = 4.363, SD = 1.084). Results of an
unpaired t-test showed no significant differences in performance between the
two groups, t(16) = 0.124, p = .903.

Given that no significant differences could be observed, the random
distribution of participants into either dyad or triad, or the resulting
performance displayed by the groups, we can conclude that the two conditions
are indeed comparable.

###  4.2. Synchronisation

To investigate the occurrence of entrainment, we analysed the motion tracking
data, see Figure 3. Each dyads’ and triads’ raw data is plotted, with the
solid red line representing the average and the green envelope indicating the
standard deviation of all dyads (left) and triads (right). The average
duration for each task completion of the iteration was 2.26 and 2.29 seconds
per iteration for dyads and triads respectively. The graph illustrates
fluctuations in task completion time for each iteration, specifically how much
faster or slower iteration $i+1$ was compared to $i$, i.e., how fast is the
next iteration compared to the last. A graph with minimal fluctuations, i.e.,
the time each individual task iteration took is more stable, suggests a higher
level of consistency in collaborative rhythm, rather than speed, during task
execution. Through manual video inspection, we removed outliers caused by
participants dropping cubes, which resulted in extreme iteration times.

Based on the motion tracking data, we observed multiple periods, of varying
duration, of increased consistency, indicated through the reduction in
fluctuations in time between iterations. The plotted data led us to
hypothesise that both dyads and triads experience an initial period of
entrainment within approximately the first five (dyads) to six (triads)
iterations222This initial period of entrainment is highlighted using a red
overlay in Figure 3.. This brief duration might be attributed to the task’s
simplicity, reducing the amount of iterations necessary for successful
entrainment. Following this initial entrainment, participants reached a period
of consistent time intervals between individual iterations. We observed
multiple groups breaking their working rhythm (i.e., temporal fluctuations
increase) and attempting to re-optimise their workflow after performing the
task for a period of time (i.e., temporal fluctuations decrease).

Figure 3. 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).

The phenomenon of synchronisation—also referred to by participants as falling
into a ‘rhythm,’ ‘pattern,’ or a ‘groove,’ was not only observed through
quantitative measurements but also directly experienced and described by the
collaborators (e.g., T1, T4, T6, D11, D13). While the majority of groups
noticed that they had fallen into a rhythm, some described it the outcome of
an active and conscious effort. For example, T1 actively made an effort to
coordinate their movements. They, consciously, attributed the occurrence of
synchronisation to this intentional effort. A cuber in T1 for instance
expressed that they:

> ‘[…] in the beginning, we’re trying to coordinate and it reaches towards
> some sort of synchronisation that we [when reached] don’t have to pay extra
> attention as long as everyone’s in the same tempo.’ \- T1

A similar observation was made by the bowler in T8, who expressed that ‘I
think I kind of adjusted to their speed. Because […] at first I think I was
going too fast.’ In the above quotes, collaborators describe the active effort
of coordinating to perform the given task efficiently. Through this effort
‘synchronisation’, a ‘rhythm’, or a ‘groove’ is reached, allowing them to
complete the task without paying active attention to this coordinating. The
participant talked about ‘synchronisation’ as being related to the tempo in
which group members perform their individual sub-task. Using the video
recordings, we observed that reaching synchronisation allowed participants to
start conversing—both on- as well as off-topic (see Section 4.4). Once a
common rhythm had been established, collaborators did not perceive the need to
adapt further, indicating that a state of effortless collaboration had been
reached. Contrasting the quote from T1, other triads observed (e.g., T4, T5,
T6) that the temporal synchronisation ‘just happened’, implying the absence of
conscious effort to reach a synchronised state. Still, synchronisation
happened nonetheless, as expressed by e.g., T5: ‘I think once we got in the
rhythm…Yeah. Then we were able to kind of just keep going.’.

