Robot reciprocation of hugs increases both interacting
times and self-disclosures
Masahiro Shiomi1 · Aya Nakata1,2 · Masayuki Kanbara1,2 · Norihiro Hagita1
Received: date/Accepted: date

Abstract: Physical contact like touching and hugging
plays an essential role in social bonding between people
by encouraging interactions and self-disclosure. However,in a human-robot interaction context, it remains unknown
whether physical activity with robots provides such
similar effects, even though several positive effects oftouch interactions have been unveiled. Therefore, we useda hugging robot that we previously developed andexperimentally investigated its physical interactions
related to encouraging interactions and self-disclosure
with 48 participants. Our results showed that reciprocatedhugs increased the interaction times and encouraged more
self-disclosure from the hugged participants than those
who did not get reciprocated hugs.
Keywords: hug interaction,interaction, self-disclosure

human-robot

touch21], researchers have focused on eliciting it from people[20, 22, 23]. For instance, researchers designed agents that
asked probing questions of interaction partners to elicit
self-disclosure [22]. Other studies showed that selfdisclosure from agents also effectively elicited similar
responses from humans [20, 23].
However, one effective way to elicit self-disclosures hasreceived inadequate focus in human-robot interaction
studies: physical interaction. In human-human interaction,
past research showed the effectiveness of such physicalinteraction as touching to elicit self-disclosures [20, 24].
On the other hand, human-robot interaction studies mainly
focused on dialog and behavior strategies to elicit them. If
physical interaction with robots has similar positive effects
that elicit self-disclosure from interacting people, a robot’sinteraction strategies should be designed to build
relationships with them.

1 Introduction

Building relationships with people is essential for social
robots that support them in such daily situations as
education [2-6], elderly care [7-9], hospitals [10, 11], andshopping [12, 13]. Past studies focused on effective
interaction strategies to build relationships between people
and social agents, including behavior changes during
interaction [13, 14], positioning behaviors [15, 16], andtheir characteristics [17-19].

(a)

(b)

(c)

(d)

In human science literature, since self-disclosure plays anessential role in building relationships between people [20,This paper is an extended version of a previous work of
Shiomi et al. [1] and contains additional references,
experiments with a different condition, analysis, anddiscussions.
1Interaction Science Laboratory, ATR,
2-2-2 Hikaridai Keihanna-Sci. City, Kyoto, Japan
E-mail: m-shiomi@atr.jp
2
Nara Institute of Science and Technology,
8916-5 Takayama-Cho, Ikoma, Nara 630-0192, Japan

Fig. 1 Reciprocated hug from a robot: (a) robot requests ahug, (b) opens its arm, (c) man hugs the robot, (d) robot
reciprocates.

2

In this paper, we investigate the effects of physicalinteraction, i.e., hugs, with a robot for encouraging
interactions and self-disclosure (Fig. 1). Our experimentused a robot named Moffuly that can give hugs. In our
experiment, we measured and compared the numbers ofself-disclosures during interactions with/without a robot’s
hug as well as the interaction times.
2 Related Work
2.1 Self-disclosure in human-robot interactions
Self-disclosure is a primary strategy with which both
virtual agents and social robots build relationships withpeople [20, 22, 23]. For example, provocative questions
from virtual agents can elicit it [22]. Several studies
reported that people revealed more about themselves after
listening to disclosure from agents [20, 23]. Past studies
effectively used self-disclosure from robots to build
relationships with children in education settings [14, 25]or customers in a shopping-support setting [13]. Other
studies focused on whether robots can elicit self-disclosureand how interaction influences their relationships [19, 26].

These studies described the importance of self-disclosure
from both robots and people for more natural andsmoother interaction as well as effective strategies toencourage self-disclosure from people. One unique pointof this study is its focus on the effects of touch interaction
for encouraging self-disclosure to investigate whether
physical interaction provides advantages for this purpose.
2.2 Touch interaction between people and robots
Several past studies focused on the positive effects ofhuman-robot touch interaction, similar to human-humantouch interaction [27-35]. Researchers focused on its
effects in mental health contexts. For instance, Shibata et
al. used a pet-type robot (Paro) for supporting seniors [27].
Another study reported that using a huggable device fortele-conversation decreased stress from a hormonal
perspective [32]. Li et al. investigated the relationships
among body accessibility, physiological arousal, andtouch style while people are touching a robot [36]. Fitter
et al. focused on situations where people clap hands witha robot and developed a motion classification system in thecontext of hand-clapping game settings. They also
investigated the perceived enjoyments of social andphysical interaction with a robot and the effects of related
modalities like facial animation and stiffness during
interaction [37, 38]

