Motion Overlap for a Mobile Robot to Express its Mind

Paper: jc11-8-3104: 2007/9/27

Motion Overlap for a Mobile Robot to Express its Mind
Kazuki Kobayashi∗ and Seiji Yamada∗∗∗

Research Center for Human Media, Kwansei Gakuin University
2-1 Gakuen, Sanda, Hyogo 669-1337, Japan
E-mail: kby@ksc.kwansei.ac.jp∗∗ National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda, Tokyo 101-8430, Japan
E-mail: seiji@nii.ac.jp[Received March 14, 2007; accepted May 23, 2007]

This paper discusses how a mobile robot may express
itself to get help from users in a cooperative task. We
focus on a situation in which a robot expresses its stateof mind to get a user to lend it help. The design we propose, called motion overlap (MO), enables a robot to
express human-like behavior in communicating withusers. We reasoned that human-like behavior in arobot could help the user to understand its state ofmind. We designed a small sweeping robot based on
MO that conducts back and forth movement, and compared its MO expression in experiments with othernonverbal communication, i.e., buzzers and blinking
LEDs. We found that the MO expression encouraged
most users to help the robot. Differences among results obtained for the three types of expression were
statistically significant, and results demonstrate that
MO has potential in the design of robots for the home.
Keywords: interaction design, sweeping robot, robot
mind, motion design, nonverbal communication

1. Introduction
Robots for the home, including sweeping robots andpets, have been commercialized and now are being studied for use in cooperative housework [10]. In cooperative
housework, home robots must ask users for help if theyencounter difficulties that they cannot overcome themselves, e.g., a sweeping robot unable to move a heavy
obstacle such as a chair or a table preventing from doing its job must ask a user to remove the obstacle (Fig. 1).
The problem is how to enable the robot to communicate
its requests to the user in cooperative work.
Some studies have proposed design for electric home
appliances. Norman [18] addressed the use of affordance [5] in artifact design. Suchman [25] studied behavior patterns of users. Users’ reaction to computers [8,21] is important to consider when designing artifacts.
Yamauchi et al. studied function imagery of auditory
signals [28], and Japanese Industrial Standards include
guidelines for auditory signals in consumer products for
elderly people [7]. These studies and guidelines deal with
Vol.11 No.8, 2007

Fig. 1. Robot needing a user’s help.

interfaces for artifacts that users operate directly themselves. These methods and guidelines assume use of an
artifact directly through user control – an approach that
may not necessarily work well for home robots that conduct tasks directly themselves. Robot-oriented design approaches are thus needed for home robots.
Our proposal for having a mobile robot express itself
assumes cooperative work in which the robot must makeusers aware of how to operate it and move objects blocking its operation – a trinomial relationship among the user,
robot, and object. The theory of mind (TOM) [1] deals
with such trinomial relationships. Following TOM, weterm a robot’s internal state “mind,” defined as its own
motives, intents, or purposes and goals of behavior. We
take the weak AI [22] position: robots can be made to act
as if they had a mind.
Mental expression is designed verbally or nonverbally.
If we use verbal expression, for example, we have therobot speak, saying, “Please help me by moving this obstacle.” In certain situations, the robot may simply repeat such speech because it cannot recognize the statusof its environment. Speech conveys a unique meaning,and such repetition may irritate users. We thus chose to
study nonverbal methods such as buzzers, blinking lights,

Journal of Advanced Computational Intelligenceand Intelligent Informatics

1

Kobayashi, K. and Yamada, S.

and movement, which convey ambiguous information thatusers can interpret as they like based on the given situation.
We consider that the motion-based approach feasiblyand effectively conveys the robot’s mind in an obstacleremoval task. Movement is designed based on motion
overlap (MO) that enable the robot to move in a waythat the user narrows down possible responses and acts
appropriately. In the obstacle-removal task, we had therobot move back and forth in front of an obstacle, andconducted experiments compared MO to other nonverbal
approaches. Experimental results showed that MO has
potential in the design of robots for the home.

2. Related Work
We assume that the mobile robot is cylindrical and expresses itself through movement. This has advantages fordevelopers in that the robot needs no components such as
displays or speech synthesizer, but it is difficult for the
robot to express itself in a humanly understandable manner. Below, we give an overview of studies on how a robot
can express itself nonverbally based on whether the robot
is human-like or nonhuman-like in shape.

