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International Journal of Social Robotics manuscript No.
(will be inserted by the editor)

Does a Robot’s Touch Encourage Human Eﬀort?
Masahiro Shiomi · Kayako Nakagawa · Kazuhiko Shinozawa · Reo
Matsumura · Hiroshi Ishiguro · Norihiro Hagita

Received: date / Accepted: date

Abstract The paper investigated the eﬀects on a person being touched by a robot to motivate her 1 . Human
science literature has shown that touches to others facilitate eﬀorts of touched people. On the other hand,in the human-robot interaction research field, past research has failed to focus on the eﬀects of such touches
from robots to people. A few studies reported negative
impressions from people, even if a touch from a person
to a robot left a positive impression. To reveal whether
robot touch positively aﬀects humans, we conducted anexperiment where a robot requested participants to perform a simple and monotonous task with/without touch
interaction between a robot and participants. Our experiment’s result showed that both touches from therobot to the participants and touches from the participants to the robot facilitated their eﬀorts.
Keywords Communication robot · touched by arobot · motivation · behavior change · persuasionof others, is one interesting social behavior of humans[2, 3]. This phenomenon has been found not only in humans but also in computer agents. For example, Rickenberg et al. reported the facilitation eﬀects of computer
agents [4], and other work reported the facilitation effects of a robot’s presence [5, 6].
In human science literature, physical interactions,particularly haptic interactions, change the behaviors
of others and facilitate their eﬀorts. Burgoon et al. reported that touching positively aﬀects impressions [7].
Other research work suggested that such positive evaluations might increase compliance with requests from aperson who did the touching [8]. Several research works
reported that touching changes behaviors in actual settings: generous tips [9], compliance with the suggestions of a restaurant’s employee [10, 11], convincing thefinders of money in a phone booth to return it [12],and increased sales in stores [13]. These research works
suggest that robots that touch humans might produce
similar facilitation eﬀects.

1 Introduction
Social facilitation, which is the tendency for people todo simple and monotonous tasks better in the presence
M. Shiomi, K. Shinozawa, R. Matsumura, H. Ishiguro ·N. Hagita are with Intelligent Robotics and Communication Labs., ATR, 2-2-2 Hikaridai Keihanna-Sci. City, Kyoto,
Japan. R. Matsumura and K. Nakagawa are with ResearchCenter for Advanced Science and Technology, The Universityof TokyoTel.: +81-774-95-1432
Fax: +81-774-95-1401
E-mail: m-shiomi@atr.jp
1
This paper is an extended version of a previous work [1]and contains additional experimental results and more detailed discussions.

Fig. 1 Robot’s touch

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2

However, no research has clearly investigated facilitation eﬀects through touch interaction with robots.
Currently, such physical interactions as touching by
robot are mainly used for the direct physical interfacesof robot locomotion [14] or physical assistance for theelderly [15]. A few research work have investigated theeﬀects of touch from people to a robot in therapy context [16, 17]. We note that they are using an animallike robot to encourage touches from interacting people
therefore the robot did not need explicit requests torealize touch interactions.
Thus, there are still unknown about facilitation effects through touch interaction, including of a touchfrom a robot to a person and/or a touch caused by explicit requests by robots, which have diﬀerent kinds ofappearances from past research works. If we could identify such facilitation eﬀects, design of haptic interaction
for robots that would contribute to such diﬀerent applications as education and health-care where people
must be motivated to do simple and monotonous but
important tasks.
This research investigates three questions. The firstis whether a touch from people to a robot facilitates human eﬀorts toward a simple task, even if the robot explicitly requests touch to people. The second is whether
a touch from a robot to people also facilitates eﬀorts
toward a simple task. The third is the gender eﬀect,because past research [18] found that males more positively react to physical contact than females (we notethat their research work focused on handshaking, which
has specific meaning in interactions diﬀerent from ourresearch work); obviously, this conclusion is related to
the future design of the touch interaction of robots.
Thus, we investigate the eﬀects of physical interaction
with robots from a facilitation point of view. To answer
these questions, we conducted an experiment where arobot facilitated participant eﬀorts toward a simple andmonotonous task through touch interaction.
To make clear the type of touch interaction initiatedby a robot or a person, we defined two kinds of touch
types based on the robot’s viewpoint: robot’s touch andhuman’s touch. “Robot’s touch” indicates a touch froma robot to a person, and “human’s touch” indicates atouch from a person to a robot.
2 Related Work
2.1 Facilitation eﬀects of robots
Some past research investigated whether the existenceof agents facilitates the interaction eﬀorts of people.
Rickenberg et al. reported the facilitation eﬀects of computer agents [4]. Robotics researchers confirmed such