These studies focused on touch interaction from people torobots, but recent studies have also concentrated on theeffects of touch interaction from robots to people, i.e.,
active touching. For example, several studies reported that
active touching from robots provides better impressions
than passive touching [28, 29]. Other studies concluded
that active hug interaction provides positive impressions
and encourages prosocial behaviors [33, 39]. From anotherShiomi et al.

perspective, researchers focused on the effect of several
characteristics of hug interaction toward perceived
impressions, i.e., softness, warmth, squeezing pressure,and release timing [40].
Although these studies reported several positive effects
and possible applications for touching in human-robot
interaction, they focused less on the outcomes thatencourage self-disclosure and the willingness to engage ininteractions that are strongly related to building
relationships between robots and people. If a robot’s hug
positively affects people, this knowledge will benefit robot
applications with touch interactions.
We note that this paper is an extended version of our
precious work [1] and contains additional references,
experiments with a different condition (the effects of a
hug-only condition), analysis, and discussions.
3 System
3.1 Robot hardware and software
In this study, we used a large teddy-bear-type robot(Moffuly, Fig. 2) that we previously developed [1]. It only
has 2 DOFs (one for each elbow) for giving a hug. It is200-cm tall with 80-cm arms for adequately reciprocatinga hug. Its arms are around 80 cm above the ground, and its
elbow joints vertically bend from the upper arm. Withoutarms, the robot only has an internal foundation to keep its
position. Its frame is covered with polypropylene. We
attached a speaker near its mouth for conversational
interactions and used a Japanese speech synthesis systemfor voice synthesis [41].

We used weak digital servo motors (MG996R, torque: 11
kgf･cm). Since the elbow joints are back-drivable, and it
is easy to stop by human power. We used one Shokkaku
Cube (Touchence Inc.) as a touch sensor that can measure
the height changes on the top of the soft material with 16
measurement points at a maximum frequency of 100 Hz.
For safety, we attached a touch sensor to the robot’s left
hand area (inside its fur) because this place makes contact
with the participants during hugs. The pressure threshold
was decided heuristically based on pre-trials done by theauthors.

Operator partCamerasMicrophone

Touch sensor

Behavior rules(Operator)

Motioncontroller

Robot(Moffuly)

Robot partFig. 2 System overview (left) and appearances of Moffuly
and touch sensor (right)

Robot reciprocation of hugs increases both interacting times and self-disclosures

3.2 Conversational behavior
Since this study investigates the effects of hug interaction
on self-disclosure encouragement, we prepared three typesof conversational behaviors: the robot’s self-introduction,
requesting self-disclosure, and replies as fillers. First, therobot introduces itself, offers a self-disclosure, and moves
its arm. For example, it might say, “Hi, my name is
Moffuly. Even though I look like a bear, my favorite foodis electricity, not honey.” The robot requests a selfdisclosure by offering to listen to whatever is on the mindsof the participants. The replies fill in the conversation lulls.
We describe the details of the hug behaviors in the next
subsection.

In our study, the operator systematically and partiallyteleoperated the robot following the Wizard-of-Oz
approach [42] to choose appropriate conversational
behaviors that consist of relatively simple utterances. The
robot behaved as a listener to encourage self-disclosure
from the participants. Thus, if they asked the robot too
many questions, it might decline to answer: “Sorry, it isquite a difficult question for me. I would like to hear more
about you!” This step discourages conversations that focus
on the robot. The purpose of these rule designs is to avoid
the effects of different interaction styles between
participants and the robot. For teleoperation, we installed
two cameras in our experiment room: one with amicrophone on the robot’s side and another on the ceiling.
The operator used a microphone to listen to theparticipants. For analysis, we recorded the video/audio
information from these cameras and the microphone.
3.3 Hug behavior
To conduct hug interactions with the robot, Moffuly first
asks for a hug from the participants, depending on theexperiment’s conditions (Section 4.3). At the beginning of
the interaction in conditions that include hugs, the robot
opens its arms and says, “Before we start talking, would
you please give me a hug?” After the person hugs the robot,
the robot closes its arms until it detects contact between its
arms and the person’s body. Based on the following threerules, the robot pats her on the back by moving both of its
arms at the end of its speech or her speech. If the person’s
speech exceeds 30 seconds, the robot pats her every 30
seconds.
4 Experiment
4.1 Hypotheses and predictions
Physical interaction with others provides positive
impressions and encourages more self-disclosure in
human-human interaction [20, 24]. In human-robot
interaction, physical interaction with robots also provides
positive impressions [27-30, 32]. Therefore, we believethat people who are hugged by a robot will disclose more
about themselves than people who are not hugged.
Previous studies reported that reciprocated touch3

