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IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 6, NO. 4, OCTOBER 2021
Using an Android Robot to Improve Social
Connectedness by Sharing Recent Experiences of
Group Members in Human-Robot Conversations
Changzeng Fu , Chaoran Liu, Carlos Toshinori Ishi , Yuichiro Yoshikawa , Takamasa Iio, and Hiroshi Ishiguro
Abstract—Social connectedness is vital for developing group
cohesion and strengthening belongingness. However, with the
accelerating pace of modern life, people have fewer opportunities
to participate in group-building activities. Furthermore, owing to
the teleworking and quarantine requirements necessitated by the
Covid-19 pandemic, the social connectedness of group members
may become weak. To address this issue, in this study, we used
an android robot to conduct daily conversations, and as an intermediary to increase intra-group connectedness. Specifically, we
constructed an android robot system for collecting and sharing
recent member-related experiences. The system has a chatbot
function based on BERT and a memory function with a neuralnetwork-based dialog action analysis model. We conducted a 3-day
human-robot conversation experiment to verify the effectiveness of
the proposed system. The results of a questionnaire-based evaluation and empirical analysis demonstrate that the proposed system
can increase the familiarity and closeness of group members. This
suggests that the proposed method is useful for enhancing social
connectedness. Moreover, it can improve the closeness of the userrobot relation, as well as the performance of robots in conducting
conversations with people.
Index Terms—Long-term interaction, social HRI.
Manuscript received February 24, 2021; accepted June 15, 2021. Date of
publication July 7, 2021; date of current version July 20, 2021. This work was
supported in part by JSPS, under Grants JP20H05576, 19H05691, 20H00101,
and in part by JST, Moonshot R&D under Grant JPMJMS2011. This letter
was recommended for publication by Associate Editor S. Rossi and Editor G.
Venture upon evaluation of the reviewers’ comments. (Corresponding author:
Changzeng Fu.)
Changzeng Fu is with the Graduate School of Engineering Science, Osaka
University, Osaka 560-8531, Japan, and also with the Interactive Robot Research Team, Guardian Robot Project, RIKEN Kyoto 619-0288, Japan (e-mail:
changzeng.fu@irl.sys.es.osaka-u.ac.jp).
Yuichiro Yoshikawa is with the Graduate School of Engineering Science,
Osaka University, Osaka 560-8531, Japan (e-mail: yoshikawa@irl.sys.es.osakau.ac.jp).
Chaoran Liu is with the Advanced Telecommunications Research Institute
International Kyoto 619-0288, Japan (e-mail: chaoran.liu@atr.jp).
Carlos Toshinori Ishi is with the Interactive Robot Research Team, Guardian
Robot Project, RIKEN Kyoto 619-0288, Japan, and also with the Advanced
Telecommunications Research Institute International Kyoto 619-0288, Japan
(e-mail: carlos@atr.jp).
Takamasa Iio is with the Faculty of Culture and Information Science, Doshisha
University, Kyoto 610-0394, Japan (e-mail: tiio@mail.doshisha.ac.jp).
Hiroshi Ishiguro is with the Graduate School of Engineering Science, Osaka
University, Osaka 560-8531, Japan, and also with the Advanced Telecommunications Research Institute International Kyoto 619-0288, Japan (e-mail:
ishiguro@sys.es.osaka-u.ac.jp).
This letter has supplementary downloadable material available at https://doi.
org/10.1109/LRA.2021.3094779, provided by the authors.
Digital Object Identifier 10.1109/LRA.2021.3094779
I. INTRODUCTION
OCIAL connectedness among group members is an important factor that affects group performance and determines
the strength of group cohesion and the sense of belongingness [1], [2]. Good social connectedness is the basis for strong
cohesion, which can bring many benefits to the group, such
as goal commitment, efficient teamwork, smooth cooperation
between members, and mutual understanding [3]–[5].
According to social psychology studies, collective events and
perceived similarity are key factors for fostering connectedness [6]–[8]. However, with the accelerating pace of modern
life, collective events for team building are barely available, as
people have fewer opportunities to participate in group building
activities. Furthermore, owing to the teleworking and quarantine
requirements necessitated by the Covid-19 pandemic, the social
connectedness of group members may become weak. Messaging
applications may alleviate this and enhance communication
among group members to some extent. However, such user-touser messaging occasionally add a certain social burden (e.g.,
replying may be awkward because of the low familiarity of the
members during a chat) so that some people would be afraid of
such direct message-based interaction [9], [10]. In this case, a
robot serving as an intermediary to aid users in establishing a
connection would be an effective approach [11].
