Robots as an Interactive-Social Medium in
Storytelling to Multiple Children
Yumiko Tamura1,2 · Masahiro Shiomi1 · Mitsuhiko Kimoto1,2 · Takamasa Iio1,3 ·Katsunori Shimohara2 · Norihiro Hagita1

Received: date / Accepted: date

Abstract This paper investigates the effects of group interaction in a storytelling situation for children using two robots:a reader robot and a listener robot as a side-participant. We developed a storytelling system that consists of a reader robot, alistener robot, a display, a gaze model, a depth sensor, and a human operator who responds and provides easily understandable
answers to the children’s questions. We experimentally investigated the effects of using a listener robot and either one or twochildren during a storytelling situation on the children’s preferences and their speech activities. Our experimental results
showed that the children preferred storytelling with the listener robot. Although two children obviously produced more speechthan one child, the listener robot discouraged the children’s speech regardless of whether one or two were listening.
Keywords Storytelling, child-robot interaction, multiple robots, group interaction
1 Introduction

Interactive storytelling is a growing topic in education services for children because it plays an important role in language
development and improves the complexity of their language [2, 3]. Interactivity is an essential factor for attracting children
and supporting their education, regardless of the storytelling situations [4, 5]. Based on these contexts, agents (including
robots) are often used in interactive storytelling systems [6-12], and these studies reported the effectiveness of various systems
and how they encourage children’s language development.
For more engagement with children, we focused on the interaction styles of a storytelling system. Past studies mainly used
only one robot or an agent for storytelling, but several recent studies described how using a group of robots for informationproviding services attracts people by showing conversations between robots in information-providing contexts [13, 14]. Suchan information-providing style is defined as an interactive-social medium [15].

This paper is an extended version of previous work by Tamura et al. [1] and contains additional experiment results and moredetailed discussions.
1Interaction Science Labs., ATR, 2-2-2 Hikaridai Keihanna-Sci. City, Kyoto, Japan
2Doshisha University, 1-3 Tatara Miyakodani Kyotanabe-shi, Kyoto, Japan
3University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan / JST PRESTO
E-mail: m-shiomi@atr.jp

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Fig. 1 Storytelling for children with two robots

In interactive-social medium designs, one robot takes the speaker role, and the other plays the listener, and they talk together
in front of an audience. In other words, the additional robot behaves as a side-participant, which is a ratified participant
during conversations [16, 17]. The presence of such a listener robot enables a storytelling system to naturally provide
additional information about the story contents by showing a conversation that directly questions the speaker robot. Of course,
if the children directly question the robots, the speaker robot answers; the listener robot might also respond to showinteractivities.
In this study, we investigate the effects of an interactive-social medium (i.e., a reader robot and a listener robot) in astorytelling situation for children (Fig. 1) by comparing an interactive medium (i.e., only a reader robot) to investigate how
multiple robots shape interaction with children. We also investigated how the number of children (i.e., one or two) influenced
their behaviors during the storytelling because understanding the interaction with multiple children is important for childrobot interaction settings. Therefore, this study focuses on the group interaction of children and robots in storytelling situations.
We employ a semi-autonomous approach to accurately respond to children’s questions during storytelling to avoid the
difficulties of speech recognition [18] to develop a storytelling system with two robots. We also developed a gaze controller
model for natural and autonomous gaze control of the reader robot. In this study, we address the following research question:- How do the interaction style of a storytelling system and the number of children influence their activities?
We note that this paper is an extended version of previous work by Tamura et al. [1] and contains additional experimentresults and more detailed discussions.
2 Related Work
2.1 Robot as an information-providing medium

Robots are often used as a medium for information-providing services because their physical existence attracts people in suchactual environments as museums and shopping malls [19-22]. For natural conversational interaction with people based on
robot behaviors in such environments with ordinary people, several researchers have developed both verbal/non-verbal
interaction models, including gaze behaviors [23, 24], body gestures [25, 26] [27], spatial coordination [28, 29], approachingpeople [30], and distribution [31] [32].
Many such information-providing robots are used as interactive media, i.e., robots that directly interact with people to provide
information [33, 34]. On the other hand, some recent studies focused on a different interaction style to provide informationby showing the conversations of robots to people [35]. In this interaction style, the robots basically talk with each other toprovide information (Fig. 2, middle) and sometimes interact with people based on the contexts of their conversations, such asasking about specific topics (note that social is defined as multiple robots interacting with each other). Sakamoto et al.
concluded that using multiple interactive robots is better for conversational types of information providing than an interactive
medium (i.e., just one single robot), and the passive-social medium approach (i.e., only showing conversations between robots
to people without interactivity) is better for advertising contexts than using multiple robots [15]. Thus, these studies makeclear about the styles of information-providing for social robots.
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Robots as an Interactive-Social Medium in Storytelling to Multiple Children