interaction from a robot encourages more feelings andcauses more behavior changes than people who are notreciprocally touched by a robot [28, 33]. Therefore, we
believe that people who are hugged by a robot will disclose
more about themselves than those who only hugged therobot.
We thought that people’s willingness to interact with therobot would change through physical interaction. We
assumed that its reciprocated hugs are useful for building
good relationships. If correct, people who are hugged by arobot will interact with it more and longer interaction times
elicit self-disclosures. Therefore, we made two hypotheses:
Prediction 1: A robot’s reciprocated hugs will increase theinteraction time of the participants more than without areciprocated hug and without any physical interaction.
Prediction 2: Its reciprocated hugs will encourage more
self-disclosure from participants than without reciprocatedhugs and without any physical interaction.
4.2 Participants
Forty-eight Japanese people (24 women and 24 men,whose ages averaged 36.19, had a standard deviation(S.D.) of 9.93, and ranged from 20 to 52) were paid fortheir participation.
4.3 Conditions
The study had a between-participant design with thefollowing three conditions. We assigned 16 participants(eight females and eight males) to each condition. The
operator controlled the robot by following the same ruleswith identical conversational contents in the following
three conditions:No-hug: The participants remained in their initial position(45 cm from the robot) and only talked with it: no physicalinteraction.
Hug-only: The robot requested a hug from the participants
and then started to chat; the robot did not return the hug.
Reciprocated-hug: The robot requested a hug from theparticipants, returned it, and then started to chat. Based on
the pre-defined rules, the robot sometimes patted them on
the back during the experiment.
4.4 Procedure
An experimenter briefly described our experiment’s
purpose and procedure before it started. The experimenter
also physically demonstrated how to hug the robot, except
for those in the no-hug condition. Next the experimenter
explained the robot’s limited conversational capability, i.e.,
it cannot understand complex conversations. It prefers
listening to stories and wants to talk with them. Before
starting the experiment, the experimenter left the room, andthe participants remained in it.

4

After starting the experiment, the robot greeted theparticipants. Next the robot requested a hug in the hug-onlyor reciprocated-hug condition. It first introduced itself andrequested self-disclosure and asked to hear their stories.
The minimum length of the experiment was ten minutes. It
started from the end of the robot’s self-introduction. Afterten minutes, the participants took a short break and chose
whether to end the experiment or to extend it. They could
extend the session for a maximum of ten minutes; during
this period, they could freely stop the interaction any time.
Before the experiment, we explained that the robot is
autonomous, and after it, we prepared a debriefing session
to explain our purpose. At the debriefing session, all theparticipants reported that they assumed that the robot wasautonomous. Their acceptance might reflect the robot’s
simple utterances and reactions during the interactions.
Our institution’s ethics committee approved this researchfor studies involving human participants. We obtained
informed consent from all of them.
4.5 Measurements
We measured two objective items and one subjective item.
For the objective measurements, we investigated theinteraction times of the participants and the ratios of selfdisclosure to non-self-disclosure conversations by dividing
the conversations related to self-disclosures by theconversations without them. We focused on the ratios
because the interaction time of the participants and thelength of the conversational contents were different.

To measure the amount of both conversational contents, acoder transcribed all the conversations of the recorded
video/audio data and segmented all the conversation data
into 289 scripts. We defined a conversation unit as aconversational topic. The coder transcribed all theconversations and segmented the texts to each script due tothe changes of the conversational topics. Therefore, whena participant talked about topics A, B, C, and returned to A
again, the number of scripts was four. Each script consistedof several sentences.
Next, the coder categorized all the scripts as either selfdisclosure or non-self-disclosure. For example, if thescripts included such private topics as hobbies or personal
experiences, the coder placed them in the self-disclosure
category. If the scripts only included mundane topics likethe weather, the coder categorized them as non-selfdisclosure. The coder also coded the self-disclosure
contents into positive/negative categories to investigate
whether the robot’s physical interaction changed the typesof self-disclosure. After these processes, another coder
coded 10% of these data. We calculated the coding’s
validity based on a previous work [43]. The kappa
coefficient [44] was 0.71, indicating substantial agreement
between the two coders.

Shiomi et al.

