The final publication is available at
http://dl.acm.org/citation.cfm?id=1957816

Do Elderly People Prefer a Conversational Humanoid as a
Shopping Assistant Partner in Supermarkets?
Yamato Iwamura

Masahiro Shiomi

Takayuki Kanda

Hiroshi Ishiguro

Norihiro Hagita

ATR Intelligent Robotics and Communication Laboratory
2-2-2 Hikaridai, Keihanna Science City,
Kyoto, Japan{iwamura, m-shiomi, kanda, ishiguro, hagita}@atr.jp
ABSTRACT
Assistive robots can be perceived in two main ways: tools or
partners. In past research, assistive robots that offer physicalassistance for the elderly are often designed in the context of atool metaphor. This paper investigates the effect of two design
considerations for assistive robots in a partner metaphor:
conversation and robot-type. The former factor is concerned withwhether robots should converse with people even if theconversation is not germane for completing the task. The latter
factor is concerned with whether people prefer acommunication/function oriented design for assistive robots. To
test these design considerations, we selected a shopping assistancesituation where a robot carries a shopping basket for elderly
people, which is one typical scenario used for assistive robots. A
field experiment was conducted in a real supermarket in Japan
where 24 elderly participants shopped with robots. The
experimental results revealed that they prefer a conversational
humanoid as a shopping assistant partner.

Categories and Subject Descriptors
H.5.2 [Information Interfaces
Interfaces-Interaction styles

andPresentation]:

User

General Terms
Design, Experimentation, Human factors

Keywords
Communication robots, Robots for elderly, Field experiments

1. INTRODUCTION
In many developed countries, including Japan, Italy, and Germany,
the ratio of senior citizens continues to increase. Societies arefacing insufficient people to assist the elderly. Studies on assistive
robots include helping the elderly and the disabled. Robots canprovide such physical assistance as walking aids [6, 11], andcarrying baggage [17].
HRI has two views toward robots: tools and partners [2]. In thetool view, robots are expected to reliably provide services based
Conference’10, March 6-9, 2011, EPFL, Lausanne, Switzerland
Copyright 2010 ACM 1-58113-000-0/00/0010…$10.00.

Figure 1. Shopping with a robot

on human commands. Assistive robots that offer physicalassistance are often considered tools in many contexts. In a
partner view (peer [7] or companion [4]), robots provide
companionship in a trusted relationship [10] and expected toinitiate interaction and other services on its own. For example,
robots have acted as guides at an expo [25], and a museum [3]..
The key point of the deference between tools and partners arewhether they have a function to establish a friendly relationshipwith interacting people. For example, with a shopping robot,
functions such as a chatting ability or having a human-likeappearance are not directly related to the main task, but thesefutures can contribute to establish/enhance the friendly
relationships.
Are there any contexts where a partner metaphor fits assistive
robots better for physical assistance? In such a context, what
would people require for the robot to be equipped with? This
study aims to answer these questions. To address the first question,
we used a situation where robots were used like tools, but if ahuman companion provides the same service, he/she would bethought of a partner, not a tool. We selected a shopping assistancesituation where a robot helps an elderly person by carrying a
shopping basket (Figure 1). This is one typical scenario used forassistive robots in past studies [9, 17].
The second question is the design consideration for assistive
robots in a partner metaphor, which is our main research questionin this paper. Assuming a context where a physical assistive
robots is a better fit with a partner metaphor, what is the crucial
design consideration? Comparing previous studies of social robots
that provide companionship [12] and assistive robots that provide
physical assistance [6, 11], the major differences in the robot
design can be summarized as two factors: conversation and robottype.
The first design factor concerns whether robots should have
conversations with people. They might be expected to giveThe final publication is available at
http://dl.acm.org/citation.cfm?id=1957816

commands by spoken dialogue. But in a context whereconversation is superfluous to complete the task, is it still valuable
for social reasons? If we choose the partner metaphor, having a
conversation is natural. Human beings talk anywhere. In contrast,
if we choose a tool metaphor, having a conversation is nonsense.
The second design factor concerns the robot-type. For
applications of shopping assistant robots, a cart type is a simple
design that satisfies the functional requirement [1, 17]. Within atool metaphor, maybe this simple is best solution is true. In
contrast, if the robot is perceived as a partner, is a functionallysimple design the best? Conversational robots are often designed
as humanoid types [12, 18]. If an assistant robot having a
conversation is perceived as a partner, people might prefer ahumanoid type robot. This paper addresses the effect of these
design factors.

