A Robot that Encourages Self-Disclosure by Hug
Masahiro Shiomi1 Aya Nakata1,2, Masayuki Kanbara1,2 and Norihiro Hagita1,2
1ATR-IRC, Kyoto, Japan
2NAIST, Nara, Japanm-shiomi@atr.jp

Abstract: This paper presents the effects of being hugged by a robot to encourage self-disclosure. Physical interactions, which are known to be essential forcommunication with others, also show the effects of eliciting self-disclosure
from the people with whom one is interacting and contribute to the constructionof social relationships. Previous research demonstrated that people who toucheda robot experienced positive impressions of it without clarifying whether being
hugged by a robot elicits self-disclosure from people. We developed a huge,
teddy-bear-like robot that can give reciprocal hugs to people and experimentallyinvestigated its effects on self-disclosure. Our experiment results with 32 participants showed that those who were hugged by the robot significantly offered
more self-disclosure than those who were not hugged by it. Moreover, people
who were hugged by the robot interacted with it longer than those who were nothugged by it. On the other hand, the perceived feelings about the robot were notsignificantly different between the conditions.
Keywords: Hug, Human-Robot Interaction, Self-disclosure

1

Introduction

Forging relationships between agents and people is essential to serve a number of
intimate support roles in human society such as education [1-5] and elderly care [6-8].
For this purpose, various interaction strategies have been proposed and evaluated inhuman-agent and human-robot interaction research fields: the characteristics of social
robots [9-11], beliefs about agent autonomy [12], physical behavior design [13, 14],and the design of long-term behavior changes [15, 16].
The elicitation of self-disclosure is one common strategy to build a friendly relationship between an agent and an interaction partner [17-19]. Past research works
mainly employed verbal tactics to elicit self-disclosure. For example, agents asked
provocative questions to elicit self-disclosure from interaction partners [17]. Anotherapproach is reciprocal self-disclosure from agents; several research works reported
the effectiveness of people who reveal more of themselves after listening to disclosures from agents [18, 19]. However, physical tactics that elicit self-disclosure are lessfocused than verbal tactics; some works have encouraged self-disclosure from people
by focusing on the effects of physical existence [20] and changes in physical distances[13]. Even though robotics researchers investigated the positive effects of physical2interaction with robots [21-26], no influence on eliciting self-disclosure has been
identified. Therefore, there is room to investigate the effectiveness of physical interaction effects on eliciting self-disclosure from people to build friendlier relationships.
In human science literature, such physical interaction as touch is essential to building relationships with others [27-30] and eliciting self-disclosure from the persons
who were touched [19, 31]. If physical interaction with a robot provides similar positive effects as humans do, it would be useful for building relationships with people byeliciting more self-disclosure from them.
This paper investigates whether physical interaction with a robot encourages selfdisclosure from an interaction partner. For this purpose, we developed a robot named“Moffuly” that resembles a large teddy bear and implemented a simple structure withwhich it hugs an interaction partner. We experimentally investigated the effects ofbeing hugged by it by measuring the amount of self-disclosure during interaction.

Fig. 1 Hug behaviors of robot

2

System overview

2.1

HardwareWe developed “Moffuly,” a robot that resembles a large teddy bear (Fig. 1). It is 200cm tall with two elbows (1*2 DOF) and a speaker. Its arms are 80 cm long, which isadequate to reciprocate a hug. To ensure safety when the robot gives a hug, we covered its frame with polypropylene cotton and used weak motors that can be easily
resisted if needed. We used speech synthesis software [32] to generate its voice. An
operator took over its speech recognition and choose its behaviors based on predetermined rules to control it.
2.2

Conversational behavior

For conversation with interaction partners, we prepared three main chat contents: selfintroduction, requesting self-disclosure from people, and replies. In the selfintroduction behaviors, the robot introduces itself with such a self-disclosure as “Hello, I’m Moffuly! Even though I look like a bear, my favorite food is electricity, nothoney.” We included self-disclosure contents because they are important for friendly
human-computer and human-robot interactions [16]. In the behavior that requests
self-disclosure from people, the robot asks to hear their stories: “I’d like to learn more
about you, could you please tell me something about yourself?” To respond to its
interaction partners, the robot uses reply contents: “I see,” and “you did your best,”.