2.1. Expression with Human-Like Shape
Hadaly-2 [6], Nakata’s dancing robot [16], Kobayashi’s
face robot [9], the document-delivery robot CREO [23],Breazeal’s Kismet [2], Kozima’s Infanoid [12], RobovieIII [14], and Cog [3] are examples of human-like robots
that easily express themselves nonverbally in a humanly
understandable manner. The robot we are interested in,
however, is nonhuman-like in shape, only having wheels
for travel. We designed wheel movement to enable therobot to express its mind.
2.2. Expression with Nonhuman-Like ShapeOno et al. [20] studied how a mobile robot’s familiarity influenced a user’s ability to understand what wason its mind. Before their experiment, participants wereasked to “grow” a life-like agent on a PC, after which it
was moved to the robot’s display. Matsumaru et al. [13]developed a mobile robot that expresses its direction of
movement with a laser pointer or animated eye. Komatsu [11] reported that users could infer the attitude ofa machine through its beeps. Those require extra components, unlike our proposal. The orca-like robot [17],seal-like Paro [24, 27], and limbless Muu [19] are efforts
of familiarizing users with robots. Our study differs fromthese, however, in that we assume actual cooperative work
between the user and robot, such as sweeping.

3.1. Obstacle-Removal Task
The situation involves a sweeping robot unable to remove an obstacle such as a chair that asks a user to remove
the chair so that it can sweep the floor where the obstacle
was (Fig. 1). Such an obstacle-removal task serves as a
general testbed for our work because it occurs often incooperative tasks between users and robots. To execute
this task, the robot must communicate its problem to theuser and ask for help. This task has been used in researchon cooperative sweeping [10].
Obstacle-removal tasks generally accompany otherrobot tasks. Obstacle avoidance is essential to mobile
robots such as tour guides [4]. Obstacles may be avoided
by having the robot (1) avoid an obstacle autonomously,(2) remove the obstacle autonomously, or (3) get user
to remove the obstacle. It is difficult for a robot to remove an obstacle autonomously because it first must decide whether it may touch the object. In practical situations, the robot avoids an obstacle either by autonomous
avoidance or having a user remove it.
3.2. Motion Overlap
Our design, motion overlap (MO), starts when movement routinely done by a user is programmed into a robot.
A user observing the robot’s movement will find an analogy to human action and easily interprets the state ofmind. The overlap between user and robot movement
causes an overlap between the minds of the user and therobot (Fig. 2).
Human beings are not natural light emitters nor do they
convey intent easily using nonverbal sounds. They do,
however, move expressively when executing tasks. We
therefore presume that a user can understand a robot’s
mind as naturally as another person’s mind if robot movement overlaps recognizable human movement.
As stated above, nonverbal communication has alternative modalities: a robot can make a struggling movement,sound a buzzer, or blink a light. We assume movement tobe better for the obstacle-removal task for the following
reasons.
• Feasibility: A robot must move as it executes tasks,

so a motion-based approach requires no additional
components such as LEDs or speakers that othernonverbal approaches would require, making themmore expensive.
• Variety:

The motion-based approach enables us
to design informational movement to suit different
tasks. The variety of movements possible is far larger
than the variety of sounds or light signals of othernonverbal methods.

• Stress minimization: Other nonverbal methods, par-3. Robot Mind Expression
The obstacle-removal task in which we have the robot
express itself in front of an obstacle and how the robot
2

conveys what is on its mind are explained below.

ticularly sound, may force a user to direct attention specifically at the robot, causing more stressthan movement would. The motion-based approach

Journal of Advanced Computational Intelligenceand Intelligent Informatics

Vol.11 No.8, 2007

Motion Overlap for a Mobile Robot to Express its Mind

Fig. 3. back and forth movement.

Fig. 4. Experimental environment.
Fig. 2. Motion overlap.

avoids distracting or invasive interruption of a user
who notices the movement and chooses whether or
not to respond.
• Effectiveness:

Motion-based information is intuitively more effective than other nonverbal approaches because interesting movement attracts auser to a robot without stress.