2. Related Works
2.1 Assistive robots for the elderly
A tool metaphor is often considered for assistive robots that offer
physical assistance for the elderly. For example, researchers havedeveloped robots as walking aids [6, 11]. Mutlu et al. developed aconveyance robot to assist hospital staffs, which is an indirect
assistance for senior citizens [21].
A partner metaphor is recently being considered for social robots
for health assistances by such conversations as reminders of
medicine schedules [20, 22]. Therapy purposes are another pointof view with a partner metaphor; for example, a seal robot wasused for robot therapy [23].
However, it remains unknown whether the elderly prefer a partner
or a tool metaphor in situations where robots are used like a tool
and a human partner could do the same service, e.g., shopping
assistance. Moreover, past researches did not focus on the effectof the appearance differences of robots. Therefore, we
experimentally tested our design considerations to determine
whether the elderly prefer a conversational humanoid in thiscontext.

2.2 Shopping assistant robots
Recent studies in social robots have started to develop shopping
assistant robots in realistic environments. Considering a realistic
shopping context, the researchers developed two functions:providing information and performing a physical assistance task.

However, these robots did not focus on conversational interaction
with people during such physical assistance tasks. Moreover,there was no discussion about the appearance effect of a robot thatphysically assists the elderly by having conversations.

3. Robot for shopping assistanceWe designed a scenario for a shopping assistance service that canbe implemented in the near future with a fully autonomous system.
We used a Wizard-of-Oz technique to provide the service.

3.1 Robot
We prepared two types of robots: a humanoid robot and a cart
robot. The major difference is their appearances; both haveequivalent capabilities to perform shopping assistant (details are
in Section 3.2): carrying a basket and engaging in conversations.
The humanoid robot is based on our communication-oriented
design, whose purpose facilitates communication by eliciting
anthropomorphic expectations toward the robot, which is basedon Robovie II (Figure 2(a)) [15]. Our humanoid robot has ahuman-like appearance with two arms (4*2 DOF), a head (3 DOF),
and is 120 cm tall. It has cameras and a speaker on its head. An
external speaker is attached to the back of its body. A microphone
is attached to the pole. It holds the basket by its arm and makes
such gestures when it isn’t holding a shopping basket.
The cart robot (Figure 2(b)), which is based on our functionoriented design, satisfies the required functionality to accomplish
the tasks without any additional elements. It follows a simple isbest principle. It is 85 cm tall and has a camera on a pan-tilt base.
It has a speaker, a microphone on its front, and an external
speaker on its back.
Both robots can carry a basket up to 5 kg. For the cart robot, thebasket location was based on ease of use; its top is at 100 cm. On
the other hand, the humanoid robot was designed to let its arm
hold the basket. Access is not as simple as the cart robot, because
the basket’s height is relatively low, and its top is at 46 cm.
Both robots share other features. Speech synthesis software,
XIMERA [16], was used for conversations. The microphone
location was different, but the operators could equally listen to theusers. Their mobile base is Pioneer3DX, which was used with thesettings of the maximum forward speed, 750 mm/sec, and the max
rotation speed, 100 degree/sec. A laser range finder and bumpers
were attached to the mobile base for safety.

Providing information such as route guidance is a common
assistance in realistic contexts, particularly in large, complex
shopping malls. For example, Gross et al. developed a shopping
guide robot named TOOMAS that performs dialogue-based
assistance by providing place and price information to shoppers[12]. Such robot has a function to greet people, which would be
partner-directed design, while most of other function is purely forassisting customer as tool-directed design; but they did notprovide any physical assistance.
Such a physical assistance task during shopping as conveying a
shopping basket is a particularly useful service for the elderly [9,17]. These robots physically assisted people in realistic
environments; they can also be used to assist the elderly. The
robots would be considered a tool, so they did not have
conversations.

