RESEARCH ARTICLE
In Good Company? Perception of Movement
Synchrony of a Non-Anthropomorphic Robot
Hagen Lehmann*, Joan Saez-Pons, Dag Sverre Syrdal, Kerstin Dautenhahn
School of Computer Science, University of Hertfordshire, College Lane, Hatfield, Hertfordshire, AL10 9AB,
United Kingdom
* hagen.lehmann@iit.it
Abstract
OPEN ACCESS
Citation: Lehmann H, Saez-Pons J, Syrdal DS,
Dautenhahn K (2015) In Good Company? Perception
of Movement Synchrony of a Non-Anthropomorphic
Robot. PLoS ONE 10(5): e0127747. doi:10.1371/
journal.pone.0127747
Academic Editor: Mikhail A. Lebedev, Duke
University, UNITED STATES
Received: July 25, 2014
Accepted: April 18, 2015
Recent technological developments like cheap sensors and the decreasing costs of computational power have brought the possibility of robotic home companions within reach. In
order to be accepted it is vital for these robots to be able to participate meaningfully in social
interactions with their users and to make them feel comfortable during these interactions. In
this study we investigated how people respond to a situation where a companion robot is
watching its user. Specifically, we tested the effect of robotic behaviours that are synchronised with the actions of a human. We evaluated the effects of these behaviours on the robot’s likeability and perceived intelligence using an online video survey. The robot used was
Care-O-bot3, a non-anthropomorphic robot with a limited range of expressive motions. We
found that even minimal, positively synchronised movements during an object-oriented task
were interpreted by participants as engagement and created a positive disposition towards
the robot. However, even negatively synchronised movements of the robot led to more positive perceptions of the robot, as compared to a robot that does not move at all. The results
emphasise a) the powerful role that robot movements in general can have on participants’
perception of the robot, and b) that synchronisation of body movements can be a powerful
means to enhance the positive attitude towards a non-anthropomorphic robot.
Published: May 22, 2015
Copyright: © 2015 Lehmann et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information files.
Funding: This research was funded by the European
Commission under the 7th Framework Programme
for Research, ACCOMPANY Project - Grant
Agreement No: 287624 (KD), http://
accompanyproject.eu/.
Competing Interests: The authors have declared
that no competing interests exist.
Introduction
Research in social robotics and Human-Robot Interaction (HRI) has recently focused on building robotic companions capable of fulfilling a range of assistive functions [1] including healthcare and support for elderly people in their own homes. This development is also reflected in
the increasing EU funding for projects specifically dedicated to companion robots [2, 3, 4].
One of the reasons for this process is the worldwide demographic change [5]. The percentage
of elderly people in our societies has been increasing during the last decades and will continue
to increase for the foreseeable future. This confronts our social and health systems with financial difficulties. One solution could be a combination of companion robots and smart home
technology in private households to enable elderly people to live independently for longer in
their own homes.
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Companion robots could be used to remind people to drink fluids or to take their medicine
regularly, keep them company and to alarm care personnel in case of potentially dangerous situations like injuries due to falling. In order for robots to be consented to in such scenarios, they
need to be able to interact in a meaningful, predictable, and socially acceptable manner with
their human users (see definition of companion robots in Dautenhahn, 2007 [1]).
At the University of Hertfordshire we have been studying since 2004, as part of the EU projects Cogniron [6], LIREC [7] and ACCOMPANY [2], how robot home companions should behave towards and in the presence of people [8, 9, 10, 11, 12]. One particular question that has
emerged during this research concerns how robots should behave during time periods in
which the user is engaged in activities like reading, watching TV, and preparing a meal. Without a concrete task to do, the robot could return to its charging station, however, in its role as a
companion robot, shall it ‘keep company’ with its user for example, in a pet like way, i.e. by
standing nearby and non-intrusively observing and non-verbally responding to the user’s behaviours, ready to engage if the user has finished her current activity? How would people perceive such behaviours from a companion robot? Would ‘being watched’ by a machine be
perceived as threatening?