While feasibility, variety, and stress minimization in
motion-based information may appear intuitively valid,
however, effectiveness must be verified experimentally.

3.3. Application of MO to Mobile Robot
We designed the robot’s movement easily understoodby users considering what human beings might do when
faced with the obstacle-removal task. Take a person
whose hands are full of baggage hesitating nervously infront of a closed door. Most human observers would identify the problem immediately. Using similar hesitation
could enable the robot to inform the user that it needs
help. Here again, however, implementation of such behavior depends in part on the robot’s shape, i.e., whether
it is human-like or not.
A study on human actions in doing tasks [26] defines
hesitation as movement that suddenly stops and either
changes into other movement or is suspended – a definition that our back and forth movement fits (Fig. 3). Seeing
a robot move back and forward in a short time in front ofan obstacle should be easy for users because human beings act similarly when they are in trouble.
Vol.11 No.8, 2007

We could have tested other movement such as turning to the left and right, but, back and forth movement
keeps the robot from swerving from its trajectory. It isalso easily applicable to other hardware such as manipulators. Back and forth movement is thus appropriate forthe obstacle-removal task in efficiency of movement andrange of application.

4. Experiments
We conducted experiments to verify the effectivenessof our motion-based approach in an obstacle-removaltask, comparing the motion-based approach to two othernonverbal approaches.

4.1. Environment and Robot
Figure 4 shows the flat experimental environment(400 × 300 mm) surrounded by a wall and containing two
obstacles. It simulated an ordinary human work spacesuch as a desktop. Obstacles corresponded to penholders, remote controllers, etc., and are easily moved byparticipants. We used a small mobile robot, Khepera II(Fig. 5), which has eight infrared proximity and ambient light sensors with up to a 100 mm range, a Motorola
68331 (25 MHz) processor, 512K bytes of RAM, 512K
bytes of flash ROM, and two DC brushed servomotors
with incremental encoders. Its C program runs on RAM.
4.2. Robot Expression
Participants observed the robot as it swept the floor in
the experimental environment. The robot used ambigu-

Journal of Advanced Computational Intelligenceand Intelligent Informatics

3

Kobayashi, K. and Yamada, S.

Fig. 5. Khepera II.

Fig. 7. Detailed experimental setup.

Fig. 6. Pattern of behavior.

ous nonverbal expressions enabling participants to interpret them based on the situation. We designed three typesof signaling to convey the robot’s mind to sweep the area
under an obstacle or the wish for wanting help to remove
the obstacle. It expressed itself using one of the three following types of signaling:• LED: The robot’s red LED (6 mm in diameter)

blinks based on ISO 4982:1981 (automobile flasher
pattern). The robot turns the light on and off basedon the signal pattern in Fig. 6, repeating the pattern
twice every 0.4 seconds.
• Buzzer: The robot beeps using a buzzer that madea sound with 3 kHz and 6 kHz peaks. The soundpattern was based on JIS:S0013 (auditory signals of
consumer products intended for attracting immediate
attention). As with the LED, the robot beeps at “on”
and ceases at “off” (Fig. 6).
• Back and forth movement: The robot moves backand forward, 10 mm back and 10 mm forth based on“on” and “off” (Fig. 6).
The LED, buzzer, and movement used the same “on”
and “off” intervals. The robot stopped sweeping and performed each when it met an obstacle or wall, then turned
left or right and moved ahead. If the robot senses an obstacle on its right (left), it makes a 120-degree turn to the left
(right), repeating these actions during experiments. Notethat the robot did not actually sweep up dust.

4.3. Methods
Participants were instructed that the robot represented
a sweeping robot, even though it actually did not sweep.
4

They were to imagine that the robot was cleaning thefloor. They could move or touch anything in the environment, and were told to help the robot if it needed it.
A participant conducted three trials and saw the robot
move back and forth, blink its lights, or sound its buzzer.
The order of expression was random. A trial finished after
the robot’s third encounter with obstacles, or when theparticipant removed an obstacle. No information about
the robot’s movement, blinking, or sounding.
Figure 7 details experimental settings that include therobot’s initial locations and those of objects. At the startof each experiment, the robot moved ahead, stopped in
front of a wall, expressed itself, then turned right toward
obstacle A. Fig. 8 shows the experiment. A participant sat
on the chair and helped the robot on the desk.
Participants numbered 17 – 11 men and 6 women aged
21-44 including 10 university students and 7 employees.
We confirmed that they had no experience in interactingwith robots for the home.