###  4.3. Leader and Follower

The data analysis revealed two distinct types of leader-follower patterns:
static and absent. Interestingly, this finding showed clear differences
between the dyadic and triadic strategies employed. During most dyadic—7 out
of 10 with the exception of D11, D14, and D16—collaboration, we could observe
a strong pattern emerge focusing on a static leader—which was defined by the
task at hand—namely the cuber. The static pattern is characterised by the
absence of change on who is leading the collaboration. Most participants in
the dyadic configurations perceived the cubers task as more difficult, making
this task the bottleneck which limited the pace at which the group could
perform. Given that this task was being perceived as slower, the bowler would
typically be ready at the centre of the table before the cuber would be ready
with the next cube. Therefore, the cuber was often perceived as the leader
during the dyadic collaboration, setting the collaborative pace to be
followed. As expressed by D15: ‘I like to believe that I set the speed […] I
don’t recall waiting for the bowl very often’. Seven of the ten dyadic groups
reported similar observations, describing the slower sub-task performer to be
leading. Contrasting these seven groups, two groups (D14 and D16) described
the absence of a distinguishable pattern. They describe that each dyad member
was seemingly independent of one another, completing their task. The dyads
found a rhythm that led them to complete their sub-task at the same speed
without needing a dedicated ‘leader’. This was expressed by, e.g., D14: ‘I
don’t think so. Okay, I think they seem pretty equal.’.

Contrasting the dyads, the triads were less explicit about the presence of a
leader. Given that each person interacted with two other collaborators instead
of only one, the presence of two leaders was a possibility, as expressed by
the bowler in T1. Here, the bowler followed both cuber’s behaviour, who were
leading the interaction and providing the rhythm to which the bowler adjusted
their pace. Due to the synchronisation between collaborative efforts of the
cubers, the two leaders, i.e., the cubers, were perceived as providing one
unified collaborative rhythm by the bowler.

> ‘I feel I [the bowler] kind of am the follower for their collective
> behaviours, because I was trying to match the tempo. But also in this case,
> because I personally see the dropping of the cube as a cue, an audio cue on
> what the rhythm is.’ \- T1

Finally, some groups described the absence of a dedicated leader. This was,
for instance, expressed by one of the cubers in T8 who stated that: ‘I don’t
think there is a leader role, but I kind of follow her because I think she put
the cube in first, and then I will follow.’ However, as evident from the
quote, it can be argued that—even though the cuber does not call it a leader
role—they are still following the other cuber.

###  4.4. Interpersonal Communication

To investigate differences in communication between dyads and triads, we used
the recorded audio-video data. Each video was coded by hand to identify the
occurrence of speech as well as the topic discussed. We counted each time a
verbal interaction occurred. A new interaction was characterised by a change
of topic from one category to another or after a conversation was started
following more than five seconds of silence. As with the previous finding (see
Section 4.3), we were able to observe difference between the two
configurations. The distribution of communication, for dyads and triads, by
different topics can be seen in Figure 4.

Figure 4. 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 Table 2) compared to the triads who focused conversation on the
task at hand. This could be indicative of the dyadic collaboration requiring
less mental workload.

While both dyads and triads conversed during task completion, we observed one
key difference between communicative behaviour. As shown in the Pareto
diagrams (Figure 4), the first two bars (i.e., ‘Small talk’ and ‘General task
related’) are reversed in order between the dyads and triads. Specifically,
the most frequent topic of communication for dyads was task unrelated ‘small
talk’ (33%), contrasting only 19.7% of small talk in the triadic setting
(e.g., ‘Have you watched Gravity Falls [Netflix show]?’ \- T5). This ordering
was switched, as ‘general task-related’ communication (e.g., ‘I think I just
dropped one cube [on the floor]’ \- D13) was the most common topic of
conversation during the triadic task completion (40.7%) and the second most
frequent for the dyads (25.4%). Results showed a significant difference among
the two groups in terms of ‘Small talk’ to ‘General task related’ conversation
($\chi^{2}$(1, N = 20) = 5.991, p = .0144).

