The final publication is available at
http://dl.acm.org/citation.cfm?id=1734469

A Larger Audience, Please!
- Encouraging people to listen to a guide robot Masahiro Shiomi

Takayuki Kanda

Hiroshi Ishiguro

Norihiro Hagita

ATR Intelligent Robotics and Communication Laboratory
2-2-2 Hikaridai, Keihanna Science City,
Kyoto, Japan{m-shiomi, kanda, ishiguro, hagita}@atr.jp
Abstract—Tour guidance is a common task of social robots. Sucha robot must be able to encourage the participation of people whoare not directly interacting with it. We are particularly interested
in encouraging people to overhear its interaction with others,
since it has often been observed that even people who hesitate tointeract with a robot are willing to observe its activity. To
encourage such participation as bystanders, we developed a robotthat walks backwards based on observations of human tour
guides. Our developed system uses a robust human tracking
system that enables a robot to guide people by walking
forward/backward and allows us to scrutinize people’s behavior
after the experiment. We conducted a field experiment tocompare the ratios of overhearing in “walking forward” and“walking backward.” The experimental results revealed that infact people do more often overhear the robot’s conversation in
the “walking backward” condition.
Keywords-components: Eliciting spontaneous participation,
tour-guide robot, social human-robot interaction

I.

INTRODUCTION

Tour guidance is one of the most promising robot
applications. Many studies in robotics have focused on thenavigation of robots with people (e.g., [1-6]), and field trials
have often been conducted with guide robots [7-13]. For
instance, Siegwart et al. developed navigation robots that
operated at an Expo [7], and Thrun et al. developed a tourguide robot for the Smithsonian Institution [8]. The usefulnessof robots in information-providing tasks [13] and advertisement
applications [11] has been demonstrated as well. Many robots
have capabilities that make them well-suited for tour guidance
applications, including mobility, the ability to speak to people,
and a novelty that attracts people’s attention.
However, these studies did not focus on how to elicit
spontaneous participation, which is one fundamental
interaction in tour-guide robots. In fact, we found that people
often hesitate to participate [12, 14]. Previous studies
considered a way for a robot to encourage people’s interaction[14-16], but they only considered a robot standing in a specificplace. A few studies have investigated robots that proactively
approach people [8, 13, 17], but these studies only considered
how to invite people to be the addressee of the interaction, i.e.,
the person who speaks, listens, and acts with the robot. In
contrast, we observed that people standing within somedistance often overheard the interaction, even though theyThis research was supported by the Ministry of Internal Affairs andCommunications of Japan.

hesitated to participate (Fig. 1). If the robot’s role is to provide
information, such participation as audience is also useful.
When a tour is led by a human professional, such tour
guides are commonly seen walking backward to look at people
to more naturally provide explanations. Stimulated by thishuman behavior, we considered walking backward as one way
to elicit spontaneous participation while our robot is leading
people. Here, we see the advantages of robots over humans.
Humans need to frequently look in the direction they arewalking to observe their environment and avoid collisions. In
contrast, a robot doesn’t have to “look” in the direction of its
movements. The term “look” might not be accurate here. A
robot observes its environment with other devices;
consequently, a robot can use its eye and head devices toexpress its “looking” direction purely for natural interaction
with people.
In this paper, we study how human-robot interactionchanges when the robot moves forward or backward. Note that
our study and analysis were conducted with a robust andaccurate tracking system of walking people [18]. It enables us
to control the robot’s behavior in a way we defined, e.g.,
forward/backward accompanying behavior. Moreover, it
records pedestrian behavior around the robots while theyexpressed their accompanying behavior. The analysis produced
a working hypothesis that the opportunity of looking at the
robot’s face, which is offered more by moving-backward,
would increase the chance to let pedestrians overhear a robot’s
guidance utterances.

Figure 1. Overhearing an interaction

The final publication is available at
http://dl.acm.org/citation.cfm?id=1734469

II.