4.4. Evaluation
We used the criterion that fewer expressions were better because this would help participants understand easily what was on the robot’s mind. The robot expressed
itself whenever it encountered a wall or obstacle. We
counted the number of participants who moved the object
just after the robot’s first encounter with the object. We
considered using other measurement such as the period
from the beginning of the experiment to when the participant moved an obstacle, but, this was difficult because thetime at which the robot reached the first obstacle differedsomewhat for each trial. Slippage of the robot’s wheelschanged its trajectory.
4.5. Results
Table 1 shows participants and behavior in experiments. Terms in bold marked by an asterisk are trial inwhich a participant removed an obstacle. Eight of 17

Journal of Advanced Computational Intelligenceand Intelligent Informatics

Vol.11 No.8, 2007

Motion Overlap for a Mobile Robot to Express its Mind

Table 1. Participant behavior.
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

Age
25
30
24
25
23
43
27
29
44
26
29
27
36
27
26
26
21

Gender
M
M
M
M
M
F
M
F
F
F
F
M
M
M
M
M
F

Trial 1
LED*
BuzzerMO
LED*
Buzzer*MO
LED
LED
BuzzerBuzzerMO
LED
MO
BuzzerBuzzer*MO
LED

Trial 2
Buzzer*
MO
LED
MO*
LED
LED
BuzzerMO*
MO*
LED
BuzzerBuzzer
LED
LED
MO*
BuzzerBuzzerTrial 3MO*
LED
BuzzerBuzzer*MO*
BuzzerMO*
Buzzer*
LED*
MO*
LED
MO*
Buzzer
MO
LED*
LED
MO

Table 2. Expressions and trials.

LED
BuzzerMO

Trial 133% (2/6)
33% (2/6)
0% (0/5)

Trial 20% (0/6)
17% (1/6)
80% (4/5)

Trial 340% (2/5)
40% (2/5)
71% (5/7)

Fig. 8. MO experiment.

(47%) did not move any obstacle under any three experimental condition. Table 2 shows ratios of participants moving the obstacle under each condition. Ratios
increased with the number of trial. This appeared moreclearly under the MO condition.
Figure 9 shows ratios of participants who moved the
obstacle immediately after the robot’s first encounter with
it. More participants responded to MO than to either thebuzzer or light. We statistically analyzed the differencesin ratios among the three methods. The result of the statistical test (Cochran’s Q test) showed significant differencesamong methods (Q = 7.0, d f = 2.0, p < .05). We conducted a multiple comparison test, Holm’s test, and obtained 10% level differences between MO–LED (Q = 5.0,
d f = 1.0, p = 0.0253 ,α  = 0.0345 1 ) and MO–buzzer(Q = 4.0, d f = 1.0, p = 0.0455, α  = 0.0513), indicatingthat MO is as effective or more effective than the othertwo methods.
1. α  is the modified significant level by Holm’s test.

Vol.11 No.8, 2007

Fig. 9. Ratios of participants who moved the object.

In the questionnaire on experiments (Table 3), mostparticipants said they noticed the robot’s action. Table 4shows results of the questionnaire. We asked participants why they moved the object. The purpose of ourdesign policy corresponds to question (1). More people
responded positively to question (1) for the cases of thebuzzer and MO. MO achieved our objective because it
caused the most participants to move the object.

Journal of Advanced Computational Intelligenceand Intelligent Informatics

5

Kobayashi, K. and Yamada, S.

Table 3. Number of participants who noticed the robot’s
expression.

LED

BuzzerMO

I noticed.

15

17

16

I don’t remember.

2

0

1

Table 4. Results of the questionnaire.

1.
2.
3.

I felt that the robot
wanted me to move it.
I moved it dependingon the situation.
I don’t know
why I moved it.

LED

Buzzer

MO

1

3

4

2

3

3

1

1

1

4.

I felt some urgency.

0

2

1

5.

Other reason.

0

0

1

Total

4

9

10

5. Discussion
We discuss the effectiveness and application of MO
based on experimental results in the sections that follow.