###  4.5. Point-of-Assembly

While entrainment primarily refers to the temporal synchronisation of actions,
the spatial aspect is equally important. As a measure of this, we investigated
two aspects of the collaboration. Firstly, the consistency of the point of
assembly (Section 4.5.1), and secondly, the consistency of the participants
hands trajectories and how this consistency—or the absence of it—evolved over
time (Section 4.5.2).

####  4.5.1. Consistency in Point-of-Assembly

We identified two different strategies utilised to negotiate the point-of-
assembly (PoA). The PoA refers to the specific spatial location where the bowl
is positioned, i.e., moved to or held, for the cube(s) to be dropped. Through
examination of the video footage and further elaboration through the
interviews, we discerned two primary strategies employed when approaching the
PoA: 1) optimisation for group efficiency, and the 2) optimisation of
individual task.

For strategy 1), we observed the tendency to prioritise adjustment of the
individual task, while not emphasising the own ease of task completion, but
rather optimise for the other collaborators. This strategy was motivated by
the intent to increase overall group efficiency by adjusting one’s own task to
facilitate one’s collaborators. Several dyads and triads (e.g., T2, T4, T8,
D12, D16) expressed this behaviour. The bowler in T4, for instance, used this
strategy to accommodate the two cubers: ‘They [the cubers] were putting the
cubes down at the same time. So I just need to place it in the middle [to make
it easy for them].’. A similar approach was described by the bowler in T2 who
optimised the placement of the bowl to increase overall group efficiency. Even
though this increased the range of motion needed by the bowler, it reduced the
task complexity for the two cubers.

> ‘I also optimise the location of the bowl, and then we can move it further
> [away from the bin, i.e., from the cubers end-point] because on a global
> sense, that would be the best strategy considering both of your arms [the
> cubers]’ \- T2

A different, albeit less frequent, approach was observed in which several
participants of both roles reported on the optimisation of their own
task—while de-emphasising the benefit to the other collaborators. This change
in strategy, has the potential to create a ripple effect, influencing the
behaviour of other participants and ultimately enhancing the overall
efficiency of the group’s tasks.

In, e.g., D11, the bowler describes reducing the distance between the bowl and
bin to increase the ease of dropping the cubes. This adjustment, to make their
own task easier, resulted in an increase in distance for the cuber, i.e.,
making the cubers task more demanding. Nevertheless, while the motivation was
to ease one’s own task, the collaborators described an overall efficiency
increase, as the adjustment resulted in an optimisation of the bowler’s task,
which in this specific dyad was perceived to be the slower task:

Figure 5. 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 (◼) and purple
(▼): left and right hand of the bowler. Light blue (▲) and green (◗): left and
right hand of cuber one. Dark blue (♠) and orange (✚): left and right hand of
cuber two.

> ‘…[the cuber] should be the one who decides speed and I can adjust to it.
> […] later, I found out, I’m actually the slower one. So then, I just moved
> the bowl near me so that it’s easier for me to put [the cubes] in.’ \- D11

Similar findings were observed in the triadic setting. In T5, for instance,
the bowler reaches the same conclusion by adjusting the position of the bowl,
aiming to minimise the distance to the bin, thereby making the task easier for
themselves. This adjustment reduces the amount of movement required for their
own task, however, while the motivation was the improvement of ones own task,
this may or may not lead to a decrease in ease of task completion amongst the
collaborator(s).

> ‘[The bowl] was more to the side [towards the bin], but I wouldn’t say it
> was too far. It’s still okay. […] This might have been inclined towards the
> bin side, in this case, to my left side’ \- T5

While groups using either strategy, performed these actions in order to
optimise the efficiency, the difference was the targeted efficiency increase,
i.e., the individual or the groups. Whereas the adjustment of own behaviour to
facilitate collaborators prioritises an improvement to group efficiency, by
reducing the collaborators workload, the second approach achieves this only as
a potential side effect resulting from the individual optimisation. Regardless
of the strategy utilised, both dyads and triads (e.g., T1, T3, T5, T7, D11,
D12) highlighted the importance of consistency to achieve high task
efficiency. This finding contrasts with the expected main contributor to
efficiency—speed. Higher consistency led to greater predictability in motion,
making it easier to anticipate the PoA and resulting in less downtime during
task completion. This was, for instance, expressed by the bowler in T3, as
described below.