Berger et al. [12] claimed that similarity-based attraction
could produce robust interpersonal relationships. Moreover,
according to McPherson et al. [13], the homophily principle
reinforces people’s social relationships, which could be
interpreted as interpersonal similarities that breed connections.
This trend could be evidently seen on social media. People
frequently assemble because of shared similarities, even if they
are geographically separated and have never met before [14],
[15]. Once the interpersonal similarities are discovered,
the development of a connected strength relationship [16]
as well as the group cohesiveness [17] are fostered. In
Addition, Kaptein et al. [18] extended the similarity-attraction
effect with computer-mediated communication. Their study
suggests that computer-mediated interaction has the potential
to improve social connectedness between people when there
is an opportunity to identify similarities. Therefore, with the
unavailability of collective events, we consider improving
intra-group connectedness by discovering similarity among
members (perceived similarity). To this end, we propose using an
S
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FU et al.: USING AN ANDROID ROBOT TO IMPROVE SOCIAL CONNECTEDNESS BY SHARING RECENT EXPERIENCES
android robot that conducts daily conversations, provides users
with a sense of talking with people, and serves as an intermediary
to increase social connectedness among group members.
Accordingly, we should first develop interactive strategies
for engaging people in human-robot conversations. Several
studies have confirmed that presenting memory and learning
abilities during an interaction can increase the willingness to
talk with robots [19]–[22]. Moreover, the results reported by Fu
et al. [23] suggest that remarking memorized user preferences in
human-robot conversations can improve user engagement and
has the potential to maintain long-term interaction. Following
the same strategy, Uchida et al. [24] further verified that sharing
preferences in group talk can tighten intra-group relationships.
However, topics limited to preferences may not be sufficient
to improve social connectedness, whereas discussions about
offering more information on daily activities [25] as well as
experience sharing [26] would be particularly helpful.
Following the aforementioned directions, in this study, we
developed a system using a neural-network-based model that
enables a robot to automatically recognize and memorize recent
member-related experiences during human-robot conversations,
and share them in future interactions. Additionally, we conducted an experiment involving conversation continuation trials
on three different days to verify the effectiveness of the proposed
system. The contributions of this study are as follows:
r By integrating neural-network-based models [27], [28], we
develop a system whereby robots can automatically collect
memories from the real world and share them in humanrobot conversations.
r We propose an interactive strategy whereby an android
robot (ERICA) can improve its performance and enhance
intra-group connectedness.
II. RELATED WORD
The principal component of this study is robot memory.
Herein, we review related work and summarize the main differences with the present study.
Memory elicitation has been demonstrated to be effective in
human-robot conversations in several respects, such as commonground establishment, intelligence [24], [30]–[32], personalization [33], [34], and long-term interaction enhancement [32],
[34]. Kanda et al. [30], [31] designed a shop guide robot that
remembers frequent consumers and gives different greetings
based on the number of times it has met the customer. It was
demonstrated that the proposed system could build rapport between users and the robot, promote the willingness of people to
interact with the robot, and improve the evaluation of perceived
intelligence. Leite et al. [32] proposed a method that allows
robots to use information from prior conversations with the
same child so that a sense of relationship can be fostered over
time. It was verified that this method could improve perceived
intelligence, increase friendliness, and facilitate engagement.
Lee et al. [33] developed Snackbot, which can remember and
share the snack choice and service usage of a user. It was demonstrated that this interaction strategy could reinforce the rapport,
cooperation, and engagement of a person with the robot. Ahmad
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et al. [34] proposed a system for designing robot behavior based
on past user performance on mathematics tests and emotion.
Their experiment results demonstrated that a robot equipped
with their proposed system could enhance the engagement and
the learning performance of children.
Although some studies have been concerned with the effectiveness of using robot memory in human-robot interaction, most
of them have focused on enhancing the relationship between the
robot and the current user by considering the memory of the
robot, which can be understood as one-to-one rapport building.
Uchida et al. [24] conducted a study on sharing individual
preferences (perceived similarity) in a one-to-many group talk
to enhance intra-group relationships; however, group talk is
a collective event that allows people to communicate (e.g.,
verbally and non-verbally). Such behaviors could also be factors
that affect these relationships.