5.1. MO EffectivenessWe avoided using loud sounds or strong lights because
they are not appropriate for a home-use robot. We confirmed that participants correctly noticed the robot’s expression. Results of the questionnaire (Table 3) showedthat the expressions we designed were appropriate for experiments.
MO is not always effective in every situations because
Table 2 suggests the existence of a combination effect.
Although participants experienced MO in previous experiments, only 40% of them moved the obstacle under
the LED–Trial3 and Buzzer–Trial3 conditions. Under the
MO–Trial1 condition, no participants moved the obstacle.
Further study of the combination effect is thus important.
We used specific lighting and sound patterns for expressing the robot’s mind, but, the effects of other patterns are not known. A more frequent, complex soundpattern, for example, may help users to understand therobot’s mind more easily. The expressive patterns we usedwere a few of many. A more organized investigation onlight and sound is thus needed to determine more effective
patterns. Our results show that conventional methods are
not sufficient and that MO shows promise.
Questionnaire results (Table 4) show that many participants felt that the robot “wanted” them to move the obsta6

cle or moved it depending on the situation. The “wanted”
response reflects anthropomorphization of the robot. The“depending on the situation” response may indicate thatthey identified with the robot’s problem. As Reeves etal. [21] and Takeuchi et al. [8] have noted participants
exhibiting interpersonal action with the robot would notreport the appropriate reason, so questionnaire results are
not conclusive. MO may, however, encourage users to anthropomorphize robots.
Table 4 compares MO and the buzzer, which received
different numbers of responses. Although fewer participants moved the obstacle after the buzzer than after MO,
the buzzer had more responses in the questionnaire. The
buzzer might offer highly ambiguous information in experiments. The relationship between the degrees of ambiguity and expression is an important issue in designing
robot behavior.

5.2. MO Applications
Results for MO were more promising results than forother nonverbal methods, but are these results generalizable? Results directly support the generality of obstacleremoval tasks. We consider that an obstacle-removal task
is a common subtask in human-robot cooperation. For
other tasks without obstacle-removal, we may need to design another type of MO-based informative movement.
The applicable scope for MO is thus an issue for future
study.
Morris’s study of human behavior [15] suggests the applicability of MO. Morris states that human beings sometimes move preliminarily before taking action, and thesepreliminary movements indicate what they will do. A
person gripping the arms of a chair during a conversation may be trying to end the conversation but does notwish to be rude in doing so. Such behavior is called
an “intention movement” and two movements with theirown rhythm, such as left-and-right rhythmic movements
on a pivot chair, are called “alternating intention movement.” Human beings easily grasp each other’s intent indaily life. We can consider the back and forth movement
to be a form of alternating intention movement meaningthat the robot wants to move forward but cannot do so.
Participants in our experiments may have interpreted therobot’s mind by implicitly considering its movements asalternating intention movement. Although the LED andbuzzer rhythmically expressed itself, they may have beenless effective than MO. Participants may not have considered them as intention movement because they were notpreliminary movement – sounding and blinking were notrelated to previous movement, moving forward.
If alternating intention movement works well in enabling a robot to inform a user about its mind, the robot
will be able to express itself with other simple rhythmic
movements, e.g., the simple left and right movements toencourage the user to help it when it loses the way. Rhythmic movement is hardware-independent and easily implemented. We believe that alternating intention movement
is an important element in MO applications, and we plan

Journal of Advanced Computational Intelligenceand Intelligent Informatics

Vol.11 No.8, 2007

Motion Overlap for a Mobile Robot to Express its Mindto study this and evaluate its effectiveness. A general implementation for expressing robot’s mind can be established through such investigations. The combination of
nonverbal and verbal information is important for robot
expression, and we plan to study ways to combine different expression to speed up interaction between users androbots.