As the subjective item, we measured the participants’
perceived positive impressions toward the robot by one
questionnaire item: “I think this robot is good overall.”This item was scored on a 1-to-7 point scale, where 1 is themost negative (completely disagree), and 7 is the mostpositive (completely agree).
5 Results
5.1 Verification of prediction 1
Figure 3 shows the participants’ interaction times. For
analysis, we conducted an ANOVA, and its results showedsignificant differences (F(2, 45)=18.030, p<0.001, partialη2 =0.445). Multiple comparisons with the Bonferroni
method revealed significant differences among theconditions: reciprocated-hug > hug-only (p<0.001) andreciprocated-hug > no-hug (p<0.001). There was no
significant between hug-only and no-hug (p=1.000).
Therefore, the robot’s reciprocated hugs significantly
increased the interaction time more than without them andwithout any physical interaction. Prediction 1 was
supported.
5.2 Verification of prediction 2
Figure 4 shows the ratios of the self-disclosure/non-selfdisclosure conversations. For the analysis, we used an
ANOVA, and its results showed significant differencesamong the three conditions (F(2, 45)=8.162, p=0.001,
partial η2 =0.266). Multiple comparisons with the
Bonferroni method revealed significant differences:
reciprocated-hug > hug-only (p=0.010), reciprocated-hug> no-hug (p=0.001), but no significant difference between
hug-only and no-hug (p=1.000). Therefore, the robot’s
reciprocated hugs significantly increased the ratio of selfdisclosure more than without reciprocated hugs and any
physical interaction. Prediction 2 was supported.
5.3 Analysis of total self-disclosure amount
Based on our analysis, the reciprocated hugs from therobot effectively increased the interaction time as well as
the self-disclosure and non-self-disclosure ratios. We also
compared the amount of both the self-disclosure and nonself-disclosure conversations between the conditions.

Figure 5 shows the numbers of both the self-disclosure andnon-self-disclosure conversations. For analysis, we
conducted a two-way repeated measure ANOVA with
mixed factors: category (self-disclosure and non-selfdisclosure) and condition (no-hug, hug-only, andreciprocated-hug). The results showed significant
differences in the category factor (F(1, 45)=21.378,
p<0.001, partial η2=0.322) and in the interaction between
the two factors (F(2, 45)=4.081, p=0.024, partial
η2=0.154). There was no significant difference in thecondition factor (F(2, 45)=2.297, p=0.112).
Multiple comparisons with the Bonferroni method showedsignificant differences in the self-disclosure category

Robot reciprocation of hugs increases both interacting times and self-disclosures(reciprocated-hug > hug-only, p=0.039) and in thereciprocated-hug condition (self-disclosure > non-selfdisclosure, p<0.001). Our analysis also identified
significant trends in the self-disclosure category(reciprocated-hug > no-hug, p=0.073) and in the hug-only
condition (self-disclosure > non-self-disclosure, p=0.078).
These results suggest that the robot’s reciprocated hugs
significantly increased the amount of self-disclosure.
***

5.4 Analysis of participant positive impressions
Figure 6 shows the perceived positive impressions of theparticipants. We conducted an ANOVA and identified nosignificant differences (F(2,45)= 1.706, p=0.193, partialη2 =0.070). Their total impressions were not significantly
different among the conditions, even though thereciprocated hugs from the robot significantly influenced
their behaviors.
7

***

20

Interaction time (minutes)

5

6
15

5
10

4
5
*** : p <= 0.001
0No-hugHug-only

3

Reciprocated-hug

Fig. 3 Interaction time (average and standard error (S.E.))2

1

Hug-only

Reciprocated-hug

Fig. 6 Positive impressions (average and S.E.)**

6

Self-disclosure ratios

No-hug

***

7

5

4
3
** : p <= 0.01*** : p <= 0.00121No-hugHug-only

Reciprocated-hug

Fig. 7 Scene where robot hugged a participant whosmiledFig. 4 Self-disclosure/non-self-disclosure ratios: 1
indicated amount of both types of contents are identical(average and S.E.)
+
7

*

6
5

(a)

(b)

(c)

(d)

***

4

3

+

+ : p <= 0.10
* : p <= 0.05*** : p <= 0.00121No-hugHug-onlyNon-self-disclosure

Reciprocated-hug

Self-disclosure

Fig. 5 Conversation contents (average and S.E.)

Fig. 8 Participant who requested a spooning-type hug

6
5.5 Observations of participants’ behaviors and analysisof their self-disclosure categories
In all the conditions, participants typically talked about
themselves after requests from the robot. Most seemedsurprised by the robot’s hug-request behavior, even though
we did explain it to them. In the reciprocated-hug
condition, sometimes participants smiled during their first
hug interaction with the robot (Fig. 7). We witnessed one
interesting scene in the reciprocated-hug condition wherea participant changed his hugging style with the robot.
During the second session, he asked the robot to open its
arms (Fig. 8a), and he turned his back to it (Fig. 8b). He
asked for another hug, closed his eyes (Fig. 8c), and waited
to be hugged in such a spooning style (Fig. 8d). He silently
maintained this hugging style until the end of the second
session. He said that he enjoyed the hug experiences withthe robot and its physical touch.