3

In this experiment, we employed the Wizard-of-Oz approach [33] and systematically tele-operated it. Appropriate reply behaviors are chosen by the operator based on
the conversation contexts. Also, we designed the robot as a listener in this study toencourage self-disclosure. Therefore, it politely refuses to answer most questions:“Sorry, I don’t know much about that. But I’d love to hear more about you!” to prevent complex conversations about itself when the interaction partners ask questions.
2.3

Hug behavior

To realize reciprocated hugs from the robot, we designed it to first ask for a hug,
which it then returns, as follows. First, the participant’s position is fixed at a distanceof 75 cm from the front of the robot, which is based on the definition of the personal
distance where friendly people interact [34] (Fig. 1-left). Then the robot opens its
arms and says, “Before we start talking, would you please give me a hug?” (Fig. 1middle). After the person hugs the robot, it moves both of its arms until it touches the
person’s body and pats the person on the back (Fig. 1-right). The timings of the pats
are based on two rules. First, while the robot is talking, the timing is based on the endof its contents. Second, when the person is talking to the robot, the timing is either
based on the end of their conversations or on 30-second periods.

3

Experiment

3.1

Hypothesis and predictions

People disclose more personal information through physical interaction with others[19, 31]. Positive effects from physical interaction are not limited to humans; people
also received positive feelings toward robots through physical interaction with them[21-25]. Therefore, we believe that reciprocated hugs from a robot will also encourage more self-disclosure from a person who hugged the robot than non-reciprocatedhugs or no-hugs.
We also focused on people’s willingness to interact with the robot. Tactile stimulations through touch facilitate social bonding [27]. If people prefer reciprocated hugs
from a robot and start to build a social relationship with it, they might want to interact
with it again. Longer interaction would also encourage self-disclosure. Based on thesereasons, we make the following hypotheses:
Prediction 1: People who were hugged by a robot are more likely to interact withit longer than people who were not hugged by it.
Prediction 2: People who were hugged by a robot are more likely to disclose more
about themselves than people who were not hugged by it.
3.2

Participants

Thirty-two Japanese people (16 women and 16 men, whose average ages were 36.22,
S.D 9.64, the age range was from 20 to 52) were paid for participation.

4

3.3

Environment

We attached the robot to a wall and installed two cameras and microphones on theceiling and one camera/microphone near it. We used the information from the sensors
to analyze the experiment and to control the robot by an operator from another room.
3.4

Condition

The study had a between-participant design with the following two conditions. For
each condition, sixteen subjects (eight females and eight males) participated. In all theconditions, the conversational and the replay behaviors were identical, and the operator controlled the robot based on the same pre-defined rules in both conditions.
No-hug: The robot only chats with the participants; it did not request a hug. We also asked the participants to avoid physical interact with the robot and to stay in theirinitial position. Thus, they did not physically interact.
Reciprocated hug: The robot requests a hug from the participants, hugs themback, and then starts to chat. During the chats, it pats them on the back based on predefined rules.
3.5

Procedure

Before the experiment, the participants were given a brief description of our experiment’s purpose and procedure. We explained their interaction with the robot and literally demonstrated how to hug it (as shown in Fig. 1-right), except for participants whojoined the no-hug condition. We also explained that the robot’s face part with which
the participants’ faces make contact during hugging was replaceable to alleviate any
sanitation concerns. Moreover, we explained that since the robot’s conversation capabilities are limited, complex conversations are difficult and should be avoided. We
stressed that the robot likes listening to stories and encouraged them to talk with it.
After the above explanations, the experimenter left the participants in the experimentroom and started the experiment.
At the beginning of the experiment, the robot introduced itself to the participants,
and made its hug-request except in the no-hug condition. After that, the robot talked
with them using the self-disclosure contents and asked for stories/information or justoffered to listen to them. In the reciprocated hug condition, the robot hugged the participants and started patting them on the back based on the pre-defined rules. The
minimum length of the interaction time was 10 minutes; if the participants would liketo interact more, we extended the interaction time to 20 minutes in maximum. Thus,
the participants could freely finish the interaction any time after 10 minutes passed.
This research was approved by our institution’s ethics committee for studies involving human participants. Written, informed consent was obtained from all of them.
3.6