6. Conclusions
We have proposed a motion-based approach for nonverbally informing a user of a robot’s state of mind. Possible nonverbal approaches include movement, sound, andlights. The design we proposed, called motion overlap,
enabled a robot to express human-like behavior in communicating with users. We devised a general obstacleremoval task based on motion overlap for cooperation between a user and a robot, having the robot move back andforth to show the user that it wants an obstacle to be removed. We conducted experiments to verify the effectiveness of motion overlap in the obstacle-removal task, comparing motion overlap to sound and lights. Experimental
results showed that motion overlap encouraged most users
to help the robot.
The motion-based approach will effectively convey
robot’s mind in obstacle-removal tasks and contribute todesign of robots for the home. The applicable scope formotion overlap, ways to combine different expression to
speed up interaction between users and robots, and investigation into effects of alternating intention movement areissues for future study.
Acknowledgements
This research was supported in part by the Ministry of Education,
Science, Sports and Culture of Japan, Grant-in-Aid for Young Scientists (Start-up) 18800067, 2006.

References:[1] S. Baron-Cohen, “Mindblindness: An Essay on Autism and Theoryof Mind,” MIT Press, 1995.
[2] C. Breazeal, “Regulation and entrainment for human-robot interaction,” International Journal of Experimental Robotics, Vol.21,
No.11-12, pp. 883-902, 2002.
[3] R. Brooks, C. Breazeal, M. Marjanovic, B. Scassellati, and
M. Williamson, “The Cog Project: Building a Humanoid Robot,”
Computation for Metaphors, Analogy and Agent, Lecture Notes in
Computer Science, Vol.1562, pp. 52-87, 1999.

[9] H. Kobayashi, Y. Ichikawa, M. Senda, and T. Shiiba, “Realization of Realistic and Rich Facial Expressions by Face Robot,” In
IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1123-1128, 2003.
[10] K. Kobayashi and S. Yamada, “Human-Robot Cooperative Sweeping by Extending Commands Embedded in Actions,” In Proc. of2005 IEEE/RSJ International Conference on Intelligent Robots andSystems, pp. 1827-1832, 2005.
[11] T. Komatsu, “Can we assign attitudes to a computer based on its
beep sounds?,” In Proc. of the Affective Interactions: The computerin the affective loop Workshop at Intelligent User Interface 2005,
pp. 35-37, 2005.
[12] H. Kozima and H. Yano, “A robot that learns to communicate withhuman caregivers,” In Proc. of International Workshop on Epigenetic Robotics, pp. 47-52, 2001.
[13] T. Matsumaru, K. Iwase, K. Akiyama, T. Kusada1, and T. Ito,
“Mobile Robot with Eyeball Expression as the PreliminaryAnnouncement and Display of the Robot’s Following Motion,” Autonomous Robots, Vol.18, No.2, pp. 231-246, 2005.
[14] T. Miyashita and H. Ishiguro, “Human-like natural behavior generation based on involuntary motions for humanoid robots,” Robotics
and Autonomous Systems, Vol.48, No.4, pp. 203-212, 2003.
[15] D. Morris, “Manwatching,” Elsevier Publishing Projects, 1977.
[16] T. Nakata, T. Mori, and T. Sato, “Analysis of Impression of Robot
Bodily Expression,” Journal of Robotics and Mechatronics, Vol.14,
No.1, pp. 27-36, 2002.
[17] T. Nakata, T. Sato, and T. Mori, “Expression of Emotion and Intention by Robot Body Movement,” In Intelligent Autonomous Systems 5, pp. 352-359, 1998.
[18] D. A. Norman, ”The Psychology of Everyday Things,” Basic
Books, 1988.
[19] M. Okada, S. Sakamoto, and N. Suzuki, “Muu: Artificial creatures
as an embodied interface,” In Proc. of 27th International Conference
on Computer Graphics and Interactive Techniques (SIGGRAPH
2000), the Emerging Technologies: Point of Departure, p. 91, 2000.
[20] T. Ono, M. Imai, and R. Nakatsu, “Reading a Robot’s Mind: A
Model of Utterance Understanding based on the Theory of Mind
Mechanism,” International Journal of Advanced Robotics, Vol.14,
No.4, pp. 311-326, 2000.
[21] B. Reeves and C. Nass, “The Media Equation: How People Treat
Computers, Television, and New Media Like Real People andPlaces,” Cambridge University Press, 1996.
[22] J. Searle, “Minds, brains, and programs,” Behavioral and Brain Sciences, Vol.3, No.3, pp. 417-457, 1980.
[23] K. Severinson-Eklundh, A. Green, and H. Hüttenrauch, “Social andcollaborative aspects of interaction with a service robot,” Robotics
and Autonomous Systems, Vol.42, pp. 223-234, 2003.
[24] T. Shibata, K. Wada, and K. Tanie, “Subjective Evaluation of Seal
Robot in Brunei,” In IEEE International Workshop on Robot andHuman Interactive Communication, pp. 135-140, 2004.
[25] L. A. Suchman, “Plans and Situated Actions: The Problem of
Human-Machine Communication,” Cambridge University Press,
1987.
[26] K. Suzuki and M. Sasaki, “The Task Constraints on Selection of
Potential Units of Action: An Analysis of Microslips Observed inEveryday Task,” Cognitive Studies, Vol.8, No.2, pp. 121-138, 2001
(in Japanese).
[27] K. Wada, T. Shibata, T. Saito, and K. Tanie, “Psychological andSocial Effects in Long-Term Experiment of Robot Assisted Activity to Elderly People at a Health Service Facility for the Aged,” In
IEEE/RSJ International Conference on Intelligent Robots and System, pp. 3068-3073, 2004.
[28] K. Yamauchi and S. Iwamiya, “A Study on Functional Imagery
and Onomatopoeic Representations of Auditory Signals Using
Frequency-modulated tones,” In Proc. of Youngnam-Kyushu Joint
Conference on Acoustics, 2005.