PARTICIPATION ROLE

A. Theoretical background
There is literature in the human sciences about modelingparticipation roles for conversation. Goffman classified people
as “ratified participants” and “unratified participants” during
conversations in the participation framework model [19]. Clark
classified participants as speaker, addressee, side-participant,bystander, and eavesdropper [20]. Speakers, addressees, andside-participants are defined as ratified participants inconversation. All other listeners are defined as over-hearers
who are unratified participants. Over-hearers include two types:
bystanders and eavesdroppers. In contrast to over-hearers,
bystanders are openly present but do not participate in theconversation.
Milgram et al. studied how people mutually influence eachother by setting up a situation where a confederate was gazing
at a nearby building on a street. They found that theconfederate’s behavior elicited other people to gaze at the samebuilding. Moreover, the number of pedestrians who looked up
at the building increased based on an increase in the number ofconfederates [21]. Similar findings have been reported in aseries of studies in group dynamics known as casual crowds[22, 23]. Similar to these findings in the literature, we
considered that a situation in which a robot interacted with aperson would stimulate the casual participation of others.
B. Participation role in tour guidance with a robot
Borrowing terms from [20], we modeled the participationrole in guiding interaction (Fig. 2). In guiding situations, since
the robot does most of the speaking, its role is the speaker.
There is usually a person in front of the robot listening to andfollowing it called the addressee. In addition, a group of people
is often involved. While the leading person behaves as theaddressee, other ratified participants are accompanied in agroup around the speaker and addressee and are called sideparticipants. All other people encouraged by the
speaker/addressee/side-participants to join the interaction (i.e.,
listen to or follow the robot) are called bystanders.
The role of bystanders is a comfortable vantage point forpedestrians. People might hesitate if they were asked to beaddressees, since this role is responsible for responding to therobot; in contrast, bystanders are not responsible for interactingwith the robot. We believe this is a reasonable strategy withwhich a robot can elicit spontaneous participation frompedestrians as bystanders.

III.

SYSTEM CONFIGURATION

Figure 3 shows an overview of our system that enables arobot to guide people by walking forward/backward. The robot
guides an addressee based on the position information of itself
and the people in the environment. Moreover, the robot
controls its speed, rotation, and behavior based on a positional
relationship between itself and the addressee. The system iscompletely autonomous after starting an interaction with anaddressee.
We used environmental sensors for position estimation [24].
Many research works have achieved robust position estimation
functions using only the robots’ sensors [7-9, 25]; however, for
our analysis we also need the position information of thesurrounding people, even those distant from the robot.
Therefore, we used environmental sensors for position
estimation.
A. Hardware
1) Robovie“Robovie” is an interactive humanoid robot characterized
by its human-like physical expressions and its various sensors.
We used humanoid robots because a human-like upper body isuseful for naturally holding the attention of humans [26].
Robovie is 120 cm high, 40 cm wide, and can synthesize andproduce a voice through a speaker.
Its lower mobile base is a Pioneer 3-DX (ActiveMedia). Its
maximum moving speed was set to 2.5 km/h (700 mm/second),
based on the average speed of people walking in a mall, thecapability of the mobile base, and safety.
For obstacle detection, we attached a Hokuyo URG-04LX
Laser Range Finder (LRF) to the lower mobile base to enable
the robot to detect low obstacles that the environmental sensors
cannot detect.

Figure 3. System overview

Figure 4. Shopping arcade and laser range finders
Figure 2. Participation roles in guiding

The final publication is available at
http://dl.acm.org/citation.cfm?id=1734469

adjusts its position to be diagonally in front of the addressee at
about 50 cm (we defined Gtf as this temporary target position
for walking forward). It makes this adjustment by controlling
its speed and rotation.
For walking backward, the robot guides the addressee bystanding in front of the addressee. For this purpose, the robot
adjusts its position to about 50 cm in front of the addressee bycontrolling its speed and rotation (we defined Gtb as this
temporary target position for walking backward).
Figure 5.