Measurement

In this study, we measured two items: the interaction time they spent with it, and theratio of the self-disclosure and non-self-disclosure conversations, i.e., the number of

5

the conversation related to self-disclosure divided by the number of the conversation
without self-disclosure. The reason of why we focused on the ratio is because ourexperiment also investigates the willingness to interaction with the robot by measuring interaction time. The different length of interaction time would have influences tothe total number of self/non-self-disclosure conversations, therefore we normalized
them by calculating the ratio between them.
For this measurement, first a coder transcribed all of the conversations of the participants from the recorded data. As a result, the coder segmented the conversation
data to 210 scripts, based on the conversation theme. Then the coder coded the transcribed 210 conversation scripts into either the self-disclosure (e.g., including suchprivate topics as hobbies, personal experiences, private matters, and so on) or nonself-disclosure (e.g., mundane topics like the weather) categories. After that, to investigate the coding’s validity, another coder coded 10% of these data [35], and we calculated the kappa coefficient [36], which was 0.74, indicating substantial agreement
between coders.
We additionally measured the subjective impressions of the participants by a questionnaire that addressed perceived friendliness toward the robot on a 1-to-7 point
scale where 7 is the most positive and 1 is the most negative.

4

Results

4.1

Verification of predictions

Figure. 2 show the average interaction time they spent with the robot and its standard
error. We conducted an ANOVA for the interaction times, which showed significant
differences among the two conditions (F(1, 30)=26.965, p<.01, partial η2 =0.473).
Thus, prediction 1 was supported.
Figure 3 shows the average ratio of the self-disclosure and non-self-disclosure
conversations and its standard error. We conducted an ANOVA for this measurement
and it showed significant differences among the two conditions (F(1, 30)=11.930,
p<.01, partial η2 =0.285). Therefore, prediction 2 was supported.
** : p < .01

1000

Seconds

800
600
400
200

**

6
Self-disclodure / Non-self-disclosure

**

1200

** : p < .01

5

4
3
2

1

0
No-hugs

Reciprocated hugs

Fig. 2 The interaction time

No-hugs

Reciprocated hugs

Fig. 3 the ratio of conversation contents

6

4.2

Questionnaire results

Figure 4 shows the questionnaire results of the total impressions of the participants(average and standard error). We conducted an ANOVA for statistical analysis that it
did not show any significant differences among the three conditions (F(1,30)=0.884,
p=n.s.). Even though the interaction time with the robot and the ratio of the selfdisclosure and non-self-disclosure conversations were significantly different among
conditions, the total impressions were not different in this study.
4.3

Additional analysis: amount of self-disclosure

The experimental results showed that the interaction time and the ratio of the selfdisclosure and non-self-disclosure conversations were significantly increased at thereciprocated hug condition than the no-hug condition. As an additional analysis, we
also measured the total amount of the self-disclosure and non-self-disclosure conversations and investigated whether the amount of them are different between the conditions.
Figure 5 shows the average numbers of the self-disclosure and non-self-disclosure
conversations and its standard error. We conducted a two-way repeated-measure
ANOVA with mixed factors: condition and category. Significant main effects wererevealed in the category factor (F(1, 30)=15.127, p<.01, partial η2=.335), and in theinteraction within them (F(1, 305)=5.530, p<.05, partial η2=.156). There is no significant difference in the condition factor (F(1, 30)=1.055, p=n.s.).
Multiple comparisons with the Bonferroni method for the simple main effects ofcondition in the self-disclosure category were significant in reciprocated hug > nohug (p<.05). There was no significance in the non-self-disclosure category (p=n.s.).
Multiple comparisons with the Bonferroni method in the simple main effect category in the reciprocated hug condition were significant in self-disclosure > non-selfdisclosure (p<.01). There was no significance in no-hug condition (p=n.s.). These
results would become another evidence for supporting the prediction 2.
** : p < .05

7** : p < .01

n.s.