Masahiro Shiomi et al.

facilitation eﬀects with robots. Through an experimentwith a robot, Woods et al. identified the influence of thesocial context and social facilitation eﬀects, which include task complexity, evaluation context, and the typeof presence [5]. Riether et al. reported significant evidence for the social facilitation eﬀects with a robotic
presence [6].
A few research works have focused on the facilitationeﬀects of robot behaviors, but not only on the existenceof such agents. Nakagawa et al. concluded that a robot’s
whispering behavior increases people’s motivation during a simple and monotonous task [19]. However, theseresearch works did not focus on the eﬀects of facilitationeﬀorts through touch interaction.

2.2 Haptic interaction with agentsThe advancement of sensing capabilities enables robots
to understand human haptic interactions. For example,
Salter et al. used a ball type robot with proprioceptive sensors to categorize human-robot interaction [20].
Cooney installed inertia sensors on a humanoid robot
to recognize full-body gestures by haptic interactions[21]. Lee et al. developed a doll type robot with skinsensors to recognize haptic interactions [22]. These research works enabled robots to respond more naturally
to haptic interaction from people.
Due to an increase of the recognition capabilities ofrobots, several researchers have started to understandthe eﬀects of haptic interaction (particularly touching)by focusing on the changes of the perceptions of people
toward a robot through touch interaction. For example, Shibata et al. developed a seal robot named Parofor therapy with senior citizens through interaction including touches from people by it (human’s touches)[16, 17]. These research works reported that human’s
touching without explicit requests of touches produced
positive feedback to robots, such as increasing friendly
impressions.
Other research works focused on touching from robot
to people (robot’s touches). Interestingly, these researchworks suggested the negative eﬀects of robot’s touching,such as negative impressions of a robot. For example,
Cramer et al. investigated the eﬀectiveness of touchingby a robot and found that it decreases machine-likenessbut negatively aﬀects dependability [23, 24]. Tiﬀany et
al. investigated the changes of the impressions of robot’s
touches, but their main research focus is the eﬀects ofverbal cues during touching [25]. These research works
did not focus on the eﬀects on facilitation eﬀorts throughtouch interaction.

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Does a Robot’s Touch Encourage Human Eﬀort?

3 Experiment
To evaluate the eﬀectiveness for facilitating people’s efforts, we conducted a laboratory experiment in which
participants performed a simple and monotonous task.
In this section, we describe its details.
3.1 Hypothesis
3.1.1 Hypothesis about human’s touching
Past research works about haptic interaction of robots
showed positive eﬀects [16,17]. Moreover, other research
work shows that an explicitly request of a physical contact (whispering behavior) by a robot is accepted andproduces positive eﬀects [19]. Therefore we believe thathuman’s touch interaction would make positive eﬀects
even if a robot explicitly request a touch to people.
Based on these considerations, we made the following
hypothesis:
Prediction 1: People who are touched to the robot
will do more of the requested tasks than people who
were not touched.
3.1.2 Hypothesis about robot’s touching
We assume that robot’s touching will have similar positive eﬀects on interaction with people based on the human science literature. However, several research works
showed that robot’s touches from a robot cause negative impressions, which might deleteriously aﬀect interaction. Since the phenomena related to robot’s touching from robots are highly unexplored, we made twocontradictory hypotheses about their eﬀects based on
diﬀerent theories and considerations.
To investigate the eﬀects of robot’s touch, we designed the robot to ask the person to touch its hand andthen it actively touches the person’s hand that is touching the robot due to limitations of our robot hardware
capabilities and safety for a touching behavior by the
robot, Thus, robot’s touching behavior conducts a human’s touching behavior first; when the person touches
the robot’s right hand, the robot moves its left hand to
make contact with the person’s hand. Then the robot
strokes the person’s hand with its left hand. These settings would make mixing eﬀects of human’s touch androbot’s touch, therefore we prepared three conditions
to investigate each touch eﬀects (details of conditions
are described later).
Hypothesis that assumes positive eﬀects
Human science literature has concluded that robot’s
touching facilitates the eﬀorts of others. Research on