Estimated positions of people

2) Environmental sensors for position estimation
We used SICK LMS-200 laser range finders (LRFs)
mounted around the trial area’s perimeter at a height of 85 cm
to track people’s positions (Fig. 4). We used a technique
derived from the algorithm presented in a previous work [18],in which individual particle filters were used to track thelocation of each person in the scan area based on the combined
torso-level scan data from all of the laser range finders. This
tracking technique provides highly stable and reliable trajectorydata. For natural walking speeds, the tracking accuracy of oursensor configuration was measured to be +/- 6 cm.
Figure 5 shows the estimated positions by scanning datafrom six laser range finders. The sensors were also used for
robot localization; the system estimates the robot’s position as
accurately as the peoples’ positions.
B. Behavior Controller
We implemented the following robot task: guiding an
addressee to a shop that will open soon in a shopping mall. The
task consists of four behaviors: idling, greeting, guiding, andadvertising. In the idling behavior, the robot simply waits for a
person to address. In the greeting behavior, the robot turns to
the target, greets him/her, and shakes hands. In the guiding
behavior, the robot asks the target to follow and moves in frontof the shop. The robot chats with the person while guiding; thecontents are randomly selected from ten kinds, such as “A few
days ago, I got caught in a downpour. My heart was racing
because I was soaked!” In the advertising behavior, the robot
advertises the shop and says “goodbye” to the target after
arriving at the shop’s front.
We developed two functions to realize the four behaviors: agesture controller and a navigation algorithm. The details ofeach function are described as follows.
1) Gesture controller
This function controls the robot’s face and arms. In greeting
and advertising behaviors, the robot uses gestures for greeting,shaking hands, and pointing to the shop. In the guiding
behavior, the robot faces the addressee while guiding, and in
this condition the maximum degree of the face direction is 90/90 from the direction in the front of the body. The robot swings
its arms while guiding.
2) Navigation algorithm
For walking forward, the robot guides the addressee bystanding to the addressee’s side. For this purpose, the robot

The robot reduces its speed by half when the distance
between the robot and the addressee exceeds 1.5 m. The robot
does not stop until it arrives at the shop or encounters an
obstacle within 50 cm in the direction of its movement. Thisbehavior is based on preliminary experimental results showingthat addressees stop and wait for the robot when it moves too
slowly. Furthermore, the robot looks at the addressee while
guiding in both conditions.
To adjust its speed, the robot estimates the distances
between itself and each pedestrian using position informationfrom the LRFs. The speed and the moving and face directionsof the robot are calculated by the following equations:⎧MaxSpeed L ( DistA < 1500 or DistAG > DistG )⎪Speed = ⎨MaxSpeed − ( DistA − 1500) × α L (1500 ≤ DistA ≤ 2500)⎪MaxSpeed / 2L ( DistA > 2500)⎩⎧θ RTP L (−45 ≤ θ RG ≤ 45 and abs (θ RTP ) < 90)MovingDirection = ⎨⎩θ RG L (otherwise)⎧90 L (θ RA > 90)⎪,
FaceDirection = ⎨θ RA L (−90 ≤ θ RA ≤ 90)⎪− 90 L (θ < −90)
RA⎩

where DistA is the distance between the robot and theaddressee, DistG is the distance between the robot and the shop,
Maxspeed is 700 mm/seconds (as described above), θRTP is theangle between the robot and a temporary target position (Gtf or
Gtb), θRG is the angle between the robot and the goal, and θRAis the angle between the direction of the front of the robot’s
body and the addressees. The major differences between thetwo cases are the positioning of the robot and the direction ofits body while walking.
IV.

FIELD EXPERIMENT AT A SHOPPING MALL

We investigated how the robot elicits spontaneousparticipation while guiding through a field experiment at a
shopping mall by comparing the effectiveness of walking
forward and walking backward.
A. Method
1) Environment and settings
The field trial was conducted inside a mall located between
a train station and an amusement park. The robot was placed ina corridor visited hourly by an average of more than 400 people(during summer vacation in Japan). We conducted experiments
during four days, all weekdays.