In order to address these issues, we designed a study whereby a non-anthropomorphic robot,
the Care-O-bot3, watched a person being engaged in a physical manipulation task. The behaviour of the robot towards the person was designed based on the concepts of head gaze, which
plays an important role during human-human interaction and joint attention. We tested how
different forms of synchronised head gaze movements were perceived, hypothesizing that the
ability for a robot to appear engaged in tasks that normally require joint attention in humanhuman interaction would facilitate the robot’s social acceptance and perception as a social entity.
The article is structured as follows. First, we discuss the relevant background information
and motivate our research questions. Next, we describe our experimental setup, including the
robotic platform we used, and provide an overview of our methods. We then present our results and discuss them in relation to other findings in the research field, acknowledging limitations of the work and pointing out directions for future research.
Background
Anthropomorphisation
Many of the currently available social robots are to a certain degree anthropomorphic [13, 14,
15, 16]. If they are not humanoid they usually have at least a part that loosely resembles a
‘head’ with facial features like ‘eyes’. The reason for this is that for robots incorporating such
features it is easier to facilitate social interactions with humans, because their appearance helps
them to directly emulate aspects of human-human social interaction dynamics [17]. Nevertheless, humanlike appearance by itself does not ensure comfortable HRI. Another very important
aspect is the movement of the robot. The more naturalistic the movements of the robot are, the
more comfortable will the interaction be [18]. Unnatural movements and behaviours, specifically in anthropomorphic robots, cause them to fail the expectations triggered by their appearance and to fall into the (hypothesised) Uncanny Valley [19, 20, 21].
One way to avoid this effect would be to equip robots with sensors and actuators that allow
them to flexibly exhibit appropriate human-like behaviour. Unfortunately this would require
deep models of cognition and emotion, which at the current state of technology cannot easily
be implemented yet. A practical alternative is to provide the robots with external expressions
typical of humans during a social interaction. These expressions are typically composed of
body movements, gestures and speech. Our decision to use body movements, specifically
movements resembling head gaze, was owed to the physical affordances of the Care-O-bot3
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and the role gaze following behaviour plays for humans during cooperative, mutualistic social
interactions [22].
Gaze is the main non-verbal source of social information between individuals, who understand each other as intentional agents with emotional states. In order to develop artificial systems with which humans feel comfortable interacting, it is necessary to consider the
mechanisms of human gaze behaviour and its related movements [23].
Synchronisation of behaviour
The perception of appropriateness of gaze during social interaction depends strongly on timing. The synchronisation of ones own movements with the actions and movements of others is
as important as their naturalistic appearance for meaningful and comfortable interaction [24,
25, 26]. There are different ways behavioural synchronisation can be used in experiments—
movement synchronisation, synchronisation of direction (opposite—same), and synchronisation focused on a specific object or location. It could be argued that positive, object centred synchronisation represents a form of object centred joint attention. In developmental psychology
this form of joint attention is seen as a prerequisite for social learning [27, 28] during ontogenetic development. It represents a very basic mechanism that allows humans to interact goal directed and purposefully.
When employing a robotic system with a set of limited torso movements in situations involving social interaction, it is important to consider the synchronization of these non-verbal
behaviours with the behaviours of the user. In HRI synchronized movements can be also referred to as congruent or contingent movements [29]. The synchronization can be positive or
negative. ‘Positive’ means in this context that the robot follows the movements and actions of
the human, generating the impression of being attentive, and ‘negative’ means the robot is
doing the exact opposite movements of the human.
Research Questions and Expectations
Our study aims to answer three research questions:
• Research Question 1 (RQ1): Are synchronised movements of a non-anthropomorphic robot
sufficient to influence participants’ perception of the robot?
• Research Question 2 (RQ2): If RQ1 can be positively answered, what role does the direction
of the synchronisation play?
• Research Question 3 (RQ3): Are the possible effects found in RQ1 and RQ2 influenced by
factors like age, gender and prior experience with robots?
Concerning RQ1, we expected that humanlike movements such as synchronised head gaze
following would induce an emotional reaction in the user, even when confronted with a robot
without a clearly distinguishable head. This expectation is based on the hypothesis that humanlike movements are more important than humanlike appearance [19].