3

In this study, we focused on storytelling applications for children. Since past studies reported that interactivity is essential for
effective storytelling (the details are described in the next section), we employed multiple robots for storytelling.
2.2 Interactive storytelling-robot systems for children

Recently robots are also being used as storytelling agents in education contexts [36] because their physical presence hasadvantages over virtual agents for spreading information [37, 38]. For example, Fridin developed a storytelling system with arobot that tells prerecorded stories to children and showed that it improved both cognitive and motor performances [9]. Kory
et al. developed a storytelling system using a robot with preschool children to support their language development [10]. Anotherperspective used a storytelling-robot system to understand children’s behaviors [13].
Interactive medium
with two children

Interactive-social medium
with two children

Interactive medium
with one child

Interactive-social medium
with one childPassive medium

Passive-social medium

Fig. 2 Types of information media with robots based on previous work [15] (bottom and middle) and interaction style of this study (topand middle)

These research works identified the effectiveness of a storytelling system with robots and scrutinized the possibilities of using
them in storytelling contexts. Unfortunately, they less focused on the interaction styles of storytelling. They employedinteraction-medium [10] and/or passive-social medium styles (or limited interactive-social style, e.g., just a tablet to selectstorytelling contents) [9, 14] without comparing the effects of an interactive-social medium with other interaction styles.
By considering interactive-social medium styles, asymmetrical roles (i.e., reader and listener robots) are essential to show
conversations between robots independently of the storytelling contents, including asking questions about the contents. If both
robots play the same roles, i.e., two reader robots like in past studies, it is illogical for either reader robot to ask the other reader
robot a question about the contents. A more natural context is where a listener robot asks the reader robot a question. One study
about child-robot interaction employed an interactive-social medium setting [39] that focused on a situation where children tella story to multiple robots, which is different from our study. Since previous studies identified the importance of interactivities
in storytelling [4, 5], we compared interactive medium (hereinafter single-robot) and interactive-social medium (hereinafter
two-robots) styles.
Several past works focused on a situation where a robot interacted with multiple people [9, 14, 40]. For example, Vázquez etal. developed a furniture-robot system that consisted of a mobile chiffonier robot and an animated lamp device and investigated
how multiple children interacted with it [40]. Short et al. investigated social interaction between a social robot and familygroups and their speech activities [41]. Similar to these works, we are interested in situations where multiple children participate
in storytelling because it will influence their activities (Fig 2, top and bottom). If multiple children are present, they will interact
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with each other, and their activities will be different from a situation where only one child is there. But past studies less focusedon group interaction between multiple storytelling robots and children. We believe that the observations of such interaction
will provide interesting knowledge for child-robot interaction research fields. In other words, exploring multi-party storytelling
between children and robots will contribute to our understanding of child-robot interaction in group settings as well as theeffectiveness of both single/multiple robots and children during storytelling situations.
3 System Overview

Figure 3 shows an overview of our developed system that consists of the following components: a human-tracking system, agaze model, a reader robot, a listener robot, and a human operator who controls the speech recognition and the behavior
selector of the robots. The reader robot shows each page of the book on a display during the storytelling. The details of eachcomponent are described as follows.
3.1 Human-tracking system

For children-position tracking, we used one depth sensor (Kinect V2) and a laptop PC to process the sensor data. The range for
person recognition was from 0.5 to 4.5 m, the field’s horizontal angle was 70°, and its perpendicular angle was 60°. This systemsends the estimated head positions of the children to the behavior selector using Kinect V2 libraries through a wired network
connection.

Fig. 3 An overview of the developed system.

3.2 Gaze model

Gaze behavior design in storytelling is an essential factor in attracting people’s interest in a story’s contents. Several gazemodels have already been proposed in multi-party conversations [23, 25, 42, 43], but they mainly focused on situations wherea speaker engages in storytelling with fewer than four persons. On the other hand, in our study, since the reader robot needs
to consider not only multiple children but also the listener robot and a display for the images, we extended the current
conversational gaze models [25].
The possible gaze targets were the listener robot, the display, an environment, and either of the children. Following a past
study [25], the reader robot changes its gaze target ratio at the beginning of each speech sentence.
In the two-robots style, i.e., with a listener robot (Fig. 4), the gaze targets are the display, the children, the listener robot, andthe environment. Following past research work, we set our reader robot to look at the display with a 70% ratio at the beginningof each sentence and at the listeners with a 73% ratio at the not-beginning of each sentence. Based on the previously definedgaze ratio probability [25], we set the ratios at the beginning of the targets to 70.0%, 22.8%, 2.52%, 2.52%, and 2.16%, andthe ratios at the not-beginning of the targets to 10.28%, 16.74%, 25.55%, 25.55%%, and 21.9% for the display, theenvironment, child 1, child 2 and the listener robot. Thus, at the beginning of each sentence, the reader robot generally lookedat the display; at the not-beginning of each sentence, it mainly looked at the children or the listener robot. When the gazetarget is the environment, the gaze angle was randomly determined from the front of the robot to the display. Without a listenerrobot, the gaze target ratio is added to the children’s ratios. When there is just one child, the gaze target ratio is also
summarized. We changed the gaze target every three seconds. The listener’s gaze is controlled with the same algorithm.