The final publication is available at
http://dl.acm.org/citation.cfm?id=1734469

Figure 6 shows the environment, where we installed six
LRFs and four cameras. The experiment space was as equal as
the area covered by laser range finders. The robot wasautonomous in the experiments except for the start signal sentby an operator in the beginning of first greeting. The robot
waits at the start position for a person to greet; when a visitor
talks to the robot, the operator simply start the greeting
behavior of the robot. After greeting, the robot guides the
person to the front of a shop that will open soon and advertises
it fully-autonomously. The guiding distance was approximately
10 m. For safety, one staff member remained around the robot
during the trials.
The visitors were mainly families, couples, and sightseers,
all of whom could freely interact with the robot. The number ofvisitors fluctuated between day and night, so we divided theexperiment into time slots that covered both daytime (lunch,
shopping, or the amusement park) and night (dinner, shopping,or the station) to avoid results skewed by different numbers ofparticipants.
The trial was divided into three stages based on the robot’s
behavior: greeting, guiding, and advertising. Each trial started
when a pedestrian talked to the robot in its idling behavior.
After finishing these three stages, each trial was completed.
The mean duration of a guiding stage was 20 seconds. The
mean duration of an advertising stage was also 20 seconds.
We obtained permission to record video and sensor datafrom the mall authorities. The experimental protocol was
reviewed and approved by our institutional review board.
2) Conditions
We adopted two conditions for the experiment and definedeach factor as follows:

and chats so they naturally follow it to a place where they canlisten to it. Therefore, basically to face the addressees, thedegree of the robot’s face direction was 90 or -90.
On the other hand, almost all addressees stood in front of
the robot while being guided in the backward condition. Fig. 8
shows guiding scenes in which people participated in theguidance and followed the robot. The robot starts to guide
addressees after greeting and shaking hands; the addressees
followed the robot and completely listened to its
advertisements (Fig. 8-a). While walking, pedestrians passing
through the corridor pay attention to the robot (Figs. 8-b, c).
Some stopped around it, indicating that they want to become
bystanders and completely listened to the robot’s advertisement(Fig. 8-d). The robot guided people 18 times in the backward
condition.
Moreover, only in the backward condition, we observed
scenes of participant interaction caused by the robot walking
backward. Such interactions were not observed in the forward
condition. For example, a few addressees beckoned to friends
or relatives as they followed the robot. Other addressees left theguidance on the way when pedestrians approached the robot.
They seemed to feel embarrassed by following the robot on
their own in a face-to-face manner. Bystanders did not leave
when an addressee or side-participants faced them during
guiding. Bystanders often left when the robot stopped at theshop or finished an explanation about it.
2) Numbers of each role of participants
We defined four categories of participants to investigate theeffectiveness of the walking direction. We classified
participants into these categories between the start and the endof the guiding part by analyzing recorded videos and estimatedposition data after the experiments.

Forward condition: In the guiding stage, the robot guides
people by walking forward. Before guiding, the robot said, “Letme give you some interesting information. Please follow me.”As shown in Fig. 7-a, the robot guides people to the shop’s
front by walking forward.
Backward condition: In the guiding stage, the robot guides
people by walking backward. Before guiding, the robot says,“Let me give you some interesting information. Please follow
me.” As shown in Fig. 7-b, the robot guides people to the
shop’s front by walking backward.
3) Hypothesis and prediction
We hypothesized that the robot would elicit more
spontaneous participation in the backward condition than in theforward condition. Based on this consideration, we made thefollowing prediction:

Figure 6. Map of shopping mall

1. In the backward condition, the number of bystanders will
be larger than in the forward condition.
B. Results
1) Visitor Interactions with the Robot
In the forward condition, the robot guided people 19 times.
As shown in Fig. 7, almost all addressees stood to the side or
obliquely behind the robot while being guided. We can easily
understand the behavior of these pedestrians: the robot walks(a) Walking forward(b) Walking backward

Figure 7. Guiding scenes under each condition

The final publication is available at
http://dl.acm.org/citation.cfm?id=1734469

(a)

(b)

(c)

(d)