For the self-disclosure contents, the most common
conversation topics were families, holidays, school, andwork. For example, several participants described a
traveling experience (note: the texts are translated fromJapanese): “Every year, I take a cycling tour with myfriends. This was our fourth straight year. Every year
we’ve had some kind of trouble, such as heatstroke, leg
cramps, and so on. This year was no different; my friend
took a spill and broke his bicycle.” Another participant
described her job at which she struggles outside on hot
summer days. The robot said, “That sounds like a very
hard job.” She thanked it and smiled. Some participants
shared hardship stories, such as failing a graduate school
entrance examination or the challenge of bringing up
children.
To investigate whether the interaction styles changed thetypes of self-disclosure, we conducted a two-way
repeated-measure ANOVA with mixed factors: category(positive and negative) and condition (no-hug, hug-only,
and reciprocated-hug). The results did not show
significant differences in the category factor (F(1,45)=2.042, p=0.160, partial η2=0.043), in the condition
factor (F(2, 45)=2.257, p=0.116, partial η2=0.091), or in
their interaction (F(2, 45)= 1.830, p=0.172, partial
η2=0.075). Thus, the robot’s hug interactions did not show
a significant difference between the types of selfdisclosures from the people with whom it interacted.
6 DISCUSSION
6.1 Why did the reciprocated hugs increase theinteraction time and the self-disclosure amount?
Our experiment results revealed that people interacted withthe robot longer and provided more self-disclosure when it
reciprocated hugs. This result begs the question: Whichcame first, the cause or the effect? It is difficult to judge
whether they interacted more because they engaged in self-

Shiomi et al.

disclosure or whether they did self-disclosure because they
stayed with the robot longer.
We believe that both are generally true, although the effectsof the reciprocated hugs on self-disclosure are relatively
strong for two reasons. First, we investigated the ratios ofthe self-disclosure/non-self-disclosure conversations at theminimum length (ten minutes). The results are notsignificant between conditions, suggesting that theparticipants did more self-disclosure during the extended
time. The experiment results already showed that theparticipants in the reciprocated-hug condition significantly
extended their interaction time. Therefore, these results
also suggest that hugs resulted in more interaction time andthat self-disclosure might also be encouraged. Second,
simple physical reactions (i.e., patting while hugging)might be perceived as additional social signals in
conversations, encouraging more conversational intent
from the participants. Of course, such an interaction loop
drives self-disclosure and increased their willingness tointeract. Moreover, a past study reported the persuasion
effects of active touch interaction [45]. Reciprocated hugs
might increase the effects in the context of requesting selfdisclosures.
Our experiment results also showed that the participants
spent more time in the reciprocated-hug condition than theinteractions in the other conditions. One possible reason isthe differences in participants’ behaviors because the robot
in the no-hug condition did not request a hug from theparticipants. However, the behaviors requested by theparticipants are identical between the reciprocated-hug andhug-only conditions, and the robot’s speech before theexperiment (e.g., please give me a hug) were the same
between conditions. Therefore, perhaps the interaction time
was affected by the robot’s reciprocated hug.
6.2 Why did the reciprocated hugs not change theperceived impressions?
Our questionnaire results did not show any significant
differences of impressions toward the robot, even though
their behaviors were significantly different between thereciprocated-hug and hug-only/no-hug conditions. One
possible reason is that the perceived impression of the robot
was mainly decided by its appearance, voice, andconversations rather than physical interaction. In fact, inthe no-hug condition, the average value of thequestionnaire results about impression exceeded four
(neutral).

Another possible reason is the interaction time because all
the participants interacted with the robot for at least ten
minutes in all the conditions. Thus, relatively longinteraction might positively affect their impressions. If we
conducted the experiment with a shorter time duration orcompared impressions before/after the experiments, wemight clarify the effects of the hug interaction toward theimpressions.

Robot reciprocation of hugs increases both interacting times and self-disclosures

Moreover, using other physiological measurements such as
hormones or brain-related activities could be useful toinvestigate physical interaction effects. Past studies
reported the effectiveness of touch interaction with robots
from these perspectives [29, 32].
6.3 Limitation and future work
Since we conducted our experiments with a specific robot,
its generality is limited. We need to consider such different
features as the robot’s size and its touches because they are
critical to apply our findings to different situations. We
also implemented a relatively simple hug interaction,although several hug types exist in human cases. For
example, a past study reported that touch characteristics
changed the perceived impressions in human-robot touch
interaction [46]. Investigating different hug interaction
effects is critical to deepen our understanding of the effectsof robot’s hug interactions. Although several limitations
continue, we believe that the knowledge from this study
will be useful for human-robot touch interaction research
fields.