[4] W. Burgard, A. B. Cremers, D. Fox, D. Hahnel, G. Lakemeyer,
D. Schulz, W. Steiner, and S. Thrun, “The Interactive Museum
Tour-Guide Robot,” In Proc. of the Fifteenth National Conferenceon Artificial Intelligence, pp. 11-18, 1998.
[5] J. J. Gibson, “The Ecological Approach to Visual Perception,”
Lawrence Erlbaum Associates Inc., 1979.
[6] S. Hashimoto et al., “Humanoid Robots in Waseda University– Hadaly-2 and WABIAN,” Autonomous Robots, Vol.12, No.1,
pp. 25-38, 2002.
[7] Japanese Industrial Standards (Ed.), “JIS S 0013:2002 Guidelines
for the elderly and people with disabilities – Auditory signals on
consumer products,” Japanese Industrial Standards, 2002.
[8] Y. Katagiri and Y. Takeuchi, “Affective Minds,” chapter 21. Reciprocity and its Cultural Dependency in Human-Computer Interaction, Elsevier, 2000.

Vol.11 No.8, 2007

Journal of Advanced Computational Intelligenceand Intelligent Informatics

7

Kobayashi, K. and Yamada, S.

Name:
Kazuki Kobayashi

Affiliation:
Kwansei Gakuin University

Address:
2-1 Gakuen, Sanda, Hyogo 669-1377, Japan

Brief Biographical History:
2006- Postdoctoral fellow, Research Center for Human Media, Kwansei
Gakuin University

Main Works:• K. Kobayashi and S. Yamada, “Human-Robot Cooperation with
Commands Embedded in Actions,” Transaction of the Japanese Society for
Artificial Intelligence, Vol.21, No.1, pp. 63-72, 2006 (in Japanese).

Membership in Academic Societies:•The Institute of Electrical and Electronics Engineers, Inc. (IEEE)•The Japanese Society for Artificial Intelligence (JSAI)•The Institute of Electronics, Information and Communication Engineers(IEICE)

Name:
Seiji Yamada

Affiliation:
National Institute of Informatics

Address:
2-1-2 Hitotsubashi, Chiyoda, Tokyo 101-8430, Japan

Brief Biographical History:
1989- Research Associate, Osaka University
1991- Lecturer, Osaka University
1996- Associate Professor, Tokyo Institute of Technology
2002- Professor, National Institute of Informatics

Main Works:• S. Yamada, “Evolutionary Behavior learning for Action-Based
Environment Modeling by a Mobile Robot,” Applied Soft Computing,
Vol.5, Issue 2, pp. 245-257 (2005).

Membership in Academic Societies:• The Institute of Electrical and Electronics Engineers, Inc. (IEEE)• Association for the Advancement of Artificial Intelligence (AAAI)• The Japanese Society for Artificial Intelligence (JSAI)

8

Journal of Advanced Computational Intelligenceand Intelligent Informatics

Vol.11 No.8, 2007