Compared with previous memory-related studies, the present
study is aimed at developing intra-group connections by sharing
recent member experiences stored as robot memories. These
shared memories are supposed to be stimuli that enhance the
perceived similarity of group members. It should be noted that
no collective events are involved in this study; that is, we attempt
to enhance one-to-many connectedness through one-to-one conversations between a user and the robot.
III. METHOD
A. System
The core function of the proposed system is to enable robots
to automatically detect people-related information (feelings and
experiences) from human-robot conversations and store them as
memories. To construct the people-related information detector, we first prepared a spoken Japanese dataset with two-way
Japanese conversations (I-JAS [35]), from which we selected
interview dialogs of native Japanese speakers as samples for
model training. Subsequently, we annotated sentences (utterance
level) with 13 labels by referring the method proposed by Stolke
et al. [36] with some modifications. Table I lists the labels, definitions, and the corresponding samples. In this study, we regard
“subjective information,” “objective information,” and “plan”
as people-related information (experience) that can be shared
in human-robot conversations. However, the annotated dataset
was not sufficiently large to train a model with a large number
of parameters, and our task requires a text classification model
that does not have an excessively large number of parameters
and is effective on a small-sample-size dataset. Accordingly,
we adopted the SeMemNN model proposed by Fu et al. [27],
which has been proved to have high training speed and fair
performance when trained on a small-sample-size dataset. After
SeMemNN was trained on the annotated dataset, the testing
accuracy of detecting people-related information was 82.57%,
which is reasonable for practical implementation. In addition to
detecting people-related information, other categories, such as
questions and agreement, are also used in the system.
Moreover, for a dialog system, a chatbot (response generation) function is essential for providing replies based on user
utterances. We trained the BERT [28] model on a Japanese
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IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 6, NO. 4, OCTOBER 2021
TABLE I
TRAINING SAMPLES FOR THE PEOPLE-RELATED INFORMATION DETECTOR (JPS-DAPRINFO [29])
study), then it is considered that there is little people-related
information shared in the current conversation. To promote an
informative dialog, the system selects a question (e.g., do you
exercise these days?) from the pre-defined question database so
that the exchange of information may be facilitated.
B. Verification Strategy
Fig. 1. Flowchart of the proposed dialogue system. The bold blocks indicate
the core functions of the system. The dashed lines indicate that a function refers
to the connected database.
conversation corpus [37] and integrated a question-answering
function based on predefined stereotype information into an
android robot.
We now present the working flow of the proposed system. As
shown in Fig. 1, the robot starts with a greeting and waits for
the user to reply. Subsequently, by using the SeMeNN model,
the system analyzes the utterance from the user and determines
whether it is people-related information. In that case, the system
stores this utterance in the memory database using the who and
what components (e.g., Chason / went to Tokyo last week.). Then,
the system calculates the similarity between the utterance and
the memories stored in the database based on the semantic cosine
distance and word overlapping rate by using the NLTK [38] and
Word2Vec [39] toolkits, matching the most similar information.
Subsequently, the system generates the robot response based on
the matched memory by using a rule randomly selected from
the template database (e.g., I talked to [who] a couple days ago,
he/she told me [what]). Then, the system determines whether
the conversation should be ended based on the round number
(max round was set to 25 in this study). If the utterance of the
user is not people-related, the system calls the chatbot function
for responding. Specifically, if the system continually uses the
chatbot function for N consecutive times (N equals 3 in this
Fig. 2 shows the verification strategy. The main purpose of
this study is to verify whether the proposed system can enhance social connectedness among group members. We invited
subjects who knew each other to participate in conversation
continuation trials conducted on three different days. In addition,
the performance of the robot in conducting conversations should
be evaluated.
In order that the robot present learning as well as memory
capabilities, the system collects people-related information in
each conversation, and shares this information in future conversations. It should be noted that at the beginning of the
experiment, the robot had no memories, and thus only the chatbot
function was used on the first day. On the second and third days,
the robot shared the collected people-related information. With
such a design, each day’s trails could regard historical trails as a
baseline for comparing the members’ connectedness as well as
the performance of the robot.