(a) Robovie(b) Cart robot

Figure 2. Robots

The final publication is available at
http://dl.acm.org/citation.cfm?id=1957816

3.2 Shopping assistance service
Carrying a shopping basket is the primary task. In addition, ashort conversation was designed to fit the following scenario.

3.2.1 Shopping behavior in a supermarket
Figure 3 shows the supermarket where we conducted ourexperiment. Its area was around 2,350 m2. Typically, people enter
the building (Figure 3(a)), grab a shopping basket or a cart (b),and go to the supermarket. They often go to the main outer
corridor (through areas c-i), and the center corridor (j). Finally,
they reach the cash register (k), pack their items (l), and exit (a).
On average, it takes about 15 minutes for elderly people to enter
the supermarket and reach the cash register.

3.2.2 Service flow
Based on the above observation, we designed a carrying service
that starts when a robot meets a user at the building entrance(Figure 3(a)). When she arrives, the robot starts to follow her for
shopping assistance. If its conversation function is enabled, it
greets her (conversation function was only enabled in a withconversation condition (Section 4.2)).
At the supermarket entrance (Figure 3(b)), the robot waits for her
to set a shopping basket on it. After that, she freely walks aroundthe supermarket, approaching shelves, selecting items, and putting
them in the basket carried by the robot, who continues to follow
her while she walks and stops beside her when she stops. If its
conversation function is enabled, the robot talks to her asfollowing the pre-defined rules (details shown in 3.2.3). For safety
precautions, the robot was unable to go into a few areas of thesupermarket. When she enters them, the robot waits for her.
When she arrives at the cash register, she unloads the basket fromthe robot, pays for and packs her items, and gives the bag to therobot. The robot follows her until she arrives at the exit of thebuilding, which is about 50 m away from the cash register. If its
conversation function is enabled, it says “Goodbye.”

3.2.3 Conversation during shopping assistanceWe designed a simple chat function. There are three types oftriggers to make the robot speak. In the experiment, this function
was only enabled in a with-conversation condition (Section 4.2).
Location We separated the supermarket into 12 areas (Figure 3).
When the robot (and a user) moved into a new area in the
supermarket (b-i in Figure 3), it initiates a short random
conversation; e.g., where fish are sold (area f), it says, “Oh, here
are fishes. I wish I could swim like a fish. Once I asked to myboss whether I can try to swim, but he said no.”For each location, the robot has a few additional keywords for

l

g

h

i
k

： Area where
robot cannot enter

j
f

50
[m]

： Cash registerand baggage table： Area region

c

d

b
a

47 [m]e： Tall shelf(200 cm)： Short shelf(100 cm)

Figure 3. Supermarket map

responses. It accepts within 20 seconds after it says the above
sentences. For example, if the robot asks, “What are you shopping
for today?”, “vegetables” is listed in this keyword set. If a user
says “vegetables,” the robot replies “Cool, it sounds delicious."
When a user puts an item in the basket The robot briefly
comments when the user puts items in the basket, without really
identifying them. It provides positive phrases based on thelocation; e. g., in an area where vegetables are sold, it says, “Thatlooks delicious!”
Response Users are allowed to talk to the robot any time.
Through preliminary trials we retrieved fourteen keywords thatare often spoken to the robot and to which it is designed torespond. For each keyword, it makes a simple response. For
instance, when a user says, “come here,” it responds with “I'mcoming.” It responds to “Can you take this for me?” by saying,“No problem.” It makes vague responses to other utterance inputs,
saying “oh really” and “ah-ha.”Note that since we intend to identify the effect of conversation
regardless of the requirement for providing the service, none of
the above conversations are related to the functionality of thecarrying service, and we did not include voice-based commands.