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5

The beginning of a sentence
No

Yes

70%

30%

22.8%

7.2%

Environment

2.52%

2.52%
1

2.16%

73%

27%

10.26%

2

25.55%

25.55%

1

2

21.9%

16.74%

Environment

Fig. 4 Proposed gaze model for reader robot with listener robot and two children

3.3 Robot

Figure 5 shows Sota, a desktop-sized interactive humanoid robot characterized by its human-like physical expressions. We
used two Sota robots as a reader and a listener. Sota has eight DOFs: three in its head, two for its shoulders, two for its elbows,and one in its base. It stands 28 cm tall and has a microphone, a camera, and a voice synthesis function. The LED on its mouthblinks depending on the sound level to indicate speaking. With the developed gaze model, the robot gazes are autonomously
controlled. Its shoulders move slowly as an idling behavior during the storytelling. We did not use pointing or other gesturesin this study.

Fig. 5 Sota

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3.4 Details of implemented behavior

An overview of the dialogue flow is summarized in Fig. 6. After the storytelling begins, the reader robot looks at the children
and the listener robot and starts to read. Both robots semi-autonomously interact with the children, except for the speech
recognition and behavior selections (details are described in the next section).
For our storytelling-robot system, we used two picture books: Etwas von den Wurzelkindern by Sibylle von Olfers (Nekko
umare no kobito tachi in Japanese) and Zucchini of Zuzu by Leo Rivas (Zuzu no Zukki-ni in Japanese). We chose these two
relatively unknown books to provide novel situations to attract more of the children’s attention in our experiment. We prepared
121 speech contents for the storytelling and chatting, and the average reading times by the robots are 5.5 for the Olfers bookand 3.5 minutes for the Rivas book.
Introduction

Display the first page
Storytelling(reading one sentence)
Yes

Did children
say anything?

Yes

Did children
speak within aminute?

No
Yes

Is there aquestion?
No

No

Questions bylistener robot

Answers byreader robot

Answers byreader robot

Yes
Go to next page

Go to next
page?

No
Yes

FarewellWere all of thesentences read?

No

Fig.6 Dialog flow in our developed system

3.5 Operator

For this study, we employed a human operator who assumed control of the speech recognition and behavior selector functions
with a tele-operated system (Wizard of Oz [44]) by following pre-determined rules because accurate speech recognition forchildren’s speech remains difficult [18]. We also needed to avoid the effects of errors in speech recognition systems toinvestigate interaction styles in storytelling. Also, the human operator waits for a minute before stimulating the listener robot
by using a timer to avoid the operator’s biases
When the children asked questions to the robots, and if no appropriate answers were identified, the operator used Sota’s textto-speech function to answer them as quickly as possible. The operator also determined the timing of the utterances during thestorytelling because we cannot predict when children will ask questions. For these reasons, we used a human operator as aspeech recognizer. The operator exists in a different room, so all commands and sensor information were remotely transferred.
4 Experiment
4.1 Hypotheses and predictions about children’s speech activities and the number of robots

We assume that the two-robots style will change the children’s speech activities during the storytelling. However, since thephenomena related to the number of robots remain basically unexplored and accurately estimating young children’s behaviors
is difficult, we made two competing hypotheses about their effects based on different theories and considerations.
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Robots as an Interactive-Social Medium in Storytelling to Multiple Children