> ‘[…] I keep on doing this sweeping motion, their goal is to put it [the
> cube] in the bowl. But then the goal point keeps on moving. It might lead to
> more confusion. So I thought it’d be better […] just keep it constant so
> that they can predict the next move.’ \- T3

The here described attempt at using a sweeping motion for the bowl can be seen
in Split 5 in Figure 5 (purple ▼). However, after initial experimentation with
a moving PoA, the group focused on a static PoA to increase predictability.

In addition to participant descriptions, the consistent PoA selection was
evident from the video analysis. After, e.g., T4 had found a position that
worked for them—the bowl slightly closer to the side of the bin—it was clear
to see that an effort was put into keeping the PoA as consistent as possible.
Figure 1 illustrates the bowl placement for T4 over the course of 60
iterations $(24\rightarrow 44\rightarrow 64\rightarrow 84)$.

####  4.5.2. Consistency in trajectories

In addition to the consistency of the PoA, we further investigated the
consistency of trajectories and the development thereof over time. To
illustrate this, we visualised the collected motion capture data and created
plots of the trajectories from two different angles (x-y and y-z perspectives)
in windows of 24 seconds each (leading to 10 windows for the 4 minute task
completion). Figure 5 presents one example for a triad (T3) illustrating the
hand trajectories of the six hands of the three collaborators over the course
of the four minutes. Similar trends were found in other dyads and triads. The
division into 24 second windows was chosen as it allowed for effective
visualisation of the task over time, leading to us being able to observe
variations in consistency of participants trajectories throughout the course
of the task completion.

From the presented time series, it became apparent that the behaviour of each
member of the triad changed during the four-minute task completion. Initially,
all three participants used both hands—albeit not necessarily to the same
extent, as evident from the trajectories presented in Figure 5. Each
collaborator is represented with two colours, one for each hand. Specifically,
cuber one: green and light blue ( ◗ / ▲ ), cuber two: orange and dark blue ( ✚
/ ♠ ), and the bowler: purple and yellow ( ▼ / ◼ ). However, within the first
two minutes of the task, all three participants transitioned to using only one
hand, indicated by the disappearance of the green, orange, and yellow
trajectories. However, cuber two shortly after switched hands, as seen in
split 6 to 9, only to switch back to dark blue in split 9/10. Following split
5, both the starting and ending points of the trajectories, as well as the
paths taken, became increasingly consistent. These trajectories are indicative
of each participant finding a working rhythm for their part of the task.

###  4.6. Multisensory Information

An additional characteristic identified through the study, and highlighted by
both dyads and triads, is related to the dependence on, at times non-obvious,
sensory information. While the task described required tactile and visual
information, i.e., feeling the cubes and seeing where to drop them, the
importance of auditory information was observed during the video analysis and
described by participants in the post-task interviews.

While the use of visual and tactile information was expected, most groups
(e.g., T1, T5, T8, T9, D12, D17) further relied on auditory cues produced by
the task. Here, the benefit of the auditory signalling was expressed in
multiple directions, specifically both to and from the bowlers’ task. As it
could at times be difficult to see if the small cubes were dropped by the
cuber(s), several bowlers reported using sound to confirm that the cubes were
in fact in the bowl. In addition to the visual cue of seeing the cuber(s)
hand(s) over the bowl, the auditory cue further provided information about
when the bowl could be moved towards the bin, thereby freeing visual cues as
the conveying information did not, exclusively, rely on visual inspection.
Audio cues were relevant in both the dyadic as well as the triadic task
completion.