3human’s touching in the human-robot interaction fieldagrees that touch interaction creates positive interaction with people. Physical interaction with a robot is
acceptable because humans perceive a robot as a social
being [26, 27]. Therefore, we believe that robot’s touching will facilitate people’s eﬀorts toward tasks requestedby that same robot. Based on these considerations, wemade the following hypothesis:
Prediction 2-A: People who are touched by a robot(and touched to the robot beforehand) will do morerequested tasks than people who touched the robot andthose who did not touch it.
Hypothesis that assumes negative eﬀects
The past research works, which focused on robot’s
touches, showed that such behavior causes negative impressions. Touch interaction is not considered appropriate behavior in every situation [9]. It is also reasonable
to assume that people might hesitate to engage in touch
interaction from a robot. On the other hand, researchon human’s touching showed positive impressions of therobot, indicating that only physical interaction frompeople is acceptable; in other words, physical interaction initiated by a robot is not acceptable. Therefore, we
believe that robot’s touching will not facilitate people’s
eﬀorts toward tasks requested by that robot. Based onthese considerations, we made the following hypothesis:
Prediction 2-B: People who are touched by the robot(and touched to the robot beforehand) will do fewer
requested tasks than people who touched the robot andthose who did not touch it.
3.1.3 Hypothesis about gender eﬀects
Gender is one considerable factor to relate the eﬀects oftouch. Human science literature reports [18] that males
perceive physical contact more positively than females.
We assume that gender also influences the eﬀects oftouching a robot and being touched by it, similar tohandshaking behaviors. Based on these considerations,
we made the following hypothesis:
Prediction 3: Males who touched the robot will domore requested tasks than females who touched it.
3.2 Robot
For this experiment, we used robovie-mR2 (Fig. 2), aninteractive humanoid robot characterized by its humanlike physical expressions. It has four DOFs in its arms,
three in its head, and four in its eyes. Its hands are soft
sponge balls to ensure safety when it touches a person.
The robot is 42 cm tall. We used a corpus-based speechsynthesis to generate speech [28].

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4Masahiro Shiomi et al.
Initial position
Control board(VS-RC)

Sensor board(FPM-15PA)

Arm

Cuff

Drag the circle into the square
Sponge ballFig. 4 Computer task in experiments
Fig. 2 Desktop-sized robot: robovie-mR2

touching behavior, the robot extends its right hand andsays, “Please hold my hand while I talk to you” (Fig.
3(a)). The robot did not actively touch the participants.
As written in above, in the robot’s touching behavior, first the robot asks the person to touch its hand
and then it actively touches the person’s hand that is
touching the robot. Its left hand moves horizontally towiden the space between both of its hands until the sensor value drops below a threshold (Fig. 3(b)). To design
the robot’s touching behavior, we employed the knowledge of human science literature that investigates theeﬀects of touching speed on impressions [29]. Since thispaper reported that touching with 5 cm/sec speed wasevaluated more positively than 0.5 or 50 cm/sec, we set
the speed of the robot’s hand during robot’s touchingat about 5 cm/sec.
(a) Human’s touching(b) Robot’s touching

3.4 Participants
Fig. 3 Touching behavior

We installed a pressure sensor (FPM-15PA) in the
robot hands to detect pressure. The sensor arrangement
is shown on the right side of Fig. 2. The air-filled cuﬀ
was stuﬀed between the component of the left forearmand the sponge ball, so the atmospheric pressure inside
the cuﬀ increases when the ball (as the robot’s hand)feels pressure from the participant’s hand. The positionof the robot’s hand was controlled in response to thesensor output to prevent touching a person too strongly.
To determine adequate threshold values of the sensors
that enable consistent touching of participant hands, we
empirically adjusted the threshold values to 32 millibars
higher than the value without touching to control thearm movement during robot’s touching.

3.3 Touching behavior
We prepared two touching behaviors for our experiment: human’s touch and robot’s touch. In the human’s

We recruited 33 university students (15 men and 18
women whose ages averaged 20.9, S.D. is 2.2) from the
web regardless of major or specialty and without interaction experience with our robot, to prevent biased
attitudes toward the robot.