Considering the possibility of the mere-exposure effect [40],
we designed a control condition (the right part of Fig. 2) to verify
whether or not the results were due to the proposed strategy.
The configuration under the control condition was essentially
identical to that under the experimental condition, but only the
chatbot function was employed in all trials. Thus, there was no
people-related information shared in conversations.
IV. EXPERIMENT
A. Subject
We invited 36 subjects from six different groups to participate
in the experiment. The profiles for each group and the assignment
of subjects to the experimental and control groups are presented
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Fig. 2. Experiment design. The left part is the design of the experimental condition, in which only the chatbot function is used on the first day, and people-related
information is shared on the next two days. The right part is the design for the control condition, in which only the chatbot function is employed for three days, and
no people-related information is shared.
TABLE II
PARTICIPANTS FOR EACH CONDITION
Uchida et al. [24] on a 7-level Likert scale to evaluate factors
3 and 4 (see the following text box). It should be noted that
factors 1 and 2 were evaluated before the experiment and after
each conversation, whereas the other factors were evaluated after
each conversation.
- Familiarity of the group members:
r I am getting familiar with group members’ recent activities.
r I would be able to know better the group members’
mental states/experiences.
r I would be able to think of something fun that we could
in Table II, where M and F denote “male” and “female,” respectively, whereas EXP. and CON. denote the experimental and
control conditions, respectively. We carefully assigned groups
to either the experimental or the control condition according
occupation, gender, and age. Thus, 18 subjects (M = 9 and
F = 9) with an average age of 31.11 (SD = 8.03) participated
in the experimental condition, and the remaining 18 subjects
(M = 9 F = 9) with an average age of 28.78 (SD = 6.21)
participated in the control condition.
We adopted a within-subject strategy for each condition with
respect to the temporal dimension, and a between-subject strategy with respect to the conditional dimension. That is, the members of a group only engaged in the experimental or the control
condition for three separate days. Moreover, the experiment was
group-dependent, implying that the sharing of memories was
limited to information relevant to the members of a group.
B. Measurement and Hypothesis
To comprehensively evaluate the effectiveness of the proposed
system, we first designed a questionnaire-based evaluation for
the factors, that is, 1) closeness with the group, 2) closeness
with the robot (ERICA), 3) familiarity of the group members, 4)
evaluation on the system/robot. Inspired by Uchida et al. [24],
we adopted the inclusion of other in the self scale (IOS) [41]
to evaluate factors 1 and 2. We used the questions designed by
all do together with group members.
- Evaluation on system/robot:.
r The robot knows something about my group’s members.
r The robot has the ability to have a conversation with
people.
r The robot has the ability to understand people’s utterances.
r I would like to have this robot in the place where my
group’s members and I are always around.
r Other members of my group would like to interact with
this robot.
In addition to the questionnaire-based evaluation, we also
conducted some empirical observations, that is, 1) the number
of times the subjects feel good or bad about the robot’s reply, 2)
the total words of the utterances of the subjects, 3) the number of
times the subjects share people-related information (experience)
in each conversation. To collect the feelings of the subjects,
we developed a touch-panel interface (see Fig. 3) with two
(good)’ and ‘
(bad)’) to let subjects provide
buttons (‘
feedback regarding their feelings during the conversation. The
subjects were asked to evaluate the content of the reply of the
robot by pressing the “good” button when the participant felt
the reply was good, or the “bad” if the reply was considered
bad. The buttons could be pressed an unlimited number of times
during the conversation. Regarding the total number of words
of the utterance of the subject and the number of times that
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Fig. 3.
IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 6, NO. 4, OCTOBER 2021
Experimental setting. The proposed system was applied to ERICA.
people-related information was obtained in each conversation,
we set a function in the system to calculate the result.
The hypotheses are the following: 1) Sharing similar recent
feelings or experiences is supposed to enhance perceived similarity, thus enhancing the social connectedness and familiarity of
group members. 2) Compared with a plain chatbot, the proposed
system can improve the conversational ability of the robot and
the robot-user relationship.
C. Apparatus and Procedure
Fig. 3 shows the experimental setting. The proposed system
was applied to an android robot (ERICA), and a touch panel was
placed in front of the subjects so that the replies of the robot could
be evaluated (“good” or “bad”), as explained in Measurement
and Hypothesis section.