In case of RQ2, we expected that the robot would be perceived more friendly and likeable,
when exhibiting movements that are positively synchronised with the actions of the user. We
hypothesise that even with its limited social interaction features, Care-O-bot3 will be perceived
as likeable and friendly if it exhibits certain behaviours (i.e. “head” gaze) in positive synchronisation with the actions of the user.
For RQ3 we expected the potential effects to be fairly robust with regard to demographic
factors. Prior research with other robotic platforms and different settings has shown only little
influence [30, 31, 32].
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Methods
Ethics statement
The research was approved by the University of Hertfordshire’s ethics committee for studies
involving human participants (under protocol no. a1112/161). The participants provided their
informed consent before seeing the videos and responding to the questions.
Materials
We conducted an online survey, in which we presented three pre-recorded one-minute videos,
in a random order, each followed by a questionnaire. The survey consisted of three parts (see
Annex 1 for a full version). The first part briefly introduced the tasks and included the ethics
approval and consent form. The second part contained demographic questions and the last
part included the actual study showing three video conditions in a randomised order followed
by the evaluation tools. We have used the Video-based HRI (VHRI) methodology reliably in
several human-robot interaction studies in the past [9, 33, 34, 35]. In a direct comparison of
live HRI and Video-based HRI we found comparable results [36, 37].
Small-scale pilot and validation studies were carried out in order to determine the most appropriate camera perspective for the final online survey [38]. We tested two different settings.
For the first setting we chose a camera angle that allowed observing the entire interaction and to
see both the robot and the ‘user’, i.e. the actor shown in the video. For the second setup an “overthe-shoulder” perspective was chosen, allowing the observation of the movements of the robot
from the front and only the movements of the hands of the user (see Figs 1 and 2). The results
from the pilot and validation studies showed that participants were capable to distinguish the different robot behaviours and rate them accordingly in both perspectives. For the final study, we
decided to use the over-the-shoulder perspective considering that it would allow for a much better control of confounding variables, i.e. participants were able to be more focused on the movements of the robot, without being distracted by contextual information such as the gender, age or
ethnic background of the person shown in the video. The final perspective can be seen in Fig 2.
The videos were produced in the living room of the University of Hertfordshire’s Robot
House, a facility dedicated to HRI research in a realistic, domestic environment. The Robot
House has the appearance of an ordinary, fully-furnished, British suburban house (Fig 3). The
house is populated with different robot companions, which are integrated into a sensor network in order to create a smart home environment.
The robot involved in this research was Care-O-bot3, a non-anthropomorphic service robot
developed by the Fraunhofer Institute for Manufacturing, Engineering and Automation [39].
Fig 1. Experimental setup from side perspective showing the robot and the ‘user’.
doi:10.1371/journal.pone.0127747.g001
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Fig 2. Final experimental setup showing the “over-the-shoulder” perspective.
doi:10.1371/journal.pone.0127747.g002
This robot is an omnidirectional platform equipped with a 7 degrees of freedom (DOF) KUKA
lightweight arm and the 7 DOF Schunk Dexterous Hand with integrated tactile sensors. Its
torso is mounted on a moveable platform, the KUKA industrial arm is located on its back and it
has an extendable tray on the front. The robot also has two cameras on the upper end of the
torso, a set of differently coloured LED lights on its front and synthetic text to speech. Due to its
non-anthropomorphic appearance it cannot generate complex human-like gestures—it lacks a
face, a clearly distinguishable head, a humanoid body and arms and hands for gesturing. Nevertheless, most of Care-O-bot3’s mobility is based on the torso which allows it to perform basic
movements such as bending forward and twisting the torso to the sides. We decided to use
these turn and forward motions during the experiment, to simulate gaze following behaviour.
Measures
Our analysis of the data is based on two standardised, validated measures. The first is the Godspeed Questionnaire created and validated by Bartneck et al. [40]. The second is the Inclusion
of Other in Self (IOS) scale, as described in Aron et al. [41].