3.5 Task
In the experiment, we adopted a simple and monotonoustask whose degree of eﬀort was objectively measured toinvestigate whether robot’s touching facilitated humaneﬀort. We prepared an on-screen task by referring to a
study of behavioral economics [30]. In this task, participants dragged a circle on the left side to the square on
the right side on the screen as many times as possible.
After dragging the circle into the square, the circle disappears, and a new one appears on the left side. The
task ends after the participants press the ESC key (theycan terminate the task anytime) or the maximum time(ten minutes) has expired. The participants were notinformed of this time period.

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Does a Robot’s Touch Encourage Human Eﬀort?

5ParticipantPartition

PC

Table 1 Experiment flow
Speaker

Speech and behavior

Robot

Hello! I’m robovie-mR2. Thanks forcoming.

Operator

2.3 m

Participant

You’re welcome.

Robot

Well, I’d like to ask you a favor.
Please hold my hand while I talk toyou. (Robot extends its right arm.)

Participant

OK (Participant holds the hand).

Fig. 5 Experimental settings

Robot(Robot starts stroking the participant’s hand at an automatically controlled pressure.)

3.6 Conditions

Robot

I’d like you to do the following task
as well as you can. Its procedure isdisplayed on the computer screen on
your right.

Robot(Robot stops stroking the participant’s hand.)Robot

Do you understand?

Robot

controlPC

4.8 m

We used a between-participant experiment design withthree conditions to investigate the robot’s touch eﬀects.
– No touching: the robot asks the participants to perform the task without touching.
– Human’s touching: the robot requests a human’s
touch from the participants (Fig. 3(a))– Robot’s touching after human’s touching: the robot
requests a human’s touch from the participants andthen actively touches them when they are listening
to it (Fig. 3(b)).

3.7 Procedure
The experiment was conducted in a laboratory room
(Fig. 5). “robovie-mR2” was placed on a table, and alaptop computer for the task was placed on another table. Before the session, participants were given a brief
description of the experiment’s purpose and procedure.
They were randomly assigned to the experiment conditions; thus eleven participants were assigned to eachcondition. The gender ratio in each condition was counterbalanced.
At the beginning of the experiment, the participants
sat in front of the robot. During the interaction, therobot requested them to perform the task. After the
robot’s request, they started the task. We prepared aten-second training phase with which they could practice the dragging task. The participants could terminate
the task whenever they want.
The flow of the robot’s conversation, behaviors, andtypical responses of the participants are shown in Table
1. The timing of the robot’s behavior was controlled by
the operator.

ParticipantRobot[*1, *2]

[*2]

[*2]

Yes.
Thank you. OK, let’s get started!
*1: human’s touching condition*2: robot’s touching after human’s touching condition

3.8 Measurements
3.8.1 Objective measurements
To investigate the touching behavior eﬀects of the robot
toward eﬀort facilitation, we measured two items: thenumber of actions (dragged circles) and the working
time (time spent on task)

3.8.2 Subjective measurements
We prepared a questionnaire that addressed the perceived friendliness to measure the subjective impressions of the participants. This measurement consists of
one item “I think the robot was friendly.” After the session, participants answered a browser-based questionnaire on a 1-to-7 point scale where 7 is the most positive
and 1 is the most negative.

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6

Masahiro Shiomi et al.
**
400

**

Number of actions

300

200

100

(a) Greeting(b) “Please hold myhand.”
0
No touch

Robot’s touch after
human’s touch

Human’s touch

Fig. 7 Average number of actions. (** indicates p <.01)

**

600

450

(d) Robot strokes hand
while requesting thetaskWorking time (seconds)(c) Participant holds
robot’s hand

**

300

150

0
No touch

Human’s touch

Robot’s touch after
human’s touch

Fig. 8 Average working time (** indicates p < .01)(e) “OK,
started”

let’s

get

(f) Participant starts
the task+

Fig. 6 Experimental sequences

7+

4 Results
4.1 Verification of hypotheses 1 and 2
We analyzed the number of actions (Fig. 7) and theamount of working time (Fig. 8). To test our hypotheses, we conducted a one-factor between subjects ANOVA
for the number of actions. There was a significant difference among conditions (F (2, 30) = 6.30, p = .005,
partialη 2 = 0.30). Multiple comparisons with the Scheﬀemethod revealed significant diﬀerences: robot’s touchingafter human’s touching > human’s touching (p = .045),
robot’s touching after human’s touching > no touching (p = .008), but no significant diﬀerence between
no touching and human’s touching (p = .754). Thismeans that robot’s touching after human’s touching significantly increased the number of actions; but we notethat the statistical power of the results is below 0.80.