3.3 Software and operator’s involvement
Our near future vision includes the operation of the fullyautonomously robot described in the previous section; but since
our goal is to conduct a field experiment to confirm whether ourdesign choice improves the acceptance of robots by the elderly,
we prepared a semi-autonomous system.

3.3.1 Software architecture
There are mainly three functions involved in this study:
localization, navigation, and dialogue management. Figure 4
shows the software architecture that illustrates the relationshipamong these functions. All are partially operated by an operator.
Localization The robot tracks its position by using the wheel
encoders. Although more accurate localization with laser range
finders is a common technique [19] and would probably work
well in this environment, we haven’t implemented it yet. Instead,the operator localizes the robot when its position greatly diverges.
Navigation The operator navigated the robot to follow the user
(Section 3.3.2 explains the details). Safety is secured by anautonomous system. The robot’s speed is autonomously limited
based on the distance between the robot and the nearest obstacle.
For instance, when there are no obstacles within 1.65 m in front of
the robot, it uses a top speed of 750 mm/sec. The speed isdecreased based on the distance to the nearest object. If the
distance is less than 0.45 m, the robot stops.
Dialogue management All utterance contents, which are preparedin advance, are associated with the three triggers shown in Section
3.2.3. When a human operator activates a trigger, the robot
Functions（semi-autonomous）

SensorLaser range
finderCameraActuator

Navigation

Motor
control

Dialogmanagement

Speaker

Localization

Microphone

Figure 4. Software architecture

The final publication is available at
http://dl.acm.org/citation.cfm?id=1957816

automatically chooses an appropriate utterance. For instance,
when the robot enters a new area, a pop-up message appears on
the interface window; it elicits the operator to order the robot to
say a location-based utterance, which the robot generates.

3.3.2 Tele-operation user interface
Figure 5 shows a user interface for tele-operation. The primary
information sources are the images from the camera (Figure 5(1)),
a map showing readings from a laser range finder (Figure 5(2)),
and sounds from the microphone. The operator uses all of thisinformation to localize the robot, to identify or search for the user
to navigate the robot for following. The robot autonomously stops
before hitting an object. But it does not generate a path that avoids
objects, so the operator decides the robot’s path.
Figure 5(3) is a panel that activates a trigger to make the robot
speak. The operator follows the rule in Section 3.2.3. When hehears a word, he chooses the heard word from the panel. Whenthe robot enters a new area, he informs this through this panel sothat the robot starts to speak. Similarly, when a user puts an itemin the basket, he activates a trigger for it.

4. FIELD EXPERIMENT
A field experiment was conducted in a real supermarket wheresenior citizens shopped with the robot.

4.1 Participants
24 senior citizens (12 men and 12 women, who averaged 67.2
years old, s. d. 4.97) participated in our experiment. They arenative speakers of Japanese who had never interacted with ourrobots before. They were paid 4,000 yen (47$) for each session,
for a total 16,000 yen (188$) for the four sessions; note that since
the shopping was real, they paid for their own groceries.

4.2 Conditions
We conducted a 2x2 within-participants factorial design with two
factors: conversation and robot-type.

4.3 Experiment procedure
The experiment was conducted in a shopping mall with asupermarket. During the experiment, participants moved aroundand select items freely. The robot followed the participants except
situations where they enter the narrow corridors (Figure 3) or the
robot lost the participants. In the situations, the robot stops andwaits until she/he comes back. Each experimental session wasscheduled in the daytime on weekdays to avoid extra crowds,
possibly caused by the robot.
Before the first session, the participants were given a brief
description of the purpose and procedure of the experiment. Since
this had a within-participant design, each participant participatedin four sessions, mostly scheduled a series of continuous four days.
The participants were notified in advance of the day and timeschedule of the experimentation, so they have enough time tothink what they will buy, as they would do in their everyday life.
At the beginning of each session, we told them whether the robot
can speak, confirmed the areas which the robot cannot enter, andexplained how to hang the shopping basket on the robot. Thenthey went shopping with a robot that provided the service
described in Section 3.2. They filled out questionnaires after eachsession; i.e. after using each kind of robot.
At the end of the fourth session, we did short interviews. Finally,
we did debriefing, in which we explained the existence of theoperator, only one participant mentioned that he had alreadyguessed the operator’s existence; it seems others were notsurprised. The order of these conditions was counterbalanced.
Staff remained around the robot for safety and recording videos.