> ´[…] also auditory. Yeah, listening. Yes. Because I would look, but I also
> make sure that I heard something.’ \- T8
>
> ‘When she [the cuber] dropped the cube into the bowl, that’s when I knew
> it’s ready to dump it [into the bin].’ \- D20

During the video inspection, we could observe that bowlers stopped the
interaction when they missed the bin during the emptying of the bowl. This was
due to the absence of audio cue, as the cubes did not hit the bin, but landed
on the carpet (see supplemental video material $\sim$ 00:07–00:13 (dyadic) and
01:17 (triadic), not producing the distinct sound the bowler was listening for
as confirmation of successful task completion.

Just as the bowler used audio cues to know when to proceed to the next step of
their task, so did the cuber(s). In the cuber(s) case the acoustic signal used
was caused by the bowler dropping cubes in the bin next to them, thereby
signalling the end of the iteration. This sound of falling cubes would signal
the cuber(s) that the bowl is about to return to the PoA to collect the next
cube(s).

> ‘Sound. Definitely! I think, whether I knew it or not, I think I was cue’ing
> into that sound of dum dum [sound of cubes dropping into the bin].’ \- T5

These findings highlight the value of multi-modal signalling during task
completion. The use of, e.g., acoustics, frees other senses for the
preparation of the next iteration, while simultaneously providing feedback
about the progression of the iteration.

##  5\. Discussion

In this paper we have presented a mixed-method study focusing on entrainment
during task completion in human pairs and groups. This paper contributes to
the HCI and HRI literature, by investigating characteristics of entrainment
during dyadic and non-dyadic collaboration. This section will discuss some of
these findings in relation to existing literature and present three design
considerations for the design and improvement of human-robot collaboration
(HRC) using collaborative robots (cobots).

###  5.1. Differences in Interpersonal Communication

As highlighted in Section 4.4, our study showed that participants in both
conditions, i.e., dyads and triads, communicated while performing the
collaborative task. We categorised this communication into several different
topics such as ‘Small talk’, ‘General task related’ or ‘Change of Strategy’,
and while we could observe that both conditions talked about the same topics
(i.e., categories), differences in frequencies were observed. Here it is
particularly interesting to note that the dyadic condition had a significantly
higher amount of off-topic conversation (e.g., movies or weekend plans) over
task-related conversation compared to the triads (33% compared to 19.7%).
While we did not collect evidence about perceived task complexity, we have
presented evidence (see Section 4.1) that indicates that no significant
differences in performance was identified between conditions. The consistency
in average iterations between dyads and triads is indicative of a natural, or
comfortable, frequency—regardless of the group formation. While the
performance is the same, the degree of off-topic conversation increased by
13.3 percentage points in the dyadic configuration.

A potential hypothesis leading to this could therefore be related to a lower
mental workload required for the task in the dyadic configuration compared to
group-based collaborations. This would allow a higher degree of non task
related conversations without impacting the task performance. To investigate
this, and be able to confirm or refute this hypothesis, a follow-up study
utilising subjective instruments such as the NASA-TLX (Hart, 1986) or the
(simplified) subjective workload assessment technique (Luximon and
Goonetilleke, 2001), performance based mechanisms, such as the tone detection
task (Butterfield and Metcalfe, 2006), or strategies relying on physiological
data such as measured through EEG or fNIRS (Aghajani et al., 2017) are needed.

An alternative explanation to the change in communication patterns might be
related to the many minds problem as presented by Cooney et al. (2020). In its
essence, the authors argue that the move from a dyadic configuration to a
group based setting, such as a triad, changes the way in which we
communicate—and about what—considerably. In the here presented study, the
observations presented in relation to the type of communication (see Section
4.4) might be related to the many minds problem, i.e., when people interact in
larger groups, they are less likely to disclose personal or private
information and opinions (i.e., engage beyond the task at hand) as they are in
dyadic configurations.