Herein, we describe the procedure. Before the experiment,
we obtained approval from the ethics committee regarding the
involvement of human subjects. The purpose and steps of the
experiment were introduced to the subjects, who were asked
to fill out a consent form. Before the first-day conversation,
we asked the subjects to answer a pre-hoc questionnaire, related only to closeness within the group and closeness to the
robot. Subsequently, the human-robot conversation was started,
the subjects casually talked to the robot, and they provided
evaluation by pushing the corresponding button. After finishing
the conversation, the subjects filled out another questionnaire
related to all the questionnaire-based measurements. Moreover,
to ensure that the good/bad measurement was properly conducted, we reminded of the subjects of this operation before
each conversation. After the experiment, we confirmed whether
the subjects pressed the corresponding button at the appropriate
time. The procedure for the conversations conducted on the
second and third days was the same as that on the first day,
except that there was no pre-hoc questionnaire.
the increase by performing a person-wise subtraction from the
scores collected by post-hoc questionnaires each day. Repeated
two-way ANOVA of two independent variables (condition and
temporal dimension) was conducted on the calculated growth
of closeness, the scores collected by post-hoc questionnaire
regarding other factors, as well as the empirical observations.
Fig. 4 shows the results for each factor. Tables III and IV present
the detailed statistical results.
Regarding the growth of closeness with the group and the
familiarity of group members, significant differences were found
in the main and the interaction effect, as well as the multiple
comparisons in the experiment condition. It can be concluded
that sharing recent experiences better enhances intra-group connectedness and increases familiarity among group members with
time than the system equipped with only the plain chatbot. A
similar observation can be made regarding the growth of the
closeness with the robot, indicating that, compared with the plain
chatbot, the proposed system, by sharing recent experiences,
considerably enhances human-robot interaction. Regarding the
evaluation on the system/robot, the significant differences in
the main and the interaction effect suggest that the proposed
system performs better in human-robot conversation than the
plain chatbot system.
Regarding the empirical observation, there are also significant
differences. The experimental condition allowed subjects to
experience more good feelings about the robot’s reply than
the control condition. However, in the multiple comparisons,
no significant differences were found between day2 and day3
under the experimental condition. Regarding the number of bad
feelings, a significant difference was found only in the temporal
dimension. Regarding the number of words and the amount of
sharing people-related information, significant differences were
found in both the main effect and the interaction effect. These
results imply that, as compared to the plain chatbot system,
sharing recent experiences can better increase the willingness
of subjects to talk with the robot and induce them to share their
experiences.
VI. DISCUSSION
Herein, we use the experimental results to support our hypotheses, and we compare our findings with those of previous
memory-related studies on human-robot interaction. In addition,
we discuss the limitations of the proposed method and indicate
future research directions.
A. Verification of Hypotheses
V. RESULTS
First, we used one-way ANOVA to compare the scores collected from the pre-hoc questionnaire (regarding closeness with
the group and closeness with the robot) between the experimental and the control group. The statistical results demonstrated that there was no significant difference in the closeness
with the group (F (1, 34) = 0.256, p = 0.616) and the closeness
with the robot (F (1, 34) = 0.739, p = 0.396) between the experimental and control groups. Then, we regarded the scores
collected before the experiment as initial scores, and calculated
Hypothesis 1: The increase in the closeness with the group
and the familiarity of group members indicate that, without a
collective event, memorizing and sharing similar recent experiences with group members can enhance social connectedness.
Moreover, we collected feedback from participants after the
experiment. Regarding the experimental condition, we received
feedback from nine subjects, and all of them were positive
regarding the effect of the proposed system. They felt that the
group members became familiar and talked with each other a little bit more because of the people-related information shared by
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Fig. 4. Statistical results for each factor by using repeated two-way ANOVA (∗ p < .05, ∗∗ < .01). The horizontal curly brackets with ‘*’ indicate a significant
difference in the temporal dimension (main effect), the vertical curly brackets with ‘*’ indicate a significant difference between conditions (main effect). The lines
connecting sub-groups indicate significant difference in multiple comparisons (interaction effect).
TABLE III
STATISTICAL RESULTS USING TWO-WAY ANOVA
TABLE IV
STATISTICAL RESULTS OF MULTIPLE COMPARISONS
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the robot (e.g., ”I got to know other members’ recent feelings or
activities through the conversation”, ”Sharing members’ information is a catalyst that triggers dialogue between members”,
and ”The robot had positive effect on our group relationship”).