7

**

6
6

5

5

4

4

3

3
2

2

1
No-hugs1
No-hugs

Reciprocated hugs

Fig. 4 The total impression

Non-self-disclosure

Reciprocated hugs
Self-disclosure

Fig. 5 The number of conversations

7

4.4

Observations of interactions and self-disclosure

In both conditions, the typical interaction pattern was that participants talked about
themselves. In the reciprocated hug, most seemed surprised by the robot’s hugrequest behavior, even though we did explain it to them. About self-disclosure, mostparticipants related such positive contents as their recent travels, experiences of summer festivals, and so on. For example, one participant talked about his hobby (running) and described recently completing a full marathon. On the other hand, someparticipants talked about relatively serious contents, such as failing an entrance examination for graduate school, difficulties of bringing up children, and so on. For example, one participant expressed melancholy about the remainder of his life, especiallyafter his children leave home. He said to the robot, "Life is full of ups and downs."

5

Discussion

5.1

Design implications

Our experiment results revealed that reciprocated hugs from a robot influence people’s self-disclosure and encourage more interaction with it. Since not only physicalinteraction but also self-disclosure are essential to construct social relationships in
human-human interaction [19, 27-31], the capabilities of reciprocated hugs would be
useful for constructing social relationships between people and robots.
A clinical context is one possible application for huggable robots, because past research works showed the advantages of autonomous agents in such contexts. For example, Lucas et al. reported that patients preferred to disclose personal information toautonomous agents than to tele-operated ones [12]. Even though our robot is teleoperated, the capabilities of reciprocated hugs for autonomous robots might contributeto clinical uses by eliciting disclosure from patients, both constructing social relationships and understanding their physical/mental situations.
Even though the results revealed that reciprocated hugs from a robot encouraged
more interaction with the robot, it is still unknown why such effects occurred. In our
settings, it is still unknown which factor contributed to longer interaction: a reciprocated hug or a hug feeling of the robot. One interesting future work is to compare ahug-only condition where participants hug the robot but it did not reciprocate hug.
From another perspective, to investigate further relationships using other measurements would be useful to discuss the effects of reciprocated hug. One interestingtrial is to investigate such physiological measurements as cortisol, which indicates thestress levels of people. A past research has already investigated huggable medium
effects, which decrease stress levels by conversations [21], but it did not address reciprocated hugs from a robot. Other physiological measures, such as brainwaves also
help to understand the reciprocated hug effects. A past research work analyzed robot’s
touch effects through EEG analysis shows that they affect Medial Frontal Negativity,
whose amplitude is correlated with feeling of unfairness [25].

8

5.2

Limitations

This research work has several limitations. Since our experiment was conducted witha robot with a teddy-bear like huggable appearance, its generality is limited. We cannot ensure that our findings can be applied to all interactive robots because size andtouch feelings are essential for hug interactions. Moreover, we only investigated asimple reciprocated hug i.e., patting a person’s back, even though obviously many
hug styles exist in human-human interaction. Investigating different hug-style situations is critical, e.g., standing face-to-face instead of sitting on the floor. However, we
believe that our setting is still adequate to offer essential knowledge for researchers
who are interested in hug interaction with interactive robots.

6

Conclusion

We focused on the effects of reciprocated hugs from a robot for eliciting selfdisclosure. Even though previous research investigated the positive effects of hug
interactions with robots, such works less focused on being hugged by a robot during
the interactions. To investigate the effects of reciprocated hugs from a robot, we developed a large, teddy-bear type robot that can reciprocate hugs from people and conducted a between-subjects experiment with two conditions: hug and no-hug.
Our experiment results showed that reciprocated hugs from the robot significantly
increased the amount of self-disclosure from people. Also, more participants who
were hugged by the robot interacted with it again than participants who were nothugged by it. Thus, this study showed that a reciprocated hug from a robot influences
self-disclosure and willingness to interact.

Acknowledgements
This research work was supported by JSPS KAKENHI Grant Numbers JP15H05322
and JP16K12505.