Perceived friendliness

6
5
4
3
2
1
No touch

Human’s touch

Robot’s touch after
human’s touch

Fig. 9 Perceived friendliness (+ indicates p < .1)

We also conducted a one-factor between subjects
ANOVA for the amount of working time. There wasa significant diﬀerence among conditions (F (2, 30) =5.84, p = .007, partialη 2 = 0.28). Multiple comparisons with the Scheﬀe method revealed significant differences: robot’s touching after human’s touching > hu-

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Does a Robot’s Touch Encourage Human Eﬀort?

Number of actions

Working time

400

600

300

450

200

300

100

150

0
No touch

Human’stouch

70
Robot’s touch
No touch
after human’s touch
FemaleHuman’stouch

Robot’s touch
after human’s touch

Male

Fig. 10 Average of task performances by gender

man’s touching (p = .047), robot’s touching after human’s touching > no touching (p = .012), but no significant diﬀerences between no touching and human’s
touching (p = .832). This means that robot’s touchingafter human’s touching significantly increased the working time; but we note that the statistical power of theresults is below 0.80.
These results support hypothesis 2-a and do notsupport hypotheses 1 and 2-b. Robot’s touching positively aﬀected the facilitation of participant eﬀorts ofa simple and monotonous task.

4.2 Verification of hypothesis 3
We analyzed the number of actions and the amountof working time by conducting a two-factor between
subjects ANOVA; the two factors were touch condition
and gender (Fig. 10). There were marginal diﬀerences
between gender (number of actions: F (1, 27) = 3.80,
p = .062, partialη 2 = 0.12, working time: F (1, 27) =3.97, p = .057, partialη 2 = 0.13), but no interactioneﬀect (number of actions: F (2, 27) = .687, p = .512,
working time: F (2, 27) = .364, p = .698). These results
did not clearly support hypothesis 3.

5 Discussion
5.1 Accuracy and average time of tasks
In this section, we discuss how the participants’ detailbehaviors in the tasks were changed between conditions.
For this purpose, we measured the accuracy of the tasks(i.e., successful ratio of each task) and the average ofdrag time (i.e., average speed of each task).

Firstly, we analyzed the accuracy of the tasks: notouching is 90.46% (S.D is 0.06), human’s touching is90.70% (S.D. is 0.09), and robot’s touching after human’s touching is 92.12% (S.D is 0.04). We conducted
an one-factor between subjects ANOVA for the accuracy rate. There was no significant diﬀerences among
condition (F (2, 30) = 0.18, p = 0.84, partialη 2 =0.01).
Secondly, we analyzed the average of drag time: notouching is 0.58 sec (S.D is 0.10), human’s touching is0.68 sec (S.D. is 0.59), and robot’s touching after human’s touching is 0.49 sec (S.D is 0.05). We also conducted an one-factor between subjects ANOVA for the
average drag time. There was no significant diﬀerencesamong condition (F (2, 30) = 0.80, p = 0.46, partialη 2 =0.05).
These results indicate that robot touch did not increase the accuracy and the speed of the tasks. However,in both touch conditions, the participants did more task
than no touching condition. These results show that the
robot touch would contribute to the participants’ performance toward the tasks; the participants did moretasks without increasing of errors.