7First, we assume that the two-robots style will stimulate children’s speech activities and encourage them to ask more questions
because showing conversations between robots enables the interacting people to understand their contents more easily [45].
Other studies suggest that using multiple robots attracts people’s interest in their conversational content and might stimulate
children’s curiosity [46] [47]. Moreover, questioning behaviors during robot conversations might fuel children’s curiosity inthe story’s contents. In fact, past work at an elementary school reported that when a child questioned a robot, others wereencouraged to ask questions themselves [48]. If the children feel that the listener robot asks the reader robot questions, theymight participate more actively during the storytelling.
However, one possible competing phenomenon might exist. Since the presence of a listener robot might change the role of thechildren from addressees to side-participants because the listener robot participates in the conversation by asking a question, apast study suggested that the roles in the conversation influence the speech activities of people [49]. Another study reported
that the number of speaking turns and times fluctuates greatly due to their roles [50], which is also directly related to people’s
speech activities. If the children feel that the reader robot is mainly talking to the listener robot, they might participate lessactively during the storytelling and focus more on the robots’ conversations. Based on these considerations, we made thefollowing competing predictions:
Prediction 1-a (stimulating speech activities): The two-robots style will encourage the speech activities of children: morestatements and/or questions, regardless of the number of children.
Prediction 1-b (discouraging speech activities): The two-robots style approach will discourage children’s speech activities,i.e., fewer statements and/or questions, regardless of the number of children.
4.2 Participants

In this experiment, 32 children (16 girls and 16 boys from three to five years old, average age 4.47, S.D 0.71) and their parents(30 mothers and two fathers) participated.
4.3 Conditions

Our experiment had a mixed participant design. The robot’s number factor is a within-participant design (single-robot andtwo-robots), and the children’s number factor is a between-participant design (alone and pair).
Single-robot condition: Just one robot conducted the storytelling with the children; this condition is a typical storytellingrobot system.
Two-robots condition: Two robots conducted the storytelling with the children.
Alone condition: Each child participated in the storytelling alone; 16 children participated in this condition.
Pair condition: A pair of children participated in the storytelling; 16 children who are friends (except one pair of twins)
participated in this condition. They attend the same kindergarten or live in the same neighborhood. The paired-children ages
and genders were the same. All participants are healthy children.
4.4 Environment

Figure 7 shows the experimental environment. The room is approximately 40 m2. We placed the reader and listener robots infront of the display and a Kinect V2 behind it. The children sat on the floor to look at the displayed book.

Fig. 7 The experimental environment in our study

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4.5 Procedure

Before the first session, the parents were given a brief description of our experiment’s purpose and procedure. We note thatall parents signed the consent form. The first ten minutes were used to adjust the children to their environment and the robots;
during this period, the reader and listener robots greeted each other and talked. Before the storytelling began, the children sat
in front of the display, and their parents moved away from their child.
The children sat through two sessions of different conditions about the number of robots. The order of the conditions (singlerobot or two-robots) and the books were counterbalanced. After each session, the parents filled out a questionnaire. After theexperiment, the parents and children separately answered questions about their preferences to reduce bias.
The experimental protocol was approved by our institutional review board.
4.6 Measurements

In this experiment, we objectively measured the number of utterances using recorded videos to verify our hypothesis. We also
subjectively investigated both the children and the parental impressions. We measured the preferences from the children andthe parents (binary choice among conditions, one item for each). We also measured their impressions about the naturalnessof the reader robot gaze from a third-person perspective. For this purpose, we prepared an item as “The degree of naturalness
about robot’s gaze behaviors” with 1-to-7 scale, 7 is most positive, one item.
5 Results

In this section, firstly, we describe observations of children’s behaviors to explain how they interact with the robots during theexperiment (Section 5.1). Next, we analyzed the behavioral results (Section 5.2) and the questionnaire results (Sections 5.3),
as well as additional analysis based on the observation (Section 5.4).
5.1 Observations

In both the single-robot and two-robots conditions, typically, the children intently listened to the story from the reader robot(Fig. 8). The focus of their attention, i.e., their gaze, changed based on the timing of the storytelling situations. For example,
when the reader robot started to talk, a child generally looked at it first and then at the display. In both conditions, children
sometimes focused on a specific part on a page, e.g., approaching the display for a closer look or pointing at a ladybug or
specific characters after the reader robot talked about them (Fig. 9).
From observations between the single- and two-robots conditions, the number of robots influenced not only the children’s
speech activities but also their physical activities during the storytelling. In the two-robots condition, the children quietlylistened to the robot’s conversations. Due to the increase in the number of robots, the children sometimes touched them, butsuch phenomena were rarely observed in the single-robot condition. For example, one girl stroked the heads of the robots or
hugged them during the story (Fig. 10). Based on these observations, we conducted more analysis of the children’s behavior(see Section 5.4 for more details). These behavior changes (i.e., children quietly listened to the storytelling and friendly
interaction such as touches to the robots) would make positive impressions and then influenced their preferences.
From observations between the alone and pair conditions, the number of children influenced their speech activities during
the storytelling, and we inferred that sitting next to a friend might relax them. For example, when the listener robot asked thereader robot a question, the children sometimes discussed it together (Fig. 11). Some children read the displayed contents
with the reader robot. One boy warned his friend, who was talking about something unrelated to the story or touching therobot (Fig. 12), to focus on the reader robot. On the other hand, sometimes children played together without focusing on thestorytelling. We also analyzed such positive/negative behaviors during the storytelling in Section 5.4.