Figure 8. Robot elicits spontaneous participation in its guiding task

Figure 9. Numbers of each role of participants

Addressees: Visitors who talked to the robot when it was
idling.
Side-participants: Visitors who listened to the greeting
behavior of the robot with the addressees.
Bystanders: Pedestrians who looked at the robot for morethan three seconds while stopping during the guiding stage.
Pedestrians: Visitors who stayed more than 10 seconds inthe environment, except people from the above three categories.
We did not count visitors sitting on benches as pedestrians.
In a field trial, particularly with such an observational study,
it was difficult to interview all of pedestrian and ask whether
they listen to the robot. Instead, we tried to establish a simple
rule to categorize whether people was likely to listen or not, tobe used in the video analysis. As described above, this was
accomplished by “three seconds” rule. That is, we classified aperson who stopped for more than three seconds with orienting
his/her face toward the robot. We decided the three seconds
rule from a quick preliminary test. When a robot is speaking,and a person walks to a robot and stop, three seconds of stop
was enough for the person to start listening to the robot (e.g. sothat the person was able to tell what the robot spoke at the
moment).
Next, we describe how we classified the participants. For
example, when the robot greets a group of three persons, wecounted one addressee (nearest person) and two sideparticipants. If another group of three persons stopped andlooked at the robot for more than three seconds during theguiding stage, we also counted them as bystanders. On theother hand, if one addressee and two side-participants leftduring the guiding stage, we counted one addressee failure andtwo side-participant failures. In our definition, side-participants
do not become bystanders because we defined visitors wholistened to the robot’s greeting behavior with the addressees as
side-participants.
In the classification process, first, based on the above rules,
a coder coded all 269 participants into four categories byrecorded images and the estimated positions of people. In theforward conditions, 19 addressees, 32 side-participants, 15
bystanders, and 58 pedestrians were classified. In the backward
conditions, 18 addressees, 26 side-participants, 41 bystanders,and 60 pedestrians were classified. Fig. 9 shows the number ofparticipants assuming each role in each condition.
The first coder also analyzed the guiding success rate withall 95 data sets (all addressees and side-participants) to show
the effects of walking direction on addressees and sideparticipants. A data set was judged successful if his/herbehavior was presented from the beginning of the guiding to its
end. In the forward condition, 16 addressees and 26 sideparticipants were classified as “participated until the end.” In
the backward conditions, 11 addressees and 19 sideparticipants were classified as “participated until the end.”
To validate the classification accuracy, a second coderclassified a randomly chosen subset of 50 participants. From
the two coders’ classifications, Cohen's kappa coefficient [27]
was 0.647, indicating that their evaluations were considerably
consistent.

The final publication is available at
http://dl.acm.org/citation.cfm?id=1734469

The second coder, who also analyzed the guiding successrate, did verification for 18 data sets chosen at random from all
95 (all addressees and side-participants). The kappa statistic
between the two coders’ measurements was 0.74, indicatingthat their evaluations were also considerably consistent.
3) Verification of prediction 1
We verified the differences of the bystander numbers
between the backward and forward conditions with a Chisquare test that revealed significant differences between theconditions (χ2 (1) = 7.802, p<.01, φ=.211). This result indicatesthat the robot’s guidance by walking backward encouraged
people to listen more than by walking forward. In other words,
the result shows that the robot gathered a larger audience bywalking backward; prediction 1 was supported.
4) Does walking direction affect addressees?
We believe that the walking direction slightly affected theguiding success rate. In fact, the experimental results show thatthe ratio of addressee being stayed is higher for the forward
condition. We think that the walking forward is common in
guidance, so the ratio might be higher than the backward
condition. We verified the guiding success rate between the
backward and forward conditions with a Chi-square test. The
results revealed no significant difference between theconditions for the number of participants (χ2 (1) = 2.501, p =0.114, φ=.260). Similarly, there was no significant difference
for side-participants, either (χ2 (1) = 0.551, p=0.458, φ=.097).
At the very least, this experimental result did not show asignificant difference between the conditions. Note that theChi-square test result for participants is approachingsignificance and its effect size is .260, which is above theignorable level. Thus it is too early to conclude that thedirection did not affect the guiding success rate.
V.