The Godspeed Questionnaire. The Godspeed questionnaire was devised as a HRI-specific
measure of participant perceptions across several dimensions, where each dimension is addressed using a set of semantic differential scale [40]. Due to the constraints of an online study
Fig 3. “Robot House” at the University of Hertfordshire.
doi:10.1371/journal.pone.0127747.g003
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Table 1. Semantic pairs for Godspeed Scale dimensions used.
Likeability
Perceived Intelligence
Dislike—Like
Incompetent -Competent
Unfriendly—Friendly
Ignorant—Knowledgeable
Unkind—Kind
Irresponsible—Responsible
Unpleasant—Pleasant
Unintelligent—Intelligent
Awful—Nice
Foolish—Sensible
doi:10.1371/journal.pone.0127747.t001
in which we needed to have a brief questionnaire, we chose 2 dimensions from this questionnaire that are most relevant to the present study, Likeability and Perceived Intelligence. The set
of semantic pairs for each dimension is included in Table 1. Each dimension was treated as an
interval scale in accordance with the assumptions of classical test theory [42].
Inclusion of Other in Self Scale. The IOS Scale in this study was used, based on Aron
et al. [41], as a pictorial scale of closeness in which participants can describe their relationship
with an ‘other’ by selecting a picture from a set of Venn-like diagrams which depict two circles
that overlap to differing degrees. The overlapping area of the different circles changes linearly
from each picture to the next, and can be compared visually by the participant, in terms of absolute degrees of overlap rather than merely relative to the adjacent images. Because of this we
treated participant responses to this scale as a seven-point interval scale [43]. The scale is presented below in Fig 4.
In addition to the experimental items in the questionnaires we collected demographic data
from the participants including age, gender, prior experience with the robot Care-O-bot3 and
with robots in general from the participants.
Recruitment of participants
The call for participation in the survey was distributed via different mailing lists, i.e. the roboticsworldwide mailing list, the euron-dist mailing list and the PHILOS-L mailing list. During the 12
days the survey was open 301 participants started the questionnaire. Out of those 119 completed
all questions. The remaining 182 were excluded as a result of missing or repetitive answers.
Recording of the videos
The videos used in the online survey showed an actor arranging coloured plastic flowers to a
bouquet and being observed passively by the robot. In the online survey we used three different
Fig 4. IOS Scale.
doi:10.1371/journal.pone.0127747.g004
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Fig 5. Experimental Setup—Showing the movements of the actor and the respective movements of
the robot for each condition. (Left to right: positive synchronised behaviour, negative synchronised
behaviour, no movement).
doi:10.1371/journal.pone.0127747.g005
experimental conditions in order to test our research questions. The robot’s engagement behaviour differed in the three experimental conditions.
At the start of the video the flowers were laid out in front of the actor on a table. A vase with
floral foam was located on the table. The actor was asked to arrange the flowers freely in the
vase. The effectiveness and suitability of this task was tested prior to the study with two resident
artists at University of Hertfordshire. During a weeklong co-habitation experiment the artists
participated in the development of the task and in an initial set of test runs [44]. Fig 5 shows
the final layout of the experiment.
Experimental conditions
1. Condition 1 (Positive Synchrony). In the first experimental condition the robot exhibited
positive synchronization by following the actor’s actions towards the objects on the table accordingly. Wherever the object of interest for the actor was (flower on the table, vase in the
center in front of the user) the robot followed the movements of the human with its “gaze”,
giving the appearance to be engaged and interested in what the actor was doing. With this
condition we wanted to examine the effect of this behaviour on the perception of the robot’s
perceived sociability.
2. Condition 2 (Negative Synchrony). In the second experimental condition the robot exhibited negative synchrony (In the initial phase of the experiment we considered using a random movement pattern in condition 2. Besides procedural difficulties—we noticed in the
pre-tests that it was very distracting for the actor and hence very difficult to keep the random movement truly random-, it would have also not allowed us to test our second research
question.). It moved its “gaze” always opposite to where the user was moving. If the user
was positioning the flower in the vase in the middle of the table, the robot looked left or
right, giving the appearance of avoiding paying attention to what the user was doing. With
this condition we wanted to test whether the fact that the robot was moving, even though it
appeared not to be engaged with the user, would have a positive effect compared to the control condition. Previous research has shown that animated artificial agents are in general anthropomorphized and ascribed social roles and behaviours [45, 46, 47].