References
1. Yun, S.-S., Kim, M., and Choi, M.-T., “Easy Interface and Control of Tele-education
Robots,” International Journal of Social Robotics, vol. 5, no. 3, pp. 335-343 (2013).
2. Shiomi, M., et al., “Can a Social Robot Stimulate Science Curiosity in Classrooms?,”
International Journal of Social Robotics, vol. 7, no. 5, pp. 641-652 (2015).
3. Komatsubara, T., Shiomi, M., Kanda, T., Ishiguro, H., and Hagita, N., “Can a social robot
help children's understanding of science in classrooms?,” in Proceedings of the second
international conference on Human-agent interaction, Tsukuba, Japan, pp. 83-90. (2014).
4. Tanaka, F., Isshiki, K., Takahashi, F., Uekusa, M., Sei, R., and Hayashi, K., “Pepper learns
together with children: Development of an educational application,” in Humanoid Robots(Humanoids), 2015 IEEE-RAS 15th International Conference on, pp. 270-275, 2015.

9
5. Tanaka, F., and Matsuzoe, S., “Children teach a care-receiving robot to promote theirlearning: Field experiments in a classroom for vocabulary learning,” Journal of HumanRobot Interaction, vol. 1, no. 1 (2012).
6. Felzmann, H., Beyan, T., Ryan, M., and Beyan, O., “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).
7. Mordoch, E., Osterreicher, A., Guse, L., Roger, K., and Thompson, G., “Use of social
commitment robots in the care of elderly people with dementia: A literature review,”
Maturitas, vol. 74, no. 1, pp. 14-20 (2013).
8. Shiomi, M., et al., “Effectiveness of Social Behaviors for Autonomous Wheelchair Robotto Support Elderly People in Japan,” PLoS ONE, vol. 10, no. 5, pp. e0128031 (2015).
9. Kruijff-Korbayová, I., Oleari, E., Bagherzadhalimi, A., Sacchitelli, F., Kiefer, B.,
Racioppa, S., Pozzi, C., and Sanna, A., “Young Users’ Perception of a Social Robot
Displaying Familiarity and Eliciting Disclosure,” in International Conference on Social
Robotics, pp. 380-389, 2015.
10. Birnbaum, G. E., Mizrahi, M., Hoffman, G., Reis, H. T., Finkel, E. J., and Sass, O., “Whatrobots can teach us about intimacy: The reassuring effects of robot responsiveness tohuman disclosure,” Computers in Human Behavior, vol. 63, pp. 416-423 (2016).
11. Martelaro, N., Nneji, V. C., Ju, W., and Hinds, P., “Tell me more: Designing hri toencourage more trust, disclosure, and companionship,” in The Eleventh ACM/IEEEInternational Conference on Human Robot Interaction, pp. 181-188, 2016.
12. Lucas, G. M., et al., “It’s only a computer: virtual humans increase willingness todisclose,” Computers in Human Behavior, vol. 37, pp. 94-100 (2014).
13. Mumm, J., and Mutlu, B., “Human-robot proxemics: physical and psychologicaldistancing in human-robot interaction,” in Proceedings of the 6th international conferenceon Human-robot interaction, pp. 331-338, 2011.
14. Huang, C.-M., Iio, T., Satake, S., and Kanda, T., “Modeling and Controlling Friendlinessfor An Interactive Museum Robot,” in Robotics: Science and Systems, pp., 2014.
15. Kanda, T., Sato, R., Saiwaki, N., and Ishiguro, H., “A two-month field trial in anelementary school for long-term human–robot interaction,” IEEE Transactions on
Robotics, vol. 23, no. 5, pp. 962-971 (2007).
16. Kanda, T., et al., “A communication robot in a shopping mall,” Robotics, IEEE
Transactions on, vol. 26, no. 5, pp. 897-913 (2010).
17. Weizenbaum, J., “ELIZA—a computer program for the study of natural language
communication between man and machine,” Communications of the ACM, vol. 9, no. 1,
pp. 36-45 (1966).
18. Moon, Y., “Intimate exchanges: Using computers to elicit self-disclosure fromconsumers,” Journal of Consumer Research, vol. 26, no. 4, pp. 323-339 (2000).
19. Cozby, P. C., “Self-disclosure: a literature review,” Psychological bulletin, vol. 79, no. 2,
pp. 73 (1973).
20. Powers, A., Kiesler, S., Fussell, S., and Torrey, C., “Comparing a computer agent with ahumanoid robot,” in Human-Robot Interaction (HRI), 2007 2nd ACM/IEEE International
Conference on, pp. 145-152, 2007.
21. Sumioka, H., Nakae, A., Kanai, R., and Ishiguro, H., “Huggable communication medium
decreases cortisol levels,” Scientific Reports, vol. 3, pp. 3034 (2013).