4.4 Measurement
We measured intention to use because in studies of the acceptanceof new technologies [5] and social robots [18, 26], it is modeled
and indicates social acceptance.
1.

Conversation factor

Intention to use: three items were adapted from the study ofHeerink et al. [13], including, “I’m planning to use thisrobot for the next few days.” Cronbach’s α was 0.936.

With-conversation condition: robot’s conversation function(described in Section 3.2.3) was enabled.

We also measured perceived enjoyment and perceived ease of use,since these concepts are considered the source of intention to use.
The model of social acceptance [13] is illustrated in Figure 6.

Without-conversation condition: conversation function was
disabled. The robot did not say anything during the experiment.

2.

Robot-type factor

3.

Either a humanoid robot or cart robot was used (details aredescribed in Section 3.1).
In all conditions, the operator controlled the robot based on thesame rules described in Section 3.3, and we made sure that theoperator can do equivalent controls in both types of robots.
(1)
(3)

(2)

Perceived enjoyment: five items were adapted from [13],including, “I enjoyed shopping with this robot.” Cronbach’s
α was 0.916.
Perceived ease of use: six items were adapted from the studyof Davis [5], including, “I found it easy to get this robot to
do what I want it to do.” Cronbach’s α was 0.928.

All questionnaire items were evaluated on a 1-to-7 point scale.

4.5 Hypothesis and prediction
Our hypothesis is that a robot with a partner design is preferred
for social acceptance, even for physical assistance tasks. Thus wemade the following predictions:
Perceived
enjoyment

Perceivedease of use

Figure 5. Tele-operation user interface

Intention
to useFigure 6. Model of social acceptance defined by Heerink et al.

The final publication is available at
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Prediction 1: a with-conversation robot will be rated high in termsof intention to use.
Prediction 2: a humanoid robot will be rated higher than a cart
robot in terms of intention to use.

5. Results
5.1 Verification of predictions
Figure 7 shows the scale results for intention to use. For theintention to use scale, we conducted a two-way repeated-measure
ANOVA with two within-subject factors: conversation and robottype. A significant main effect was revealed in both theconversation factor (F(1,23)=12.74, p=.002, partial η2=.356) andthe robot-type factor (F(1,23)=4.36, p=.048, partial η2=.159). No
significance was found in the interaction within these factors(F(1,23)=1.83, p=.189, partial η2=.074). This means that bothwith-conversation and humanoid robot type significantly
increased the social acceptance, measured by the intention to use
scale. Our predictions are supported; we note that the result of thestatistics did not change even if we eliminated the data of theparticipant who guessed the operator’s existence during theexperiment.
This result indicates that both conversation and human-like robots
increased acceptance in a realistic shopping context.

5.2 Related scales to intention to use
We further analyzed perceived enjoyment (Figure 8(a)) andperceived ease of use (Figure 8(b)) and conducted a two-way
repeated-measure ANOVA with two within-subject factors:
conversation and robot-type. For the perceived enjoyment ratings,
a significant main effect was revealed in both the conversation
factor (F(1,23)=27.39, p<.001, partial η2=.544) and the robot-type
factor (F(1,23)=5.24, p=.032, partial η2=.186). No significancewas found in the interaction within these factors (F(1,23)=0.01,
p=.921, partial η2=.000). This means that both with-conversationand humanoid robot type significantly increased the perceived
enjoyment.
For the perceived ease of use ratings, a significant trend was only
revealed in the conversation factor (F(1,23)=3.63, p=.069, partialη2=.136), and no significance was found in the robot-type factor(F(1,23)=0.02, p=.889, partial η2=.001) or in the interaction
within these factors (F(1,23)=0.87, p= .362, partial η2=.036). The
effect of the conversation factor is not conclusive, but robot type
did not affect the perceived ease of use. These results arereasonable. We did not design conversation to assist the task. We
expect the possibility that humanoid robot type might decrease theperceived ease of use, since its basket location was relatively lesseasy-to-access than the one in the cart robot; but no such