3. Condition 3 (Control). In the control condition the robot was not moving at all and was
“looking” straight forward during the entire experiment. This condition controlled for the
overall effect of robot movement vs. non-movement.
The participants were not given any information about the purpose of the study or the reasons
behind the robot’s behaviour.
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Results
Characteristics of the Sample
The sample consisted of 119 participants. The mean age was 35.28, ranging from 22 to 76, and
with a median age of 32. There were 36 females in the sample, and 83 males.
Results for Godspeed Questionnaire Subscales
In order to address research question 1 and 2, the differences in participant ratings of the robot
along both the two subscales of the Godspeed Questionnaire as well as their responses to the
Inclusion of Other in Self scale between the different conditions were examined.
Reliability measures. To ensure that the sets of pairs described in Table 1 could be used as
scales, the internal consistency of the two subscales was assessed using Cronbach’s alpha. The
high Cronbach’s α across the three conditions shown in Table 2 suggested that we could proceed treating these two dimensions as interval scales as proposed by Bartneck et al. [40].
The subscale scores for each dimension for each condition was calculated and all subsequent
analyses of the Godspeed subscales were done on the dimension subscales rather than the
individual items.
The descriptive statistics for Likeability are presented in Table 3.
The results suggest that for the Likeability dimension participants overall scored the robot
higher than a “neutral” score of 3 only in condition 1, while the participants scored the robot
lower than this “neutral” score in both condition 2 and 3. The effect of condition on participant
ratings along this dimension was assessed using a repeated measures ANOVA. The repeated
measures ANOVA found a significant effect for Condition (F (2, 236) = 147.12, p = 0, partial
η2 = 0.55.
Post-hoc tests presented in Table 4, suggest that there were significant differences between
all three conditions, with Condition 1 receiving the highest scores, followed by Condition 2
and Condition 3 receiving the lowest scores.
The results presented in Table 5 suggest that for the Perceived Intelligence dimension participants would only score the robot higher than a “neutral” score of 3 only in Condition 1, and
lower than this “neutral” score in Condition 2 and 3. The effect of condition on participant
Table 2. Godspeed questionnaire reliability.
Condition
Dimension
1
Likeability
0.87
Perceived Intelligence
0.86
2
3
alpha
Likeability
0.93
Perceived Intelligence
0.88
Likeability
0.91
Perceived Intelligence
0.87
doi:10.1371/journal.pone.0127747.t002
Table 3. Descriptive statistics Likeability.
Condition
Variable
Mean (SD)
Median
95%CI
t(p)
Condition 1
Likeability
3.47 (0.6)
3.6
3.36–3.58
8.48 (<.01)
Condition 2
Likeability
2.56 (0.81)
2.6
2.41–2.71
-5.93 (<.01)
Condition 3
Likeability
2.2 (0.71)
2.2
2.07–2.33
-12.24 (<.01)
doi:10.1371/journal.pone.0127747.t003
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Table 4. Post-hoc tests for Likeability.
Pair
Mean Difference
95% CI of Diff.
t(df)
p
Con1Liking—Con2Liking
0.91
0.75–1.07
11.38 (118)
<.01
Con1Liking—Con3Liking
1.27
1.13–1.41
18.17 (118)
<.01
Con2Liking—Con3Liking
0.36
0.21–0.52
4.59 (118)
<.01
doi:10.1371/journal.pone.0127747.t004
Table 5. Descriptive statistics for Perceived Intelligence.