###  5.2. Robot Adaptability

Typical industrial robots, such as those used in the automobile industry, are
characterised by their ability to perform very effectively given constrained
repetitive tasks that require no adaptation and minimal human intervention.
However, with the increase in un-caged collaborative robots, humans and robots
are beginning to collaborate in close proximity (e.g. (Sauppé and Mutlu, 2015;
Cheon et al., 2022a, b)). While previous research has shown that humans can
entrain on robots (Lorenz et al., 2011; Rea et al., 2019), we argue that this
can be improved upon by giving robots a higher sense of awareness of their
collaborators. This would allow the robotic collaborator to adjust to the
humans’ pace. This robots awareness of the human collaborators performance,
has the potential to led to a higher degree of adaptation from robot to
human(s), ultimately strengthening the collaboration as it becomes
bidirectional entrainment, instead of unidirectional. While this would be
novel for collaborative robots, similar effects have already been documented
for interaction with social robots (Ansermin et al., 2017). We argue that, as
demonstrated in the human collaboration presented in this paper, providing
collaborative robots with a better sense of awareness regarding elements such
as collaborator’s movement speed, performance, or deviations in the PoA, this
could enhance the robots’ ability to adapt, and in turn collaborate, with in
mixed human-robot configurations. This possibility of bidirectional adaptation
($Humans\leftrightarrow Robots$), contrasting humans one sided adaption to a
non-adaptive robot, can help to increase the level of collaboration
(Christiernin, 2017) between humans and robots.

As presented in this paper, participants experienced the occurrence of
synchronisation. While variations between strategies for reaching
synchronisation (described as ‘groove’ or ‘rhythm’) occurred, the motion
capture data in conjunction with the collaborator’s described experiences are
indicative of this. From this, we derive our first design consideration for
future research focusing on collaboration between cobot(s) and human(s).

> Design Consideration 1: Designers should consider how collaborative robots
> can adapt their behaviour based on human collaborators’ fluctuations in
> performance, speed, and PoA deviations.

###  5.3. Noise is not always Noise

Within industrial tasks such as production, manufacturing, or warehousing,
noise is typically perceived as an undesirable presence having potentially
harmful implications on workers’ health (Rabinowitz et al., 2011). Especially
given the nature of this task, which was inspired by an industrial pick-and-
placing task, one would typically be interested in reducing noise. Therefore,
it stands to reason that the reduction of noise would be a desirable goal. In
this study we demonstrated, that subtle acoustic signals—specifically noise
related to the task at hand (i.e., the sound of the dropping cubes)—allowed
the collaborator’s visual attention to be directed at other parts of the task
while still receiving feedback on the progress of the collaborative effort.

In this study, we investigated mutual entrainment (Rinott and Tractinsky,
2021), meaning we did not provide any external stimuli, such as a beat, to
which to entrain. However, the pick-and-placing task produced intrinsic
acoustic feedback on multiple steps. Specifically, the cube(s) dropping into
the bowl and from the bowl into the bin. As presented in Section 4.6, this
auditory cue provided a rhythm to which collaborators structured their task.
Specifically, the auditory cue informed the bowler that the bowl can be moved
to the bin as they hear the cubes land in the bowl, and the cubers know that
the next iteration has started once they hear the cubes drop in the bin. As
not every task produces task intrinsic auditory cues, the addition of task-
relevant acoustic signals could aid the collaborators, humans and robots, by
giving additional stimuli to which to entrain to. Previous research has shown
that auditory signals can be perceived as useful during robot approach
behaviour (Lohse et al., 2013), industrial collaboration (Sauppé and Mutlu,
2015), or to increase positive sentiment toward robots (Thiessen et al.,
2019). In this paper, we argue that task-relevant sounds during human-robot
collaboration might provide additional stimuli for entrainment during
collaboration. Based on these findings, we derived our second design
consideration for collaboration with cobots.

> Design Consideration 2: Designers should consider how collaborative robots
> can utilise sensory channels to provide feedback emphasising key events
> during the collaboration.