One possible application is using reciprocated huginteraction in a clinical context. A past study reported theusefulness of computer graphics (CG) based agents forthese contexts; patients preferred to disclose their personal
information to such agents than to teleoperated ones [47].
Our study identified the effectiveness of physicalinteraction that cannot be achieved by CG-based agents.
Hug interactions with robots might contribute to building
relationships with patients by self-disclosure.
7 Conclusion

Although past studies showed that haptic interaction frompeople to robots provides positive effects, the effects ofreciprocated haptic interaction from robots to people havereceived less focus. We concentrated on the effects ofreciprocated hugs from a robot for eliciting self-disclosureand more interaction. For this purpose, we used a social
robot that can hug people and conducted an experimentwith participants.
The participants who were hugged by the robot interactedwith it longer than participants who were not hugged. The
participants in the reciprocated-hug condition madesignificantly more self-disclosures than participants
without reciprocated hugs. These results provide evidence
that shows the merits of haptic interaction from robots topeople in the field of human-robot interaction.
Note that the participants’ perceived positive impressions
toward the robots were not significantly different between
the conditions even if their behaviors were. Perhaps theminimuminteraction
time
and
the
robot’s
appearances/voices increased the perceived positive
impressions, but this remains an open question. One future
work of this study will investigate the effects of hug
interaction toward perceived impressions using different

7

types of measurements, including such physiologicalmeasurements as hormones or brain-related activities.
Compliance with Ethical Standards
Funding: This work was supported by JST CREST Grant
Number JPMJCR18A1, Japan.
Conflict of Interest: The authors declare that they have noconflicts of interest.
References[1] M. Shiomi, A. Nakata, M. Kanbara, and N. Hagita, "A
Robot that Encourages Self-disclosure by Hug," Social
Robotics: 9th International Conference, ICSR 2017,
Tsukuba, Japan, November 22-24, 2017, Proceedings,
A. Kheddar, E. Yoshida, S. S. Ge et al., eds., pp. 324333, Cham: Springer International Publishing, 2017.
[2] S.-S. Yun, M. Kim, and M.-T. Choi, “Easy Interface andControl of Tele-education Robots,” International
Journal of Social Robotics, vol. 5, no. 3, pp. 335-343,
2013.
[3] M. Shiomi, T. Kanda, I. Howley, K. Hayashi, and N.
Hagita, “Can a Social Robot Stimulate Science
Curiosity in Classrooms?,” International Journal ofSocial Robotics, vol. 7, no. 5, pp. 641-652, 2015.
[4] T. Komatsubara, M. Shiomi, T. Kanda, H. Ishiguro, and
N. Hagita, “Can a social robot help children's
understanding of science in classrooms?,” inProceedings of the second international conference on
Human-agent interaction, Tsukuba, Japan, pp. 83-90,
2014.
[5] F. Tanaka, K. Isshiki, F. Takahashi, M. Uekusa, R. Sei,
and K. Hayashi, “Pepper learns together with children:
Development of an educational application,” inHumanoid Robots (Humanoids), 2015 IEEE-RAS 15th
International Conference on, pp. 270-275, 2015.
[6] F. Tanaka, and S. Matsuzoe, “Children teach a carereceiving robot to promote their learning: Field
experiments in a classroom for vocabulary learning,”
Journal of Human-Robot Interaction, vol. 1, no. 1, 2012.
[7] H. Felzmann, T. Beyan, M. Ryan, and O. Beyan,“Implementing an ethical approach to big data analytics
in assistive robotics for elderly with dementia,” SIGCAS
Comput. Soc., vol. 45, no. 3, pp. 280-286, 2016.
[8] E. Mordoch, A. Osterreicher, L. Guse, K. Roger, and G.
Thompson, “Use of social commitment robots in thecare of elderly people with dementia: A literature
review,” Maturitas, vol. 74, no. 1, pp. 14-20, 2013.
[9] M. Shiomi, T. Iio, K. Kamei, C. Sharma, and N. Hagita,“Effectiveness of Social Behaviors for Autonomous
Wheelchair Robot to Support Elderly People in Japan,”
PLoS ONE, vol. 10, no. 5, pp. e0128031, 2015.
[10] B. Mutlu, and J. Forlizzi, “Robots in organizations: therole of workflow, social, and environmental factors inhuman-robot interaction,” in Proceedings of the 3rd8