Figure 8. Questionnaire result
significant difference was reported.
In summary, both conversation and humanoid robot typeimproved perceived enjoyment, but no significant difference wasobserved among the conditions in the perceived ease of use.
Considering the social acceptance model defined by Heerink [13],these results suggest that a significant improvement of perceived
enjoyment, which is a part of the source of intention to use, is one
major reason why intention to use significantly increased in theexperiment. As verification of the predictions, we note that theresult of the statistics did not change even if we eliminated thedata of the participant who guessed the operator’s existence
during the experiment.

5.3 Why did robots provide different
impressions?
As reported in Section 5.1, the humanoid robot and conversation
conditions resulted in better social acceptance. We specificallyasked why in a semi-structured interview conducted at the end of
the experiments. We analyzed the interview results to investigate
the reasons behind these different impressions.

5.3.1 What was the benefit of conversation function?
In the interviews, we asked the participants about theirperceptions of the robot’s conversation function to see whether
they preferred it. Two coders analyzed and classified thetranscribed results of interview from all 24 participants. The
Cohen's kappa coefficient from the two coders’ classifications was
0.931. They yielded the following classification.
(a) With conversation, they perceived that they are with ordoing things with someone: one participant(b) With its utterance or communicating with it, they perceived
positive feelings (enjoyment, better feeling, and lessloneliness): 13 participants(c) Combination of (a) and (b): eight participants(d) Its conversation function did not change their perceptions:two participantsThis result confirmed our findings in Section 5.1 and 5.2 thatconversation was connected to enjoyment and social acceptance,
but hardly with ease of use. Note that nine participants mentioned
the sense of “together with someone,” which matches the partner
metaphor.

5.3.2 Why did a humanoid robot result in bettersocial acceptance?
We also asked participants about their perceptions of the robot
type. As in the analysis in Section 5.3.1, two coders analyzed andclassified the results. The Cohen’s kappa coefficient from the two
Figure 7. Intention to use

The final publication is available at
http://dl.acm.org/citation.cfm?id=1957816

coders’ classifications was 0.915. They yielded the followingclassification.
(a) With a humanoid robot, they perceived that they are
together or are doing things with someone (or perceived ahuman-likeness in it): 16 participants(b) Using a cart robot was easy because it was simple to put
items in the basket (and humanoid robot was not easy to use):no participant, but combination with (a) is categorized as (c).
(c) Combination of (a) and (b): four participants(d) Using the humanoid robot was difficult because it
suggested a feeling of exploiting the robot: one participant

There were large individual differences whether participants
talked to the robots. Mr. T was actively involved in verbal
interaction. When the robot said, “Let’s get a basket,” he replied,“OK, let’s go.” When he put items into the basket (Figure 10(c)),
he said, “Now I need some potatoes.” After adding them, he said,“I need some fish next. Robovie replied, “That sounds good.” He
often talked to the robot when he put items in the basket andreplied seven out of 12 times to the robot. For instance, when the
robot entered area i, it initiated small talk:
Robovie: “We walked a lot today. I’m tired, but I really enjoyedit.”
Mr. T: “I’m tired, too.”(e) Robot type did not change their perceptions: twoparticipants

Robovie: "Well, but I could pass a lot of people, it was very
interesting."(e) Other answers: one participant

Finally, the robot said “goodbye” and Mr. T replied “goodbye”
and waved to the robot at the exit (Figure 10(d)).

In Section 5.2, we found improvement of enjoyment, butparticipant articulations were more about a sense of “together withsomeone” rather than simple curiosity or impressions of novelty.
Note also that although in category (b) they mentioned the ease ofusing the cart robot, it did not affect the averaged perceived easeof use or the overall social acceptance. This matches our conceptof a partner metaphor rather than a function metaphor.