Condition
Variable
Mean(SD)
Median
95%CI
t(p)
Condition1
Perceived Intelligence
3.13 (0.67)
3.2
3.01–3.25
2.15 (0.03)
Condition2
Perceived Intelligence
2.42 (0.74)
2.4
2.29–2.56
-8.51 (<.01)
Condition3
Perceived Intelligence
2 (0.69)
2
1.87–2.12
-15.92 (<.01)
doi:10.1371/journal.pone.0127747.t005
Table 6. Post-hoc tests for Perceived Intelligence.
Pair
Mean Difference
95% CI of Diff.
t(df)
p
Con1Intell—Con2Intell
0.71
0.55–0.86
8.91 (118)
<.01
Con1Intell—Con3Intell
1.13
0.98–1.29
14.34 (118)
<.01
Con2Intell—Con3Intell
0.43
0.29–0.56
6.27 (118)
<.01
doi:10.1371/journal.pone.0127747.t006
Table 7. Descriptive statistics for the IOS.
Condition
Variable
Mean (SD)
Median
95%CI
Condition 1
IOS
2.91 (1.25)
3
2.68–3.13
Condition 2
IOS
1.92 (1.06)
2
1.72–2.11
Condition 3
IOS
1.17 (0.53)
1
1.07–1.26
doi:10.1371/journal.pone.0127747.t007
ratings along this dimension was likewise assessed using a repeated measures ANOVA. The repeated measures ANOVA found a significant effect for Condition (F (2, 236) = 114.53, p = 0,
partial η2 = 0.49.
Post-hoc tests shown in Table 6 found significant differences between all three conditions,
suggesting that participants rated the robot highest along this subscale in Condition 1 followed
by Condition 2 and finally by Condition 3.
Results for Inclusion of Other in Self Scale
While the results in Table 7, suggest clear differences between the conditions, all the mean median scores are below the “middle” rating of 4. The repeated measures ANOVA found a significant effect for Condition (F (2, 236) = 130.29, p = 0, partial η2 = 0.52.
The post-hoc tests shown in Table 8 for the IOS scale also suggest the same relationship between conditions as that found for the other two measures, i.e. with highest scores for Condition 1 followed by Condition 2 and finally by Condition 3.
Impact of Demographics
In order to address Research Question 3, the relationships between demographic factors and
prior experience of robots, and responses along the Godspeed subscales and the IOS scale were examined using a series of Spearman's correlations. This analysis found no significant relationships.
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Table 8. Post-hoc tests for the IOS.
Pair
Mean Difference
95% CI of Diff.
t(df)
p
IOS1Number—IOS2Number
0.99
0.74–1.24
7.9 (118)
<.01
IOS1Number—IOS3Number
1.74
1.53–1.95
16.45 (118)
<.01
IOS2Number—IOS3Number
0.75
0.55–0.94
7.59 (118)
<.01
doi:10.1371/journal.pone.0127747.t008
Discussion
Findings
The results largely confirm our initial hypotheses. We expected the robot to be rated most likeable in the condition in which its behaviour was synchronised with the behaviour of the actor.
In the condition in which Care-O-bot3 exhibited positive synchronisation it was rated most
likeable and intelligent, followed by the condition in which the robot exhibited negative synchronisation. We could further show that people rated an animated robot more likeable than
the same robot not moving. This concurs with previous HRI findings [48] and with studies in
developmental psychology research, which show that humans start at a very early age to ascribe
goals and mental states to moving agents [49, 50]. In Condition 3, in which the robot did not
move at all, it was rated the least likeable and least intelligent. The findings of the Inclusion of
Others in Self Scale follow the same pattern. The participants rated the relationship between
the actor and the robot closest in the condition in which the robot’s behaviour was synchronised with the actions of the human.
We found no correlations between age and perceived intelligence, likeability or the rating of
the IOS scale. We also found no correlation between prior experience with robots in general or
exposure specifically to the Care-O-bot3 with any of the measurements. In contract to the findings of other studies [51, 52] we found no correlations between the gender of the participants
and our measurements.
The effect that the robot is perceived more positive, even when it is exhibiting behaviour
that could be interpreted as ‘avoidance’, is a key finding of our study, providing on the one
hand interesting insights in the human perception of social robots and agents, and on the other
hand has practical consequences for designers of robot home companions. It seems that exhibiting behaviour that can be interpreted as coherent and goal-directed, even if it does not conform to what would be expected of socially engaging behaviour, facilitates the human
propensity to ascribe intentions to agents (objects).