Fig. 8 Child listening to story in single condition8

Robots as an Interactive-Social Medium in Storytelling to Multiple Children

9Fig. 9 Child pointing at a specific part of the display

Fig. 10 Girl hugging robot during storytellingFig. 11 Girls talking during storytellingFig. 12 Boy playing with a robot during storytelling9

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5.2 Verification of prediction: Speech activities based on the number of children and robots

During the experiment, the children made 336 utterances. First, we analyzed the utterances of the children during thestorytelling by separating them into three categories: statements (e.g., “I see”), questions (e.g., “what’s a zucchini?”), andconversations between children (e.g., talking about the contents by themselves) (Table 1). In the coding process, a coderclassified them into 336 contents, and another coder coded 10% of these data [51]. After that, we investigated the coding’s
validity by calculating the kappa coefficient [52], which was 0.726, indicating substantial agreement between coders.
Figure 13 shows that the average number of children’s utterances per minute depends on the number of children and robots.
We conducted a two-factor mixed ANOVA for each factor on robot’s number (within) and children’s number (between) andidentified significant main effects in the robot’s number factor (F(1, 30)=5.929, p=.021, partial η2=.165) and the children’s
number factor (F(1, 30)=32.462, p<.001, partial η2=.520). No significance was found in the interaction effect (F(1, 30)=1.209,
p=.280, partial η2=.039). This result indicates that children spoke less in the two-robots condition than in the single-robot
condition regardless of the number of children who were present. Thus, prediction 1-b was supported, i.e., the competing
prediction (1-a) was not supported. This result also indicates that the speech activities of two children are larger than the
speech activities of just one child.
Next, we explain the children’s utterances, which were mainly statements in the alone condition, and in the pair condition,
their utterances were mainly both statements and conversations regardless of the number of robots. For example, when one
child saw a picture during the storytelling, she said, “a snail!” Another example about a conversation concerned a child who
said, “a fish is eating zucchini.” The other child answered, “you’re right.” Some children asked such questions as “what’s azucchini?” The operator answered with the prepared answering behaviors, which covered all the questions from the children.
In the pair condition, the conversations and the questions to the robot were sometimes related; for example, when thecharacters in the story were making something with a large zucchini, the children asked the reader robot, “they’re going to
make a boat, right?”
Table 1Categorizationof children
utterances ineach condition

Single

Two

Alone

Pair

Alone

Pair

Statements

28

80

16

69

Questions

1

2

1

3

Conversations

-

70

-

66

*

3

* : p < .05

Average per minute

2.5
2

1.5
1

0.5
0

Single-robot
Alone

Two-robots
PairFig. 13 Average number of children utterances in each condition

5.3 Additional analysis of subjective impressions

In this section, we analyzed both the children and the parental preferences and the perceived naturalness of the gaze behavior
from the parent’s perspective. Fig. 14 shows the number of children who preferred the single-robot or two-robots conditions.
These results include both the alone and pair conditions. We eliminated three children who could not decide which condition
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11

they preferred and conducted a two-tailed binomial test that indicated that the proportion of the preferences for the two-robots
condition of 0.72 was higher than expected 0.50, p=.024.
*

25* : p < .05

Preferred number

20
15
10
5

0
Single-robot

Two-robots

Fig. 14 Number of robots preferred by children

18
16

Preferred number

14
12
10
8
6
4
2
0

Single-robot

Two-robots

Fig. 15 Number of robots preferred by parents in each condition
7

Gaze naturalness

6
5
4
3

2
1
Single-robot
Alone

Two-robots
PairFig. 16 The naturalness of reader robot’s gaze in each condition

Figure 15 shows the parental preferences for the single- or two-robots conditions. We conducted a two-tailed binomial test,
which indicated that the proportion of preferences for the two-robots condition of 0.41 was not as high as expected: 0.50,
p=.458. Based on these three analyses, the number of robots and children did not significantly affect the parental impressions
in our study.
We note that we did not share their preferences with their parents/children and their thought to avoid biases between children
and parents. However, some parents thought that children are more attracted to the storytelling in the two-robots condition
more than the single-robot condition, but they preferred the single-robot condition because the multiple robots might distract
children’s attention.
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Figure 16 shows the questionnaire results for the naturalness of the gaze of the reader robot. We conducted a two-factor mixed
ANOVA for each factor on the robot’s number (within) and the children’s number (between). No significance was found in
the robot’s number factor (F(1, 30)=1.114, p=.300, partial η2=.036), the children’s number factor (F(1, 30)=1.277, p<.267,
partial η2=.041), or the interaction effect (F(1, 30)=0.070, p=.794, partial η2=.070). Since the average values of this item atleast exceeded the middle (four) in all conditions, our developed gaze model seems to work satisfactorily regardless of thenumber of robots.
5.4 Children’s behaviors analysis
5.4.1 Behavior category of children