ACM/IEEE international conference on Human robot
interaction, Amsterdam, The Netherlands, pp. 287-294,
2008.
[11] S. Ljungblad, J. Kotrbova, M. Jacobsson, H. Cramer,
and K. Niechwiadowicz, “Hospital robot at work:something alien or an intelligent colleague?,” inProceedings of the ACM 2012 conference on Computer
Supported Cooperative Work, pp. 177-186, 2012.
[12] Y. Iwamura, M. Shiomi, T. Kanda, H. Ishiguro, and N.
Hagita, “Do elderly people prefer a conversational
humanoid as a shopping assistant partner insupermarkets?,” in Human-Robot Interaction (HRI),
2011 6th ACM/IEEE International Conference on, pp.
449-457, 2011.
[13] T. Kanda, M. Shiomi, Z. Miyashita, H. Ishiguro, and N.
Hagita, “A communication robot in a shopping mall,”
Robotics, IEEE Transactions on, vol. 26, no. 5, pp. 897913, 2010.
[14] T. Kanda, R. Sato, N. Saiwaki, and H. Ishiguro, “A twomonth field trial in an elementary school for long-term
human–robot interaction,” IEEE Transactions on
Robotics, vol. 23, no. 5, pp. 962-971, 2007.
[15] J. Mumm, and B. Mutlu, “Human-robot proxemics:
physical and psychological distancing in human-robot
interaction,” in Proceedings of the 6th international
conference on Human-robot interaction, pp. 331-338,
2011.
[16] C.-M. Huang, T. Iio, S. Satake, and T. Kanda,“Modeling and Controlling Friendliness for An
Interactive Museum Robot,” in Robotics: Science andSystems, pp., 2014.
[17] I. Kruijff-Korbayová, E. Oleari, A. Bagherzadhalimi, F.
Sacchitelli, B. Kiefer, S. Racioppa, C. Pozzi, and A.
Sanna, “Young Users’ Perception of a Social Robot
Displaying Familiarity and Eliciting Disclosure,” inInternational Conference on Social Robotics, pp. 380389, 2015.
[18] G. E. Birnbaum, M. Mizrahi, G. Hoffman, H. T. Reis,
E. J. Finkel, and O. Sass, “What robots can teach us
about intimacy: The reassuring effects of robot
responsiveness to human disclosure,” Computers in
Human Behavior, vol. 63, pp. 416-423, 2016.
[19] N. Martelaro, V. C. Nneji, W. Ju, and P. Hinds, “Tellme more: Designing hri to encourage more trust,disclosure, and companionship,” in The Eleventh
ACM/IEEE International Conference on Human Robot
Interaction, pp. 181-188, 2016.
[20] P. C. Cozby, “Self-disclosure: a literature review,”
Psychological bulletin, vol. 79, no. 2, pp. 73, 1973.
[21] J. L. Gibbs, N. B. Ellison, and R. D. Heino, “Selfpresentation in online personals: The role of anticipatedfuture interaction, self-disclosure, and perceived
success in Internet dating,” Communication Research,
vol. 33, no. 2, pp. 152-177, 2006.
[22] J. Weizenbaum, “ELIZA—a computer program for the
study of natural language communication between man

Shiomi et al.
and machine,” Communications of the ACM, vol. 9, no.
1, pp. 36-45, 1966.
[23] Y. Moon, “Intimate exchanges: Using computers toelicit self-disclosure from consumers,” Journal ofConsumer Research, vol. 26, no. 4, pp. 323-339, 2000.
[24] S. M. Jourard, and J. E. Rubin, “Self-disclosure andtouching: A study of two modes of interpersonalencounter and their inter-relation,” Journal of
Humanistic Psychology, vol. 8, no. 1, pp. 39-48, 1968.
[25] S. Chandra, P. Alves-Oliveira, S. Lemaignan, P.
Sequeira, A. Paiva, and P. Dillenbourg, “Children's peer
assessment and self-disclosure in the presence of an
educational robot,” in Robot and Human Interactive
Communication (RO-MAN), 2016 25th IEEE
International Symposium on, pp. 539-544, 2016.
[26] M. J. Pettinati, R. C. Arkin, and J. Shim, “The influenceof a peripheral social robot on self-disclosure,” in Robotand Human Interactive Communication (RO-MAN),
2016 25th IEEE International Symposium on, pp. 10631070, 2016.
[27] R. Yu, E. Hui, J. Lee, D. Poon, A. Ng, K. Sit, K. Ip, F.
Yeung, M. Wong, and T. Shibata, “Use of a Therapeutic,
Socially Assistive Pet Robot (PARO) in Improving
Mood and Stimulating Social Interaction andCommunication for People With Dementia: Study
Protocol for a Randomized Controlled Trial,” JMIR
research protocols, vol. 4, no. 2, 2015.
[28] M. Shiomi, K. Nakagawa, K. Shinozawa, R. Matsumura,
H. Ishiguro, and N. Hagita, “Does A Robot’s Touch
Encourage Human Effort?,” International Journal ofSocial Robotics, vol. 9, pp. 5-15, 2016.
[29] H. Fukuda, M. Shiomi, K. Nakagawa, and K. Ueda,“‘Midas touch’in human-robot interaction: Evidence
from event-related potentials during the ultimatum
game,” in Human-Robot Interaction (HRI), 2012 7th
ACM/IEEE International Conference on, pp. 131-132,
2012.
[30] K. Nakagawa, M. Shiomi, K. Shinozawa, R. Matsumura,
H. Ishiguro, and N. Hagita, “Effect of Robot’s
Whispering Behavior on People’s Motivation,”International Journal of Social Robotics, vol. 5, no. 1,
pp. 5-16, 2012.
[31] T. Hirano, M. Shiomi, T. Iio, M. Kimoto, I. Tanev, K.
Shimohara, and N. Hagita, “How Do Communication
Cues Change Impressions of Human–Robot Touch
Interaction?,” International Journal of Social Robotics,
vol. 10, no. 1, pp. 21-31, 2018.
[32] H. Sumioka, A. Nakae, R. Kanai, and H. Ishiguro,“Huggable communication medium decreases cortisol
levels,” Scientific Reports, vol. 3, pp. 3034, 2013.
[33] M. Shiomi, A. Nakata, M. Kanbara, and N. Hagita, “A
Hug from a Robot Encourages Prosocial Behavior,” in2017 26th IEEE International Symposium on Robot andHuman Interactive Communication (RO-MAN), pp.
418-423, 2017.