MODELING PARTICIPATION PROCESS

A. Analysis of stopped positions of bystanders
Why did walking backward attract more bystanders? Some
literature has analyzed how people participate in suchinteraction. Katagiri et al. [28] analyzed participation in
relationship to bodily interaction and demonstrated theimportance of standing, and Mutlu et al. [29] demonstrated theimportance of gaze. But both studies addressed interaction
where people have already stopped together. To further study
how people participate in interaction when they are walking,we analyzed the data from a field experiment to establish aworking hypothesis about how people participate in mobileinteraction with a robot. Due to our human-tracking system,
we were able to measure the data that indicate the stopped
positions of bystanders.
Figures 10 and 11 show the stopped positions of bystanders
in the forward and backward conditions. Their positions aredenoted relative to the robot position, where the x- and y-axes
are identical with Fig. 6; the direction from the left to the rightis the positive x-axis, and the direction from the bottom to the
top is the positive y-axis.
Interestingly, the trend of the stopped positions seems quite
different in the forward and backward conditions. In theforward condition, the pedestrians mainly stopped around theside of the robot. In contrast, in the backward condition, thepedestrians stopped not only at its side but also around thefront of its body direction.
B. Area of audience and area of passing
The difference of the stopping positions in the two
conditions enables us to further speculate on people’s behavior
to a moving robot. We are interested in finding the area of
audience (AOA), where pedestrians tend to become membersof the audience, and the area of passing (AOP), where
pedestrians tend to keep passing.
Our analysis suggests that AOA is where the pedestrian can
see the robot’s face. In Fig. 10 (forward condition), bystanders
often stopped at one side of the robot: the direction of its face;
the robot’s gaze direction was oriented to the addressees whostood at this side of the robot (the direction from the origin to
the minus y-axis). A similar trend was observed in the
backward condition. In Fig. 11, bystanders often stopped at thedirection of the robot’s face (the direction from the origin to
the minus x-axis). These findings suggest that the direction
from which the robot is viewed encourages pedestrians to stop
walking and participate in the interaction as bystanders.
Therefore, this direction establishes the AOA.
Our analysis also suggests that AOP is where a pedestrian
has difficulty stopping. As shown in Figs. 10 and 11, almost all
bystanders failed to stop at the direction of the robot’s
movement (the direction from the origin to plus x-axis). They
also did not stop at the area between the robot and addressees.
These seem to be areas where pedestrians feel that stopping is
inappropriate, even if they wanted to observe the guiding robot
and the addressee. If pedestrians want to observe the guiding
robot, they will avoid the movement direction of the robot andthe addressees. They will also not stand in the area between the
robot and addressees. Therefore, we believe these two areas
establish the AOP.
From these considerations, we illustrated AOA and AOP inwalking forward (Fig. 12) and walking backward (Fig. 13).
C. Confirmation of AOA and AOP with observed data
We confirmed that the experiment data at least support ourworking hypothesis. As shown in the bottom right of Fig. 14,
we defined the AOA area as the overlapping of the view
direction and an observable area where other people can viewthe robot. The observable area’s radius is 7.2 m, and the view
direction is an isosceles trapezoid. We refer to the “publicdistance” of proxemics theory [30] to define the sizes of theisosceles trapezoid and the circle.
Similarly, we defined the AOP area as the union of the
movement direction of the robot and the addressee and the area
between the robot, the addressees, and the side-participants.
The area between the robot, the addressees, and the sideparticipants forms an ellipse that includes them. We also
defined the moving direction as an isosceles triangle. We referto the “social distance” of proxemics theory to define the sizeof the isosceles triangle.

The final publication is available at
http://dl.acm.org/citation.cfm?id=1734469

We measured how the defined AOA and AOP matched thegathered position data. Nine bystanders (60.0%) stopped in the
AOA and one (6.7%) stopped in the AOP in the forward
condition. In the backward condition, 27 bystanders (65.9%)
were included in the AOA and none in the AOP. We believethat the results reasonably fit our working hypothesis about
AOA and AOP.
Figure 10. Stop positions of bystanders in forward condition

Figure 11. Stop positions of bystanders in backward conditionVI.

DISCUSSION

1) Contribution to HRI research
A main contribution of this work to HRI is that it shows
how human-robot interaction changes when the robot moves
forward or backward in real environments. The experimental
results indicate that when walking backward, the robot’s
guidance encouraged people to listen more than when walking
forward. By analyzing the stopped positions of bystanders, wehypothetically drew two kinds of spaces: AOA and AOP,
which affected their stopped positions. The experimental
results reveal the validity of the effects of AOA and AOP,
whose definitions leave room for a variety of interpretations
and whose concepts can be applied to other kinds of tasks.
2) Why did backward movement attract more pedestrians?
As shown in the experimental results, backward movements
attracted more pedestrians to become bystanders than forward
movements. We believe that the AOA direction affects thenumber of bystanders.