5.2 Why did robot’s touching produce positive eﬀects?
Our experimental results showed the positive eﬀects ofrobot’s touching, which are diﬀerent trends from several
previous research works that reported negative eﬀects
of such robot’s touching. Next we clarify why these differences occurred.
In our experiment, our robot first explicitly requested
human’s touching to the participants. Since this is one
diﬀerence from previous research, such requests might
reduce the feelings of discomfort of touching rather thanthe unexpected touching by the robot. The robot’s appearance, which is also diﬀerent from past research works,
might aﬀect the perceptions of people toward it, androbot’s touching might cause positive eﬀects. However,
we believe that past research works also used similar
policies to set-up their robots, so this might not be themain reason for the diﬀerences.
Takemura reported that positive evaluations increased
compliance with requests from a person who was touchedby other person [8]. We analyzed the perceived friendliness toward the robot (Fig. 9) by conducting a onefactor between subjects ANOVA for it. There was a significant diﬀerence among the conditions for perceived
friendliness (F (2, 30) = 5.12, p = .033, partialη 2 =0.20 ). Multiple comparisons with the Scheﬀe method
revealed marginal diﬀerences: robot’s touching after human’s touching > human’s touching (p = .072), robot’s
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8.072), but there was no significant diﬀerence between
no touching and human’s touching. The results suggestthat robot’s touching increased the friendliness morethan human’s touching and no touching. These results
are evidence concerning why robot’s touches resulted
in positive eﬀects and why human’s touches resulted innegative eﬀects.
These results provide an interesting suggestion thata psychological index is not related to eﬀort facilitation, even though past research suggested that some
psychological indexes toward agents are related to such
eﬀorts [8, 31]. To solve these contradictory results, physiological measures might aid analysis, e.g., brain waves.
This is beyond the focus of this research. But our preliminary research, which is analyzing robot’s touch effects through EEG, analysis, shows that they aﬀect Medial Frontal Negativity, whose amplitude is correlatedwith feeling of unfairness [32]. Future work will investigate the details of the eﬀects of robot’s touches throughphysiological measurements.

Masahiro Shiomi et al.

Another factor would be activeness of touching. Because in this experiment, human’s touch is explicitly
requested by the robot, but the robot did not tell any
information about robot’s touch before human’s touching. If the robot explicitly tell information about its
touch, participants feeling would be changed.
Based on these knowledge coming from our researchwork, such future works would be interesting to deeply
understand the eﬀects of robot touch: 1) the robot or
the participants do more physical contact to investigate the eﬀects of amount of robot’s touch, 2) realizing
robot’s touch without human’s touch to investigate theeﬀect of type of touch and the mutual touch, 3) the
robot explicitly asks participants whether the robot cantouch to them would be interesting to investigate theeﬀects of activeness of touch and 4) the robot explicitly
asks participants to touch the other robot’s hand (e.g.,left hand) to investigate the eﬀects of the amount of
human’s touch.
5.4 Eﬀects for diﬀerent kinds of tasks

5.3 Amount and activity of touching
In the experiment we employed human’s touch to realize robot’s touch safely; it means that the robot touchedmore in robot’s touching after human’s touching condition than human’s touching condition. This limitation
would make future works about this research work. Here
we discussed the diﬀerences between conditions how
these factors are aﬀected to the eﬀects of touch.
Firstly, the amount of touch is possible factor whichhave eﬀects to experimental results. As written in above,
the amount of touch is diﬀerent between the conditions,but the types of touches are diﬀerent. Thus, robot’s
touching after human’s touching condition consists of
one robot’s touch and one human’s touch, and human’s
touching condition consists of one human’s touch only.
Amounts of touch between conditions are diﬀerent, but
their types are also diﬀerent. If we mixed the amount oftouch with diﬀerent touch types on discussion, it would
make additional confusions. Therefore, it is diﬃcult toclearly discuss the eﬀects of amount of touch from theexperiment results.
Secondly, mutual touch is also one of possible factors for positive eﬀects. Because “robot’s touch after human’s touch” condition realized mutual touch between
the robot and the participant, and it showed positiveeﬀects than other conditions which not realized mutual
touch. However, in our settings, diﬃcult to separate
interaction eﬀects between human’s touch and robot’s
touch. Therefore, this eﬀect is still unknown from ourexperimental results.