10
22. Shiomi, M., Nakagawa, K., Shinozawa, K., Matsumura, R., Ishiguro, H., and Hagita, N.,“Does A Robot’s Touch Encourage Human Effort?,” International Journal of Social
Robotics, pp. 1-11 (2016).
23. Yu, R., Hui, E., Lee, J., Poon, D., Ng, A., Sit, K., Ip, K., Yeung, F., Wong, M., andShibata, T., “Use of a Therapeutic, Socially Assistive Pet Robot (PARO) in Improving
Mood and Stimulating Social Interaction and Communication for People With Dementia:
Study Protocol for a Randomized Controlled Trial,” JMIR research protocols, vol. 4, no. 2(2015).
24. Nakagawa, K., Shiomi, M., Shinozawa, K., Matsumura, R., Ishiguro, H., and Hagita, N.,“Effect of Robot’s Whispering Behavior on People’s Motivation,” International Journalof Social Robotics, vol. 5, no. 1, pp. 5-16 (2012).
25. Fukuda, H., Shiomi, M., Nakagawa, K., and Ueda, K., “‘Midas touch’in human-robot
interaction: Evidence from event-related potentials during the ultimatum game,” inHuman-Robot Interaction (HRI), 2012 7th ACM/IEEE International Conference on, pp.
131-132, 2012.
26. Shiomi, M., Nakata, A., Kanbara, M., and Hagita, N., “A Hug from a Robot Encourages
Prosocial Behavior,” in Robot and Human Interactive Communication (RO-MAN), 2017
26th IEEE International Symposium on, pp. to appear, 2017.
27. Bartz, J. A., Zaki, J., Bolger, N., and Ochsner, K. N., “Social effects of oxytocin inhumans: context and person matter,” Trends in cognitive sciences, vol. 15, no. 7, pp. 301309 (2011).
28. Burgoon, J. K., Buller, D. B., Hale, J. L., and Turck, M. A., “Relational messages
associated with nonverbal behaviors,” Human Communication Research, vol. 10, no. 3,
pp. 351-378 (1984).
29. Fisher, J. D., Rytting, M., and Heslin, R., “Hands Touching Hands: Affective andEvaluative Effects of an Interpersonal Touch,” Sociometry, vol. 39, no. 4, pp. 416-421(1976).
30. Gallace, A., and Spence, C., “The science of interpersonal touch: an overview,”
Neuroscience & Biobehavioral Reviews, vol. 34, no. 2, pp. 246-259 (2010).
31. Jourard, S. M., and Rubin, J. E., “Self-disclosure and touching: A study of two modes ofinterpersonal encounter and their inter-relation,” Journal of Humanistic Psychology, vol.
8, no. 1, pp. 39-48 (1968).
32. Kawai, H., Toda, T., Ni, J., Tsuzaki, M., and Tokuda, K., “XIMERA: A new TTS from
ATR based on corpus-based technologies,” in ISCA Speech Synthesis Workshop, pp. 179184. (2004).
33. Dahlbäck, N., Jönsson, A., and Ahrenberg, L., “Wizard of Oz studies: why and how,” inProceedings of the 1st international conference on Intelligent user interfaces, Orlando,
Florida, USA, pp. 193-200. (1993).
34. Hall, E. T., “The hidden dimension,” (1966).
35. Lombard, M., Snyder‐Duch, J., and Bracken, C. C., “Content analysis in masscommunication: Assessment and reporting of intercoder reliability,” Human
Communication Research, vol. 28, no. 4, pp. 587-604 (2002).
36. Cohen, J., “A coefficient of agreement for nominal scales,” Educational and psychologicalmeasurement, vol. 20, no. 1, pp. 37-46 (1960).