One of the first examples demonstrating this effect was introduced by Heider and Simmel
[45]. They presented a short cartoon involving two triangles and a dot moving inside and outside a square to participants. Their results indicated that depending on the movement of the
geometric shapes, people started to ascribe not only intentions but also personality characteristics to them. Tremoulet and Feldman [53] demonstrated that a single moving object can be
perceived as being ‘alive’, depending on the variations in speed and directions of the motion.
Pantelis et al. [54] have shown that the attribution of intentionality also applies to autonomous
agents interacting with a virtual environment. Gergely et al. [55] demonstrated that 12-monthold pre-verbal children already interpret agent (objects) “acting” seemingly goal directed as
having intentions, and according to Meltzoff [56] 18-month-old children are already capable
of understanding the intention of others correctly. More recently, Ma and Xu [57] have shown
that pre-verbal infants by about 9–10 months of age infer the presence of an intentional agent
from the perception of regularity in a visual display. These results from developmental psychology research indicate how deeply rooted the propensity of understanding the goal directed
movements of agents as being intentional is in human ontogeny.
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In the last 20 years this has been of increasing interest in the HRI community. The research
on the social aspects of HRI focuses on the expressivity that proxemics, movements, gestures,
and postures can give to social robots. It is concerned with how to integrate robotic agents into
human social ecologies, including robotic agents built to accomplish tasks in education, entertainment, assistance, mediation in therapeutic relations, and rehabilitation contexts. The focus
is on how to enhance the capabilities of robots to interact efficiently in the social domain they
are built for, in order to improve their social acceptance [58].
Dautenhahn [59] identified 6 factors that facilitate the human perception of robots as intentional agents. Amongst these factors are goal-directed behaviour, and synchronisation and interactivity. Our findings show that even without interactivity people prefer a robot exhibiting
seemingly synchronised behaviour. This is arguable a result of interpreting the robot’s movements as active avoidance and therefore being intentional. Using the human propensity for ascribing agency and intentionality to animated agents (objects), even in situations in which the
robot is not actively participating in a given task, might be crucial for the successful introduction
of robot companions into private homes and possibly other human-inhabited environments.
The results also have possible practical considerations for designing socially acceptable behaviours for robot home companions. A moving robot, even if it is moving in a way that is not
following the expected ‘social behaviour’, and this might include a robot that is moving but
malfunctioning, or a robot where the behaviour policies, perceptual or movement abilities are
limited, may still be perceived more positively than a robot that is not moving at all. Thus,
making a robot move seems to be a crucial requirement for a home companion robot. These results from our study are supported by previous research on robots with ‘idle movements’, such
as blinking or gaze avoidance, which have been shown to be perceived more positively compared to robots without such movements. The specific use of robot movements such as gaze,
nodding, blinking, and human-robot movement contingency has been discussed in the context
of specific communicative functions in human-robot interaction (see e.g. [60–64]). However,
our results indicate that the mere introduction of such movements to a robot, even when not
yet functioning at the level of complexity we find in humans, may already increase participant’s
acceptance and positive perception of the robot, compared to a robot lacking such movements
at all. Future research needs to investigate the interrelationships between robot and human
movements, non-verbal cues and the attribution of agency in more detail.
Limitations of study
During the experiment the robot was a passive observer. There was no direct communication
or interaction between the user and the robot, however in the positive synchronisation condition the robots behaviour suggested joint attention towards the objects the human user was
manipulating during the course of the experiments. The robot was also based in close proximity to the human user, which arguably created the impression of a social interaction between the
robot and the user from the perceptive of an outside observer. In this specific sense we speak of
an interaction scenario.