Table 1 Shopping behavior in each condition
Robot-type

Cart robot

Conversation(with/without)

without

Humanoid robot
withwithoutwith

5.4 Is shopping with a robot different?

Enter main corridor

21/24

21/24

19/24

21/24

5.4.1 Overall shopping behaviors

Into narrow space

17/24

18/24

18/24

17/24

First, we quantitatively compared the overall shopping behaviors
among the conditions (Table 1) and measured the following three
behaviors: shopping route, shopping time, and number ofpurchases.

Shopping time[minutes] (S. D.)
Number ofpurchases (S. D.)

12.44 (3.94)

14.30 (4.99)

12.63 (5,59)

13.10 (4.57)

7.96 (2.91)

9 (2.27)

8 (2.18)

9.25 (2.60)

For shopping route, we checked whether they went to the main
corridor (outer corridor that goes through areas c to i in Figure 3)
or into the area where the robot couldn’t (Figure 3). Most people
did both, and there were no significant differences among theconditions. Their shopping time was also similar, approximately
15 minutes. These behaviors are roughly the same as what we
observed as shopping behaviors without robots (Section 3.2.1).

：Robot initiates
speaking to
the participant：Participant
initiates speaking
to the robot

Small differences were found in the number of purchases,
indicating that participants bought more items in the withconversation robot (F(1,23)=8.17, p=.009, partial η2=.262).
Overall, their shopping patterns resembled those without the robot
and did not change among the conditions, although a small
difference in the number of purchases was found (participantsbought only one more in average in the with-conversation robot).

：Participant
puts an item
into basket

Figure 9. Route and behaviors

5.4.2 Shopping with robot
Here, we describe an interaction scene in one of shopping with therobot that was retrieved from an experimental session with anelderly male participant (hereinafter, referred as Mr. T) and ahumanoid robot in the conversation condition. Figure 9 illustrates
their behaviors, showing where they talked and when he put itemsin the basket. His shopping lasted 13.5 minutes.
Figure 10 shows a couple of interaction scenes. When they met,
the robot greeted him (Figure 10(a)) saying, “Hello! I'm Robovie.
Let's get a basket.” After the greeting, Mr. T went to the basket
area. The robot followed him and said, “Please hang your basket
here,” and gestured with its arm (Figure 10(b)). Then he started
shopping; and the robot followed him and held the basket.

(a) Robot greets to participant(b) Participant sets basket(c) Participant adds item(d) Goodbye

Figure 10. Interaction scenes

The final publication is available at
http://dl.acm.org/citation.cfm?id=1957816

5.5 Summary of results
First, both conversation and robot-type factors significantly
changed intention to use, which indicates social acceptance. Bothfactors contributed to improvement of perceived enjoyment, butthey did not affect perceived ease of use. The results suggest that
one main reason why intention to use significantly increased is thesignificant improvement of perceived enjoyment in the experiment.
The interview results highlighted that both humanoid robot andconversation provided a sense of “togetherness,” which is one
main reason they reported perception differences. We believe that
this supports our partner metaphor for robots for assistive tasks.

6. Discussions
6.1 Design implications
One immediate design implication is that it is better for robots in a
shopping mall for carrying services to have conversational
humanoid characteristics. In reality, they involve costconsiderations because humanoid robots usually cost more. We
believe that robots in public environments would often be
expected to have multiple functions, e.g., providing route
direction as well as carrying a basket. For such purposes, ahumanoid robot design would be financially beneficial, since asingle robot can engage in multiple roles, and if available for acarrying service, it outperforms a simple cart robot.
Another important implication is that this study confirms theimportance of enjoyment in a shopping support context. Its
importance was previously found in social acceptance studies [13,14]. This result suggests that when we think about human-robot
interaction, not only its functional ease but also its enjoyment
should be explicitly considered. Elderly people, who often sufferfrom social isolation, would particularly benefit from ubiquitous
friendly conversation; since recently so many functionalities inour society are automated and lack a conversation.