Based on our observations, we analyzed the children’s behaviors using recorded videos. First, we analyzed the types of
behavior during the interactions between the children. We categorized their behaviors during the storytelling into three types:
positive (e.g., trying to listen to the story), negative (e.g., inviting another child to play with toys instead of hearing the story),
and other. Note that we categorized the children’s behaviors that are unrelated to storytelling, such as a discussion about the
robot’s behaviors (e.g., gaze target) or adjusting their sitting positions as other categories.
In the coding process, a coder classified them into 218 behaviors, and then another coder coded these data. We calculated the
kappa coefficient, which was 0.780, indicating satisfactory agreement between the coders. Table 2 shows each behavior
category. We conducted a chi-square test, but it did not show any significant differences among the conditions.
Even though the numbers of behaviors are not significantly different, the behaviors types are slightly different between thealone/pair conditions. For instance, the positive behaviors in the alone condition are mainly reactions to the storytelling, suchas approaching the display to look at pictures, clapping their hands at the end of the storytelling, and so on. On the other hand,in the pair condition, a typical behavior is to talk about the storytelling between children. For instance, after the page ischanged, a child says, “that's a bug,” and when the other child asks where the child points to it. When the storytelling is almost
finished, a child gives his/her impression, and the other child agrees. In another situation, when a child asks the robot aquestion about the story, the other child also manifested interest in the question. Another example included suggesting that achild pay attention to the story; if a child was distracted during the storytelling (e.g., playing with a toy), the other child asked
him/her to put the toy down and focus on the story. Moreover, one child’s behaviors often led the other child to perform
similar behaviors; for example, if a child read aloud (i.e., repeating the story content) by following the reader robot, the otherchild simultaneously did the same behavior.
Concerning the negative behaviors, their behavior types are also different between the alone and pair conditions. For instance,
typical negative behaviors in the alone condition are playing with a toy alone and going to their parents during the storytelling.
Instead, in the pair condition, they did not approach their parents but played together because both children asked the otherchild to play.
5.4.2 Conversation topics of children

Next, we categorized the children’s conversation topics in the pair condition into seven types: robot (e.g., talking about robot’s
characteristic), story (e.g., talking about the contents by themselves), toy (e.g., talking about the around toys), advice (e.g.,asking to turn other child’s attention to the story), parents (e.g., talking about their parents’ behaviors), and other (Table 3).
In the coding process, a coder classified them into 58 conversations, and then another coder coded these data. We calculated
the kappa coefficient, which was 0.934, indicating satisfactory agreement between the coders. Note that the total number ofconversation types and utterances (Table 1) is different because one conversation includes several utterances, and the total
amount of utterances in the conversation category is not very different between the single and two conditions as shown in
Table 1. The chi-square test did not show any significant differences, but the presence of the listener robot increased thevariety of conversation topics.
Table 2 Number of each behavior category

Condition

Robot

Positive

Negative

Other

Single

25

19

22

Two

12

20

17

Single

17

11

18

Two

21

19

17

Alone

Pair

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Table 3 Number of each conversation category in pair condition

Robot Story

Toy

AdviseParentChat

Single

1

7

2

2

0

7

Two

7

11

5

6

2

8

5.4.3 Touch behaviors between robots and children

Finally, we analyzed the children’s touch interactions during the storytelling because we noticed that the children often
touched the robot more in the two-robot condition than in the single-robot condition. We counted the number of touch
interactions between the children and the robots, and Fig. 17 shows the average number of touches in each condition. Only
one coder counted them because identifying them is quite simple.
We conducted a two-factor mixed ANOVA for each factor of robot’s number (within) and children’s number (between) andidentified a significant main effect in the robot’s number factor (F(1, 30)=7.354, p=.011, partial η2=.747). No significancewas found in the children’s number factor (F(1, 30)=1.053, p=.313, partial η2=.169) or the interaction effect (F(1, 30)=0.681,
p=.416, partial η2=.681). These results indicate that the number of robots increased the touch interactions from the children,
regardless of the number of children.