In the experiment, we employed a fitts-law based simple
and monotonous task. Therefore, it is still questionable
about the eﬀects of robot touch for other kinds of tasks,
such as more stressful or complex one. To discuss thispoint, we measured the perceived easiness towards thetask by questionnaires to investigate the perceptions of
the participants toward the tasks first. In all conditions
the average values of this metrics are more than 6 (1-to7 scale, no significant diﬀerences between conditions).
We also measured the perceived enjoyment of the tasks
in the experiments; the average values of this metrics
are less than middle (no significant diﬀerences between
the conditions, too). These results suggested that theworkload of the task was low but not enjoyable; repeating simple and monotonous tasks would be stressful forthe participants. We note that the participants in touchconditions did more tasks than no touch condition, even
if the tasks would be perceived as not enjoyable; it also
would suggest that robot touch has positive eﬀects forstressful tasks.
However, it is still questionable whether robot touch
would facilitate other kinds of tasks such as more complex and or diﬃcult (e.g., cognitively demanding) tasks.
This question might be interesting because past research works related to social facilitation eﬀects reported
that a physical existence increase the performance ofsimple tasks but decrease the performance of complex
tasks. If robot touch has similar trend towards facilitation of complex tasks, the robot touch should be carefully used for facilitation purposes; if robot touch eﬀects
are opposite from these trends, the worth of robot touch

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Does a Robot’s Touch Encourage Human Eﬀort?

would increase. This research question is out of scopefrom this research work, but it would be an interesting
future work for social robots.
Moreover, we think that robot touch would havepositive eﬀects for collaboration tasks. As written inabove, we are trying to analyze touch eﬀects throughEEG analysis. In this research work preliminary showedthat robot touch would decrease unfairness toward therobot [32]. These eﬀects would be important for collaboration tasks between robots and people.

5.5 Eﬀects for diﬀerent kinds of people
In this section we discuss the eﬀects of robot touch fordiﬀerent kinds of people such as elderly people, because
in this paper we conducted with student participants.
The diﬀerent properties of people would make diﬀerent
eﬀects.
Related to this topic, Nomura et al have well investigated the attitude or perceptions for robots between students and other people such as elderly people[33–35]. For example, they investigated the eﬀects ofeducational backgrounds in human-robot interaction;
they found that the students with educational backgrounds of natural science and technology feel morepoliteness and assertiveness towards the robot than students with a social science background [33]. Other research work reported that elderly people complied withthe real robot than students [34]. Moreover, they investigated the diﬀerences between elderly people andyoung people. They reported that elderly people positively evaluated the robot services and feel more friendliness than younger people through a field trial withmuseum robots [35].
From these research works, we think robot touch
would make positive eﬀects towards other people, particularly elderly people. In fact, a use of robot is oneof promising applications for elderly therapies; Shibata
et al have developed a seal type robot for elderly carethrough haptic interaction. The robot is already installed for elderly care homes and used for investigating
the eﬀects for elderly care [36].
Another future work is to investigate culture diﬀerences. Several research works which related to touch interactions with robots are already conducted, but such
culture diﬀerences are not strongly focused yet. As wellknown, meaning of touch behaviors is diﬀerent depending on cultures. Therefore investigating such diﬀerences
through touch interactions with robots would be one ofinteresting future works of this research work.

9

5.6 Gender eﬀects
Our experimental results showed marginal significant
diﬀerences of gender eﬀects. The p values exceed 0.05,
but we cannot rule out observed gender eﬀects. Our results resemble those from human science literature [18],which shows that males more positively perceive physical contact than females, even if touch style is diﬀerent.
Our experimental results also showed diﬀerent trendsof facilitation eﬀects between females and males. The
performance of female participants increased in boththe human’s/robot’s touching conditions, but the maleparticipants only seemed to be influenced by robot’s
touching (Fig. 10). The influences of robot touchingseem diﬀerent between genders. The touching interaction of robots should be designed that facilitates humaneﬀorts. For example, a robot could change its touch interaction design depending on the gender of the interaction person.

5.7 Negative eﬀects
In this section, we discuss whether the robot’s touch
has negative eﬀects from our experimental results. One
possibility is that participants felt compelled to work
more through the robot’s touch. We did not measuresuch negative eﬀects like a feeling of compelling via
questionnaires, but free descriptions of questionnaires
did not include such negative impressions toward therobots touch.
We note that the robots touch is designed to constantly pat participants hands during conversation to
avoid specific meanings of touch timing or strengths.
Because if the robot touches or changes its behaviors byconsidering of conversational contents, it would have an
implied intention to emphasize of requests. For example, if the robot strongly pat or touch a human’s handwhen the robot started a request, it would have implicit
meaning; in such situation negative eﬀects may occur
more.
As a future work, the design of synchronization between such conversational contents and touch timing
or behaviors would be important to avoid negative impressions.