Regarding the control condition, we received feedback from
seven subjects. They felt the robot had no particular effect (e.g.,
”So far, no effect”, ”I don’t think there is any particular effect
right now”, and ”I didn’t feel any effect from the conversation”).
This also demonstrates the effectiveness of the proposed system
in improving social connectedness.
Hypothesis 2: The second hypothesis can be verified by the
increase in the closeness to the robot and the evaluation of the
system/robot. Although the chatbot can increase robot-human
closeness in the three-day consecutive interactions, the proposed
system improved this score more significantly. The empirical
observations also support the second hypothesis to some extent.
On the days (day2 and day3 in the experimental condition)
of sharing people-related information, users reported a larger
number of good feelings. Additionally, the number of words
and the amount of sharing people-related information indicate
that sharing people-related information with group members
can promote the volume of user utterances and the willingness
to share more information. These results suggest that sharing
people-related information could stimulate human-robot conversations.
In addition to using people-related information to engage
users in human-robot conversations, by memorizing shared
people-related information, the system can find common ground
with the user and provide appropriate information in different
groups even for the same topic. This suggests that the proposed
system has good generalizability for wide-range use.
IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 6, NO. 4, OCTOBER 2021
proposed method is quite effective, some limitations must be
acknowledged.
Subject diversity: The subjects who participated in the experiment were adults, and more experiments are required to verify
whether the proposed method is useful for children. To this end,
more factors should be considered in the experimental setting
for the children group (e.g., whether the evaluation method in
the form of questionnaires can be valid for children, or whether
the appearance of the robot scares children). Therefore, in future
work, adjustments should be made to the experimental setting
that would allow the participation of children subjects.
Use of people-related information: In this study, we let
the robot share people-related information under a maximum
setting (all memory). More effective methods of using information obtained from the current user and other members
should be developed to enhance intra-group connectedness.
Moreover, the optimal order of the stages of rapport building
should be studied (first establishing user-robot connections and
then user-member connections, or first showing member-robot
connections and then enhancing user-robot and user-member
connections). These aspects should be explored in detail in a
separate study. In addition, we should consider the effect of the
ratio of the current-user memory to member-related memory on
member connectedness and bot evaluation. Furthermore, in such
an interaction strategy, private information should be carefully
handled. More research should be conducted on this issue, and
identifiers should be developed so that robots may distinguish
how private the information is and choose what to share in
human-robot interaction. Such privacy-preserving behavior may
enhance the trustworthiness of robots.
VII. CONCLUSION
B. Findings Comparison
We proposed a memory-sharing-based strategy to improve
human-robot interaction. The results are consistent with the
findings of existing related studies [23], [30]–[32], [34], that
is, using memory enhances user engagement and promotes
user-robot closeness. However, this is a preliminary study that
discusses whether a robot, in the absence of collective events, can
improve social connectedness by only taking advantage of the
perceived similarity of group members through the elicitation of
people-related information.
The principle of the rapport-building mechanism of the proposed method is to enhance one-to-many (user-members) connections through only one-to-one human-robot conversations.
By contrast, existing memory-related studies focus on one-toone (user-robot) rapport building through one-to-one humanrobot conversations. That is, this study presents the possibility of
using robots as a medium to establish multi-person connections
by sharing people-related information.
We proposed a dialog system that enables an android robot
to memorize and share people-related information in humanrobot conversations. Moreover, we conducted a 3-day humanrobot conversation experiment to verify the effectiveness of the
proposed system. Compared to the plain chatbot system, the
results demonstrated that the proposed system could increase
the familiarity and closeness of group members, as well as
the performance of the robot in conducting conversations with
people. Moreover, sharing people-related information could induce people to engage in conversations with robots and improve
intra-group connectedness. In future work, we will validate our
approach in a wider age range and investigate the most beneficial
memory-sharing strategies for enhancing social connectedness.
Furthermore, we will explore the effect of sharing people-related
information on the trustworthiness of robots. Finally, we will
investigate methods for enabling robots to discern whether a
piece of people-related information can be shared or not.
REFERENCES
C. Limitations and Future Work
In this study, we developed a system and designed an interaction strategy based on memory sharing under a maximum
setting (all memory) to improve intra-group connectedness.
Even though it was experimentally demonstrated that the
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