Average number of touch interactions*
3.5* : p < .05

3
2.5
2

1.5
1

0.5
0
Single-robot

Two-robots

Alone

Pair

Fig. 17 Number of observed touches between children and robots in each condition

6 DISCUSSION

The following are the two main contributions of our paper: the investigation of the effects of an interactive-social medium (i.e.,
two-robots style) in a storytelling setting and the development of a gaze control model for a storytelling robot by considering
multiple targets. Our experiment results showed how children’s activities changed due to the number of robots, and ourdeveloped gaze control system works satisfactorily regardless of the number of targets. In this section, we discuss the children’s
behaviors based on observations, implications, and other topics.
6.1 Implications of experiment results

In this study, we prepared competing predictions toward children’s activities during storytelling, and the experiment results
supported the prediction about discouraging speech activities. This effect happened regardless of the number of children,
although multiple children more talked to each other.
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One of the possible reasons why children decreased their speech activities is that the multiple robots changed children’s roles
via storytelling, as discussed in the above section. Children basically observed and listened to the storytelling or conversations
between the robots as side-participants; therefore, their speech activities would be decreased.
On the other hand, the number of robots significantly increased the children's preferences and their touch behaviors toward therobots. Physical interaction did not disturb storytelling in our setting then they could touch the robots during storytelling. Suchphysical interaction might have positive effects on their preferences. Still, it is difficult to estimate why children touched therobots more in the two-robots style. Their roles in the storytelling (i.e., side-participant) may be related to their behavior changes.
For example, becoming a side-participant role may decrease their cognitive load to understand the story, and then it may
increase their additional playful behaviors. Such behavior changes in children during interaction would be one interesting futurework.
6.2 Design implications for application perspective

This study provides several design implications for the application perspective. First, the two-robots style was preferredsignificantly more than the single-robot style. In other words, the children preferred storytelling with two robots more thanwith just one. Our results and those of past work indicate that the two-robots style attracts people’s interest in a robot’s contents,
regardless of the ages of the participants.
Second, our experimental results showed that the two-robots style discouraged the children’s speech activities more during thestorytelling regardless of the number of children than in the single-robot style. From a storytelling perspective, this phenomenon
has both positive and negative effects. Decreasing the speech activities of children might focus more on their interest in thestorytelling itself. On the other hand, this effect might discourage active participation in storytelling, relegating the children topassive participants.
Related to discouraging speech activities, their touch interaction activities showed an interesting phenomenon. One possible
explanation for fewer speech activities is because of the perceived tension caused by the number of robots, but the children
showed more touch interactions with two robots than just one; if they felt the tension, they might touch the robots less.
Investigation about the relationship between touch and speech activities would be one interesting future work, which is notcovered in this study.
Based on these considerations, the appropriate interaction style for storytelling should reflect a particular purpose. If a user
wants children to participate actively, the single-robot style might be more appropriate. If a user wants children to listen quietly,
perhaps during a kindergarten event, the two-robots style might be better. But in such situations, the robots need to respond totouch interactions from the children during the storytelling to avoid disturbing the storytelling due to continuous touches.
6.3 Toward (semi-) autonomous storytelling

In this study, we employed a semi-autonomous storytelling-robot system due to the difficulties of smooth and naturalconversations in real environments with young children: speech recognition and unpredictable questions. For the former, paststudies reported that the success rate of current speech recognition systems is only 21.3% [53] in real environments, andchildren’s speech recognition also remains difficult [18]. For the latter, children often ask a wide range of questions to test thecapabilities of robots [48]. Even if their questions are not related to the storytelling contents, ignoring or dismissing them isgenerally unacceptable and will discourage their interest. To improve the performance of speech recognition for the wide rangeof questions asked by children, more data collection and training are needed.
In addition, storytelling robots need the capability of detecting touches from children. In the first stage, we did not assume thatchildren would touch the robots during the storytelling, but our experimental results showed that they often touched them when
the number of robots was two. Using a touch sensor to cover the robot’s body might be useful, and past studies already proposed
various approaches to detect touches from people using whole-body touch sensors [54, 55]. Another approach is acceleration
sensors that detect the body movements of the robots. This approach was also used to understand the physical interaction
between robots and people [56] and robots and their environments [57].
Another perspective is to involve a human operator actively to realize semi-autonomous storytelling with multiple places. In
this study, a human-operator only controlled one storytelling robot system. However, past studies reported that the semiautonomous system could decrease the workload of an operator [58], and such an approach can enable them to control multiple
robots simultaneously [53, 59]. Therefore, if this storytelling system can call the operator when only the system needs help, theoperator’s workload can be decreased too. If one operator can control multiple storytelling systems simultaneously, the costeffectiveness and robustness toward child-robot interaction compared to a fully autonomous system would have advantages.