###  5.4. Short-term Consistency is Key

A frequent observation was the emphasis on consistency leading to
predictability. The importance of legibility in human and robot motion, i.e.,
the ease of reading and inferring the goal based on observed motion, to
identify the robot’s intent, has been emphasised in previous research (Dragan
et al., 2013). Based on our findings, we argue that collaborative robots need
to be consistent within the short-term, i.e., between individual iterations,
while allowing for long-term adaptations, allowing for change in performance
over the course of several iterations. Naturally, what constitutes ‘short-
term’ and ‘long-term’ varies greatly depending on the task. In the here
presented task collaborators were able to perform each iteration within 2.27
seconds while other tasks (e.g., the assembly of several sub-components) might
take significantly longer to complete. This is particularly important in tasks
in which the robot initiates the task, i.e., is defining the pace at which the
collaborations occurs. We, therefore, argue that the robot’s movement and
approach to solving a given task should not only be legibly—but that the need
for legible behaviour can be reduced through short-term consistency as
observed during human-human task completion (see Section 4.5).Based on this,
we pose our third design consideration, emphasising the need for consistency.

> Design Consideration 3: Designers should consider how collaborative robots
> can exhibit short-term consistency, allowing them to be consistent between
> iterations, while also allowing for long-term adjustments based on the human
> collaborators behaviour.

###  5.5. Limitations and Future Work

As this lab study was conducted in a controlled environment, no external noise
was present. However, the task chosen, resembling an industrial pick-and-
placing task, is typically performed in environments with high potential for
machinery producing noise. Therefore, the value and desirability of auditory
cues, as presented in Section 4.6 and used as the foundation for the second
design consideration (see Section 5.3), might be affected by the presence of
other auditory information. To identify the usefulness of auditory information
in noisy environments, a follow-up study focusing on ecological validity needs
to be conducted. Furthermore, as this was a novel task for the participants,
different observations might occur when investigating this in a longitudinal
context.

Future work includes implementing and evaluating the proposed design
considerations to assess their effect on human-robot entrainment in different
configurations (e.g., dyads and triads). A follow-up study will have the
potential to highlight the usefulness of the proposed design considerations in
order to achieve synchronisation amongst humans and robots. This could be of
particular interest in collaborative settings where the maximum working speed
between humans and robots is not well aligned. Furthermore, this could provide
insights into the side effects of human-robot entrainment during industry-
inspired, fast-paced, collaborative tasks in group configurations dyadic
setting investigated in current research. An additional future research
direction could involve comparing cognitive workload during the collaboration
between dyads, triads, and larger groups (as described in Section 5.1).
Additionally, a quantification of the here presented findings identifying
exactly when entrainment has occurred might make the findings presented in
this paper more actionable. Lastly, the verification of the findings presented
in this paper through the investigation of different tasks could be
beneficial. The authors of this paper are currently investigating the data
from a follow up study using the ‘envelope stamping’ task, which was piloted
and mentioned in Section 3.2.1.

##  6\. Conclusion

In this paper, we present the findings of a mixed-method laboratory study that
investigates how human dyads and triads synchronise with each other
temporally. To achieve this, we designed a fast-paced, short-cycle repetitive
task inspired by an industrial pick-and-place scenario. We collected empirical
data through a mixed-method approach, which involved interviews, audio-video
recordings, and motion tracking of collaborators’ hands and objects of
interest.

Overall, we observed strong spatial consistency within participant groups,
especially at the point of assembly, along with minimal temporal fluctuations
in task performance, indicating the occurrence of entrainment. Specifically,
we outline five key characteristics of how human dyads and triads entrain with
each other. These characteristics relate to the occurrence of synchronisation,
variations in leader-follower dynamics, distinctions in communication between
dyads and triads, the significance of the point of assembly, and the impact of
unintended noise generated by the task. Finally, we discuss three design
considerations that will inform future research in the field of human-robot
collaboration.

###### Acknowledgements.

We would like to express our gratitude to the study participants for their
participation. Furthermore, we would like to express our thanks to the
additional participants who helped with the pilot testing of the tasks. This
work was supported by the Engineering and Physical Sciences Research Council
[grant number EP/V00784X/1] UKRI Trustworthy Autonomous Systems Hub and the
Office of Naval Research [N00014-19-1-2299]. Any opinions, findings, and
conclusions expressed in this material are those of the author(s).

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