Our call for participation received 301 replies within 12 days. Being able to reach this many
participants in such a short time is an argument in favour for using an online survey. There are
arguments for both using video material to rate behaviour and for exposing the participants to
the actual robot in a real world scenario. It has been shown in the past that the actual physical
presence of the participant during the experimental scenario with a robot can have a strong effect on the perception and rating of behaviours exhibited by the involved robot [65, 66]. On the
other hand there are many video studies with virtual artificial agents on screens that illustrate
that watching behaviour in a video is a valid method to create reliable results [67, 68].
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Another potential source of bias in our results is the selection of mailing lists for the call for
participation. We selected two robotic mailing lists and a mailing list mainly read by philosophers and psychologists. This might have had an effect on the results. Even though the data
suggests a reasonably well contribution of prior experience with robots, the sample was very
likely not representative for the general population. Access to these mailing lists is usually restricted to academics and students. Furthermore a positive reply to our request suggests an interest and possibly a positive predisposition towards the topic. However, this is a problem
social sciences and HRI research involving human volunteers as participants is facing in general. The use of mailing list makes this problem more pronounced due to the pre-selection of the
users of these mailing lists. For further HRI research using online video studies other forms of
distributing the call for participation, e.g. social media like Facebook, could be involved.
Summary of Hypotheses and Implications
The main research intent of this study was to investigate how participants judge a scenario
where a robot is watching the activities of a person. Specifically, we were interested in the effects of different forms of behavioural synchronisation exhibited by a non-anthropomorphic
robot on its “Likeability” and “Perceived Intelligence”, and to evaluate its IOS Scale rating by
an outside observer. We showed that positive behaviour synchronisation resulted in the highest
ratings, followed by negative synchronisation, which was followed by no movement at all. Our
assumptions from RQ1 and RQ2 were confirmed. With respect to RQ3 we found no correlations concerning demographics and background of the participants.
Future work
As the discussion illustrates there are different directions that future research based on this
study could take. The results of our study backup the iterative use of online video surveys in
HRI to support rapid prototyping of robotic behaviours and HRI scenarios. Further research
will need to examine the specificities of potential attention (idle) behaviours for robotic companions. A variety of different scenarios can be tested in order to generate a database for these
types of situations. Live HRI studies, with participants observing a robot watching a person, and
with participants themselves being watched, need to validate the results from the video studies.
Conclusion
The aim of our study was to test the effects of different behavioural interaction patterns of
Care-O-bot3 on its perceived likeability, perceived intelligence and involvement, in a situation
where a robot watches a person. We tested specifically how people would react to different levels of behavioural synchronisation between the actions of a human and the robot. Our results
showed that the positive perception of the robot could be enhanced if the robot follows the actions of its user. We showed that people perceive a robot that moves in a non-synchronised
way as more likeable, compared to an inactive robot.
The results show that, for a companion robot in people’s own homes, even in situations
where the robot is not directly involved in the tasks, its behaviour has a significant impact on
the overall user experience of the robot.
Supporting Information
S1 Supporting Information. Compressed/ZIP File Archive.
(ZIP)
PLOS ONE | DOI:10.1371/journal.pone.0127747 May 22, 2015
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Perception of Movement Synchrony of a Non-Anthropomorphic Robot
Acknowledgments
The experiment presented in this paper is part of the EU FP7 Project ACCOMPANY (Acceptable robotiCs COMPanions for AgeiNg Years) [2]. This project aims at generating an integrated system in which a robot companion is combined with a smart-home environment in order
to facilitate independent living for the elderly. The robotic platform used in ACCOMPANY is
the Care-O-bot3, a non-anthropomorphic service robot developed by the Fraunhofer Institute
for Manufacturing, Engineering and Automation [39].
The authors would like to specifically thank Nathan Burke and Maha Salem for providing
assistance during the preparation and filming of the scenarios, and Joe Saunders for his idea of
the flower-sorting task. We would also like to thank everyone that made the pre-tests during
the HRI Summer School in Cambridge possible.
Author Contributions
Conceived and designed the experiments: HL JSP DSS KD. Performed the experiments: HL
JSP DSS. Analyzed the data: HL JSP DSS. Contributed reagents/materials/analysis tools: HL
JSP DSS KD. Wrote the paper: HL JSP DSS KD.
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