6.2 Ethical consideration
Using robots often raises ethical concerns in various fields ofapplication. Typical concerns about robots for the elderly argue
that such use works against the elderly preference to receive
services from humans and forces them to receive services fromcold machines. Another concern is that the use of robots increases
the already serious isolation of senior citizens from other people.
These important issues must be considered.
On the other hand, in our society in the world, services are alreadybeing replaced by machines such as self-checkout. In somesupermarkets, the elderly don’t need to interact with people at all;they choose items from the shelf, put them in baskets, bring themto the self-payment register, and go home. For the application
discussed in the paper, human services no longer exist, so having
robots does not harm the elderly.

6.3 Can it be autonomous?
In this study, our robot was mostly teleoperated. Although thisstudy indicates that robots were perceived as useful and accepted,
it remains unclear whether such a robot can be autonomous in thenear future. If it fails to gain autonomy and one human operator
per one robot is always required, realistic deployment is difficult.
We discuss the possibility of automation for teleoperated
techniques. Maybe the easiest function to automate is detectinguser actions for putting items into the basket. This may be easily
automated with either weight sensor or image processing.
Localization, which is recently maturing in robotics, may also be
automated easily. SLAM studies have often demonstrated robust
localization performance in complex environments. Moreover, we
believe that with an adequate filtering technique, we can achieve
localization in such crowded environments as supermarkets.
Robust tracking of users might be one of the difficult problems.
Human tracking is often a focus of studies in robotics. Even
though many techniques have been developed, yet due to
occlusion in crowded environments a robot often loses a person.
Maybe a combination with other identification techniques, such as
RFID tags, should be considered. At least, a human operator
might help situations where a robot lost the tracking; with thisshared autonomy approach and with small efforts from humanoperators, robots could provide a carrying service.
Speech recognition is another difficulty in realistic environments,
where the current speech recognition system has a success rate of
only 21.3% [24]. This would be a critical problem if applications
required voice-based commands; however, for the scope of thisservice, some recognition errors could be tolerated. Thus, it might
be possible to automate the service with current techniques, or asemi-autonomous approach [8] could be used.

6.4 Limitations
The long-term goal of our study is to reveal the design
considerations for the physical assistance for the elderly; but since
this study was conducted for a carrying service at a supermarket in
Japan, we cannot generalize about this design implication only
from this study. We believe that it is useful to test the design
considerations based on the partner metaphor in other contexts.
Even though this experiment was held in a realistic situation withparticipants who engaged in actual shopping, it was conductedwithin a framework of academic study. Participants only used therobots a few times. Thus, perhaps the effect shown in our
experiment would be moderated if they were to get accustomed tothe shopping assistant robots; i.e., the novelty effect was lost. As
single study cannot reveal all; we were not able to extend thestudy to be realistic long-term study. This is one of the limitationsof this study, which will be tested in the future, perhaps with therealistic deployment of robots in society.

7. CONCLUSION
This paper addresses a design consideration for assistance robots
for the elderly. We tested whether the elderly accept a robot that isbased on a partner metaphor in a typical basket-carrying service
in a shopping context. We tested two factors: robot type (ahumanoid robot or a cart robot) and conversation. We conducteda field experiment in a realistic shopping context with 24 seniorcitizens. The results revealed that the participants prefer aconversational humanoid as a shopping assistant partner; bothconversation and human-like robot contributed to better
acceptance. Qualitative data from interview indicates thatconversation would provide positive feeling, and bothconversation and human-like robot contributed to provide thesense of “together with someone". These results are consistent
with the partner metaphor. We believe that our findings will leadto the development of assistive robots that act in realistic
environments for elderly people.

The final publication is available at
http://dl.acm.org/citation.cfm?id=1957816

8. ACKNOWEDGEMENTS
This research was supported by the Ministry of Internal Affairs
and Communications of Japan. We wish to thank the staffs at
APITA Seika-dai Co., Ltd. for their helpful participation. We also
wish to thank Satoru Satake, Satoshi Koizumi, Dylan F. Glas,
Tetsushi Ikeda and Masaya Shimoyama for their help.

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