5.8 Limitations
Since our experiment was conducted with an existing
robot, robovie-mR2, robot generality is limited. We cannot ensure that our findings can be applied to all interactive robots, particularly unhuman-like robots. Its

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10perceived gender of the robot might change the eﬀects
of human’s/robot’s touching.
The number of participants are relatively small, students only, and diﬀerent number of male/female. The
values of eﬀect size showed the valid results; but thestatistical powers of the results are below 0.80.

Masahiro Shiomi et al.

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Masahiro Shiomi received M.E. and Ph.D. degrees
in engineering from Osaka University, Osaka, Japan, in
2004 and 2007. From 2004 to 2007, he was an Intern
Researcher at the Intelligent Robotics and Communication Laboratories (IRC) and is currently a Researcher
at IRC at the Advanced Telecommunications Research

11

Institute International (ATR) in Kyoto, Japan. His research interests include human-robot interaction, interactive humanoid robots, networked robots, and field trials.
Kayako Nakagawa Kayako Nakagawa received her
M.E. and Ph.D. degrees from Osaka University, Osaka, Japan, in 2009 and 2012, respectively. From 2009
to 2012, she is an Intern Researcher at the Interaction
System Laboratory, Intelligent Robotics and Communication Laboratories, Advanced Telecommunications
Research Institute International (ATR), Kyoto, Japan.
She is currently a project researcher at Research Center
for Advanced Science and Technology, The Universityof Tokyo, Tokyo, Japan. Her research interests include
human-robot interaction and human interface.
Kazuhiko Shinozawa received the B.E. and M.E. inElectrical Engineering from Keio University in 1988 and1990, respectively, and Ph.D from Kyoto University in
2003. He has been conducting research for building arobot that can communicate with humans naturally
and smoothly. He is currently a cooperate researcher,Intelligent Robotics and Communication Laboratories,
Advanced Telecommunications Research Institute International (ATR), and a Professor of the Osaka Kyoiku University.
Reo Matsumura received a M.E. and Ph.D. degrees
in engineering from Osaka University, Osaka, Japan,
in 2008 and 2012. From 2008 to 2012, he was an Intern
Researcher at the Network Robot Laboratory in the Intelligent Robotics and Communication Laboratories at
the Advanced Telecommunications Research InstituteInternational (ATR), Kyoto, Japan. He is currently acooperate researcher, Intelligent Robotics and Communication Laboratories, Advanced Telecommunications
Research Institute International (ATR), and a project
researcher at Tokyo University. His research interests
include the development of communication robots andtheir control theory.
Hiroshi Ishiguro received a Ph.D. degree in engineering from Osaka University, Japan in 1991. He was a Research Associate (1992-1994) in the Graduate School ofEngineering Science at Osaka University and an Associate Professor (1998-2000) in the Department of Social
Informatics at Kyoto University. He was also a Visiting
Scholar (1998-1999) at the University of California, San
Diego, USA and an Associate Professor (2000-2001) anda Professor (2001-2002) in the Department of Computerand Communication Sciences at Wakayama University.
He then moved to the Department of Adaptive Machine

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12

Systems in the Graduate School of Engineering Scienceat Osaka University as a Professor (2002-2009). He iscurrently a Professor of the Department of Systems Innovation in the Graduate School of Engineering Science at Osaka University (2009-) and a Group Leader(2011-) of the Hiroshi Ishiguro Laboratory at the Advanced Telecommunications Research Institute International (ATR). His research interests include distributed
sensor systems, interactive robotics, and android science.
Norihiro Hagita received B.E., M.E., and Ph.D. degrees in electrical engineering from Keio University in
1976, 1978, and 1986. In 1978, he joined Nippon Telegraph and Telephone Public Corporation (now NTT),
where he developed handwritten character recognition.
He was a visiting researcher in the Department of Psychology, University of California, Berkeley from 198990. He is currently a Board Director of ATR and an
ATR Fellow, a director of the Social Media ResearchLaboratory Group and Intelligent Robotics and Communication laboratories. His research interests include
networked robotics, human-robot interaction, patternrecognition, and data-mining technology. He is a member of IEEE, the Information Processing Society of Japan,
and the Japanese Society for Artificial Intelligence, The
Institute of Electronics, Information and Communication Engineers (IEICE) and The Robotics Society ofJapan.

Masahiro Shiomi et al.