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15

6.4 Limitations

This study has several limitations since it was conducted with our robot system and its specific settings, e.g., only using one
kind of robot (Sota) to investigate the effects of storytelling with our gaze control models. Since we only investigated theparticipation of one or two children, adding more children might increase speech activities. Moreover, even though measuring
subjective impressions from young children is difficult, more objective analysis is needed.
In our study, we used a listener robot that asks the reader robot questions, and so the total amount of information provided
during the storytelling was different between the conditions. In addition, we only compared the effects of different roles using
robots without focusing on people and/or virtual agents.
Another limitation is the roles of the robots. In this study, we assigned a reader and a listener for robots, but we did notinvestigate the effects of multiple readers and listeners. Adding more robots might decrease children’s speech activities moreby following our results, but it is not investigated yet. In addition, we did not investigate the effects of a role-playing style, e.g.,
robots behave as characters of the story. In reality, such a storytelling style is available to human readers. Investigating such astyle would provide rich knowledge in the context of developing a storytelling robot system, although it is out of scope in thisstudy.
However, we believe that our setting is satisfactory to offer knowledge to readers who are interested in child-robot interaction
and storytelling in a two-robots style.
7 Conclusion

Understanding the effects of interaction styles during storytelling for children is essential for child-robot interaction. We
investigated the effects of an interactive-social medium design of storytelling for children. We developed a semi-autonomousstorytelling-robot system and experimentally investigated the effects of the numbers of robots on children’s activities during
storytelling.
Our experiment results showed that the number of children significantly affected the speech activities, i.e., their speech
activities significantly decreased with two robots compared to just one. On the other hand, using two robots encouraged touchbased interaction activities from the children. Taken together, these experimental results suggest that the appropriate
interaction style for storytelling should reflect different storytelling purposes and situations.
Acknowledgments: This research work was supported in part by the Tateishi Science and Technology Foundation, JST
CREST Grant Number JPMJCR18A1, Japan, JST, PRESTO Grant Number JPMJPR1851, Japan, and JSPS KAKENHI Grant
Numbers JP18H03311 and JP19J01290.
Conflict of interest: The authors declare that they have no conflict of interest.
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Yumiko Tamura received a B. Eng. degree from Doshisha University, Kyoto, Japan in 2016. She is currently an M. Eng.
student at Doshisha University and an internship student at the Intelligent Robotics and Communication Laboratories (IRC),
Advanced Telecommunications Research Institute International (ATR). Her research interests include child-robot interaction
and multiple social robots.

Masahiro Shiomi received M. Eng. and Ph.D degrees from Osaka University in 2004 and 2007. From 2004 to 2007, he wasan intern researcher at the Intelligent Robotics and Communication Laboratories. He is currently a group leader in the AgentInteraction Design Department at Interaction Science Laboratories, Advanced Telecommunications Research InstituteInternational. His research interests include human-robot interaction, social touch, robotics for childcare, and multiple social
robots.

Mitsuhiko Kimoto received M. Eng. and Ph.D. degrees from Doshisha University, Kyoto, Japan, in 2016 and 2019,respectively. He is currently a JSPS Research Fellow (PD) at Keio University, Kanagawa, Japan. His research interests include
human-robot interaction and multiple social robots.

Takamasa Iio received the Ph.D degree from Doshisha University, Kyoto, Japan, in 2012. He is an assistant professor at
University or Tsukuba, Ibaraki, Japan. His research interests include social robotics, group conversation between humans andmultiple robots and social behaviors of robots.

Katsunori Shimohara received B.E. and M.E. degrees in computer science and communication engineering and a Doctor ofEngineering degree from Kyushu University, Fukuoka, in 1976, 1978, and 2000, respectively. From 2001 to 2006, he was the
director of the Network Informatics Laboratories and the Human Information Science Laboratories, Advanced
Telecommunications Research Institute (ATR) International, Kyoto. He is currently a professor at the Department ofInformation Systems Design, Faculty of Science and Engineering, and the Graduate School of Science and Engineering,
Doshisha University, Kyoto. His research interests include human communication mechanisms, evolutionary systems,
human-system interactions, and socio-informatics.

Norihiro Hagita received B.S., M.S., and Ph.D degrees in electrical engineering from Keio University, Japan in 1976, 1978,
and 1986. From 1978 to 2001, he was with the Nippon Telegraph and Telephone Corporation (NTT). He joined the Advanced
Telecommunications Research Institute International (ATR) in 2001 and established the ATR Media Information Science
Laboratories and the ATR Intelligent Robotics and Communication Laboratories in 2002. He is currently the chair andprofessor in the Art Science Department at Osaka University of Arts, and the director and ATR fellow of Norihiro Hagita
Laboratories at Advanced Telecommunications Research Institute International (ATR). His current research interests include
communication robots, networked robot systems, interaction media, and pattern recognition. He is a co-chair for the IEEE
Technical Committee on Networked Robots.

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