Robot reciprocation of hugs increases both interacting times and self-disclosures

M. Shiomi, T. Minato, and H. Ishiguro, “Subtle
Reaction and Response Time Effects in Human-Robot
Touch Interaction,” in International Conference on
Social Robotics, pp. 242-251, 2017.
[35] C. J. A. M. Willemse, A. Toet, and J. B. F. van Erp,
“Affective and Behavioral Responses to Robot-InitiatedSocial Touch: Toward Understanding the Opportunities
and Limitations of Physical Contact in Human–Robot
Interaction,” Frontiers in ICT, vol. 4, no. 12, 2017.
[36] J. J. Li, W. Ju, and B. Reeves, “Touching a mechanicalbody: tactile contact with body parts of a humanoid
robot is physiologically arousing,” Journal of HumanRobot Interaction, vol. 6, no. 3, pp. 118-130, 2017.
[37] N. T. Fitter, and K. J. Kuchenbecker, “Teaching a Robot
Bimanual Hand-Clapping Games via Wrist-Worn
IMUs,” Frontiers in Robotics AI & SOCIETY, vol. 5, pp.
85, 2018.
[38] N. T. Fitter, and K. J. Kuchenbecker, “How Does It Feelto Clap Hands with a Robot?,” International Journal ofSocial Robotics, pp. 1-15, 2019.
[39] M. Shiomi, and N. Hagita, “Audio-Visual Stimuli
Change not Only Robot’s Hug Impressions but Also Its
Stress-Buffering Effects,” International Journal ofSocial Robotics, pp. 1-8, 2019.
[40] A. E. Block, and K. J. Kuchenbecker, “Softness,
Warmth, and Responsiveness Improve Robot Hugs,”
International Journal of Social Robotics, vol. 11, no. 1,
pp. 49-64, 2019.
[41] H. Kawai, T. Toda, J. Ni, M. Tsuzaki, and K. Tokuda,“XIMERA: A new TTS from ATR based on corpusbased technologies,” in ISCA Speech Synthesis
Workshop, pp. 179-184, 2004.
[42] N. Dahlbäck, A. Jönsson, and L. Ahrenberg, “Wizard of
Oz studies: why and how,” in Proceedings of the 1st
international conference on Intelligent user interfaces,
Orlando, Florida, USA, pp. 193-200, 1993.
[43] M. Lombard, J. Snyder‐Duch, and C. C. Bracken,"Content analysis in mass communication: Assessment
and reporting of intercoder reliability," Human
Communication Research, vol. 28, no. 4, pp. 587-604,
2002.
[44] J. Cohen, “A coefficient of agreement for nominal
scales,” Educational and psychological measurement,
vol. 20, no. 1, pp. 37-46, 1960.
[45] G. Huisman, “Social Touch Technology: A Survey ofHaptic Technology for Social Touch,” IEEE
Transactions on Haptics, vol. 10, no. 3, pp. 391-408,
2017.
[46] X. Zheng, M. Shiomi, T. Minato, and H. Ishiguro,“What Kinds of Robot's Touch Will Match Expressed
Emotions?,” IEEE Robotics and Automation Letters, pp.
127-134, 2019.
[47] G. M. Lucas, J. Gratch, A. King, and L.-P. Morency,“It’s only a computer: virtual humans increase
willingness to disclose,” Computers in Human
Behavior, vol. 37, pp. 94-100, 2014.
[34]

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