Figure 12. AOA and AOP for walking forward

Figure 13. AOA and AOP for backward

Figure 14. Defined AOA and AOP with stopped positions of bystanders. We
calculated the movement direction and the area between robot, addressees, andside-participants from recorded position data.

In the forward condition, AOA is established on the side of
the robot. Pedestrians who encounter the robot from its
opposite side cannot enter the AOA. On the other hand, suchpedestrians can enter the AOA in the backward condition
because it is established on both sides of the robot.
Moreover, the AOA moved with the robot. In the forward
condition, pedestrians who encounter the robot from behindalso have difficulty entering the AOA because this area moves
forward with the robot. In contrast, such pedestrians easily
come into the AOA because they are already in it when they
approach the robot. These effects explain why the backward
movement encouraged more pedestrians to become bystanders.
3) Angle of robot’s faceIn the experiments, the robot primarily engaged in theinteraction with the addressee. Thus, we designed robot’s
behavior to fit with this context. For example, gaze wasallocated in this, so that the robot kept gazing at the addressee.
Therefore, there are two differences in the robot: the traveling
direction and the angle of the robot’s “face” between theforward and backward conditions. Our intention was todifferentiate the interaction motion, not the angle of the robot’s
face toward the addressee. Thus, in the forward condition, therobot and the participant walked side-by-side, and the robot
turned its face to the side to maintain eye-contact. We did notallow the robot to look backward while walking forward: itwould look odd if a humanoid robot’s head was directedbackwards. Such odd behavior might provoke strange reactions
from people and change the behaviors of pedestrians. In thisresearch, we did not design our experiment to have twoindependent motion and face-direction conditions to focus on
the effects of the robot’s travel direction.

The final publication is available at
http://dl.acm.org/citation.cfm?id=1734469

4) Limitation
Since we only conducted tests with a particular robot and in
the specific environment of a shopping mall, the generality of
the findings is limited. However, such a situation is difficult to
avoid in HRI because it is too expensive to use two or moredifferent robots and different environments to generalize
findings. We believe that our findings are applicable to otherrobots with a similar appearance and interaction complexity.
We used the same data to construct the hypothesis andverification of the hypothesis. In the future, we will try toverify the other data set.
VII. CONCLUSION
Our research focused on how a robot elicits spontaneousparticipation from pedestrians while it guides an addressee. We
proposed a model to design behavior inspired by the wellknown “participation roles” in conversation structure. We
designed a guiding behavior in which the robot walks
backward to elicit spontaneous participation. We also
developed a robot system that applies this behavior in a realenvironment.
We conducted a field trial at a shopping mall where therobot was given commercial tasks of guiding and advertising.
The robot interacted with a person, guided that person to thefront of a shop, and advertised it. The results showed that the
robot elicited more spontaneous participation by walking
backward than by walking forward. We believe that ourfindings will lead to the development of robots that act in realenvironments with commercial tasks; the concepts of AOA andAOP can easily be applied to robots that have wheels andnavigation tasks.
ACKNOWLEDGMENTS
We wish to thank the administrative staff at Sumisho Urban
Kaihatsu Co., Ltd. for their helpful participation. We also wish
to thank Dr. Akimoto, Dr. Miyashita, Mr. Glas, Dr. Satake, andMr. Kurumizawa for their help.
REFERENCES
[1]
[2]

[3]
[4]
[5]

[6]

E. A. Sisbot, et al., “Navigation in the Presence of Humans,” Humanoids
2005, pp. 181-188, 2005.
E. A. Sisbot, et al.,” Implementing a Human-Aware Robot System,”IEEE International Symposium on Robot and Human Interactive
Communication 2006, 2006.
R. Simmons, et al., “Grace: An autonomous robot for the AAAI robot
challenge,” AI Magazine, vol. 24, no. 2, pp. 51-72, 2003.
R. Gockley, J. Forlizzi, R. Simmons, “Natural Person-Following
Behavior for Social Robots,” HRI2007, pp. 17-24, 2007.
K. Dautenhahn, et al., “How may I serve you?: a robot companion
approaching a seated person in a helping context,” In Proc. Int. Conf. on
Human-robot interaction (HRI2006), pp. 172–179, 2006.
M. L. Walters, et al., “Human Approach Distances to a MechanicalLooking Robot with Different Robot Voice Styles,” IEEE Int.
Symposium on Robot and Human Interactive Communication (Ro-Man
2008), pp. 707-712, 2008.

[7]

R. Siegwart, et al., “Robox at Expo.02: A Large Scale Installation of
Personal Robots,” Robotics and Autonomous Systems, 42(3), pp. 203222, 2003.
[8] S. Thrun, et al., “Minerva: A second-generation museum tour-guiderobot,” ICRA 1999, pp. 1999–2005.
[9] W. Burgard, et al., “The interactive museum tour-guide robot,” Proc. of
National Conference on Artificial Intelligence, pp. 11-18, 1998.
[10] M. Shiomi, et al., “Interactive Humanoid Robots for a Science
Museum,” HRI2006, pp. 305-312, 2006.
[11] T. Kanda, et al., “Who will be the customer?: A social robot that
anticipates people’s behavior from their trajectories,” UbiComp2008,2008.
[12] M. Shiomi, et al., “A Semi-autonomous Communication Robot-A Field
Trial at a Train Station-,” HRI2008, pp. 303-310, 2008.
[13] M. Finke, et al., “Hey, I'm over here - How can a robot attract people's
attention,” IEEE International Workshop on Robot and Human
Communication, 2005.
[14] N. Bergstrőm, et al. “Modeling of Natural Human-Robot Encounters,“IEEE/RSJ International Conference on Intelligent Robots and Systems,
2008.
[15] Michalowski, M.P., Sabanovic, S., and Simmons, R., “A Spatial Modelof Engagement for a Social Robot,” Proceedings of AMC 2006.
[16] K. Hayashi, et al., “Humanoid robots as a passive-social medium-a field
experiment at a train station-,” HRI2007, pp. 137-144, 2007.
[17] S. Satake, T. Kanda, D. F. Glas, M. Imai, H. Ishiguro, and N. Hagita,“How to Approach Humans?-Strategies for Social Robots to Initiate
Interaction,” HRI2009, pp. 109-116, 2009.
[18] D. Glas, et al., “Laser Tracking of Human Body Motion Using Adaptive
Shape Modeling,” In Proc. Int. Conf. Intelligent Robots and Systems, pp.
602-608. 2007.
[19] E. Goffman, “Forms of Talk,” University of Pennsylvania Press, 1981.
[20] H. H. Clark, , “Using Language,” Cambridge University Press, 1996.
[21] S. Milgram, I. Bickman, and L. Berkowitz, “Note on the drawing powerof crowds of different sizes,” Journal of Personality and Social
Psychology, 13, 79-82, 1969.
[22] H. Blummer, “Collective behavior,” In A. M. Lee (Ed.), New outline ofthe principles of sociology. New York: Barnes & Noble. 1946.
[23] R. W. Brown, “Mass phenomena,” In G. Lindzey (Ed.), Handbook ofsocial psychology (Vol. 2, pp. 833-876). Cambridge, MA: AddisonWesley. 1954.
[24] S. Nishio, et al., “Robotic Platforms Structuring Information on People
and Environment,” IEEE/RSJ International Conference on Intelligent
Robots and Systems, 2008.
[25] A. Scheidig, et al., “Generating Persons Movement Trajectories on a
Mobile Robot,” IEEE International Symposium on Robot and Human
Interactive Communication, 2006.
[26] T Kanda.., Ishiguro, H., Imai, M., and Ono, T., “Development and
Evaluation of Interactive Humanoid Robots,” Proceedings of the IEEE,
Vol. 92, No. 11, pp. 1839-1850, 2004.
[27] J. Carletta.: “Assessing agreement on classification tasks: the kappa
statistic,” Computational Linguistics, Vol. 22(2), 249- 254, 1996[28] Y. Katagiri,., M. Bono, , and N. Suzuki, “Conversational Inverse
Information for Context-based Retrieval of Personal Experiences,”
Proceedings of the International Workshop on Conversational
Informatics, pp. 1-6, 2005.
[29] B. Mutlu et al “Footing In Human-Robot Conversations: How Robots
Might Shape Participant Roles Using Gaze Cues,” (HRI2009), pp. 61-68,
2009.
[30] E. T. Hall, “The Hidden Dimension,” Anchor Books, 1990.