16th IEEE International Conference on Robot & Human Interactive Communication

TA2-4

August 26 ~ 29, 2007 / Jeju, Korea

Action Sloping as a Way for Users to Notice a Robot’s Function
Kazuki KOBAYASHI1 , Member, IEEE, Yasuhiko KITAMURA2 , Member, IEEE,and Seiji YAMADA3 , Member, IEEE
1

2

Research Center for Human Media, Kwansei Gakuin University,Hyogo, Japan, kby@ksc.kwansei.ac.jp
School of Science and Technology, Kwansei Gakuin University, Hyogo, Japan, ykitamura@ksc.kwansei.ac.jp
3
National Institute of Informatics, Tokyo, Japan, seiji@nii.ac.jp

Abstract—This paper focuses on the problem that will arisein the near future from multi-function robots. Users will haveto read thick operation manuals to use them. If users canuse these robots without reading difficult manuals, it will
improve user efficiency. We then proposed Action Sloping as
a way for users to naturally recognize a robot’s function.
It provides the robots with gradual feedback signals when
the user performs given actions. By changing the intensity of
the feedback signal according to his/her action, it encourages
him/her to perform an action that will trigger the robot’s
function. In our experiments, we made three kinds of feedback
behaviors according to Action Sloping and one non-feedback
behavior as the control condition. The participants of theexperiment tried to find a robot’s function and the latencies tofirst finding the triggered action were measured. An analysis of
the latencies showed the difference between the sound feedback
group by Action Sloping and the control group. This result
showed that the effectiveness of Action Sloping was partially
supported.

I. I NTRODUCTION
There has recently been an increase in research focusing
on home robots [1]. For example, an autonomous lawn
mower called Robomow1 and an autonomous sweeping
robot called Roomba2 were developed for practical use. It is
anticipated that these types of home robots will become increasingly sophisticated and have multiple functions similar
to conventional home electric appliances. However, this willcause a usability problem. A robot with multiple functions
will confuse its users because it will become difficult forthem to comprehend all its functions. The user will also havedifficulty reading their operation manuals upon introduction
to them. Such problems are typically found in the latest
mobile phones with multiple functions.
Some researches have proposed design methods for artifacts. Norman [2] has addressed the use of affordance[3] for artifacts design and Suchman [4] has analyzed
users’ behavior patterns for machines. Taking into consideration a users’ automatic reaction to computers [5], [6]are also important for artifacts design. Yamauchi et al.
have studied function imagery of auditory signals [7] andJapanese Industrial Standards employs the auditory signals
on consumer products for elderly people guidelines [8].
In the Human-Agent Interaction research field, Ono et al.
[9] have developed a technique for understanding a robot’sinternal state by improving the user’s familiarity with therobot. Komatsu [10] has reported that users can infer amachine’s internal state from its beeps. Kobayashi et al.
have developed a method for expressing a robot’s mind
1 http://www.frietndlyrobotics.com/robomow/
2 http://www.irobot.com/

978-1-4244-1635-6/07/$25.00

2007 IEEE.

[11] and a method for controlling a robot in a more naturalmanner [12]. These researches have focused principally on
the usage of artifacts.
Our approach to the human-centered robot design problem is to investigate users’ awareness of robot functions.
Awareness is one of the important factors for designing
robots, especially when they have multiple functions. If
users can easily notice a robot’s functions without reading
the operation manuals, they will reduce their workload andincrease their operational efficiency.
In this paper, we analyze the relationship between user’s
actions and a robot’s reaction in terms of the awarenessof its function. To assist users in easily noticing a robot’s
functions, we propose “Action Sloping” as the way for
robots to react against their actions. By applying Action
Sloping to a robot, it provides gradual feedback to the users.
In the experiments, we employed three types of behaviors
according to the Action Sloping and applied them to a doglike robot, and the participants were engaged in the taskof finding the long-time-touch operation. The experimental
results suggest that Action Sloping using a sound feedback
behavior is effective in assisting users in noticing a robot’s
functions.
II. ACTION S LOPING
In this section, we explain a way for users to notice a
robot’s function without reading manuals.
A. Function Awareness
When a user purchases home electric appliances or uses
a machine for the first time, he/she reads the operation
manuals before use to comprehend their functions. However,searching for the desired functions and studying how to
operate the appliances are sometimes complicated tasks
and for robots it would probably be even more difficult.
Therefore, it would be ideal for a user to comprehend arobot’s functions without reading the manuals. With thisin mind, we then introduced Function Awareness, which
is users’ awareness of a robot’s functions. This Function
Awareness is achieved by a nonverbal interaction between
a user and a robot. Fig. 1 shows a nonverbal interaction
achieving the Function Awareness. We assume that a user
and a robot exchange nonverbal information. Although it
is possible to make robots express verbal information, thiswould be an experience similar to the manual reading forusers. Therefore, we employed a nonverbal interaction. Theinteraction is composed of the following four steps.
1) The user makes gestures or body movements for the
robot.

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Fig. 2.

Fig. 1.

Feedback Methods.

Function Awareness.

2) The robot provides feedback signals.
3) The user performs more actions according to therobot’s feedback.
4) The robot performs a function corresponding to theuser’s action.
According to this interaction, a user can easily notice
the relationship between his/her actions and the robot’s
actions. We define this user awareness of the relationshipas, Function Awareness.
B. Action Sloping for achieving Function AwarenessThe key technology for achieving Function Awarenessis how a robot helps a user easily understand its feedback
signal and perform a particular action to trigger a function.
Therefore, we propose Action Sloping, which is the way
for a robot to change the intensity of its feedback signal
according to user’s action. Some of the feedback signalsthat can be employed are changing area the lighting area,
a sound pitch, or the timing of a movement among others.
Fig. 2 (a) shows the conventional method for providing a
feedback signal. When a robot provides a feedback signal
in the conventional way, the user notices its function by
performing a triggered action. The triggered action is theuser’s action that triggers the robot’s function. In this case,the user notices a behavior that the robot performs to
execute a function as a feedback signal. If he/she performs
actions other than the triggered actions, no feedback signal
is provided and he/she will not notice the function. However,Action Sloping, showed in Fig. 2 (b), provides feedback
signals even when the user performs actions other than
the triggered action. Changing the intensity of a feedback
signal according to his/her action will encourage him/her toperform the triggered action.
For example, imagine a housekeeping robot that performs
a specific function, such as putting something away when
the user places it at a certain distance from its sensors. In
this case, the robot decreases the intensity of the light when
the user places something like a washed dish to far from adesignated point, and increases the intensity when the user
places it to close to a designated point. Fig. 3 shows that theFig. 3.

Example of Action Sloping.

robot gradually changes the intensity of its feedback from
low to high. Once the user observes the change, he/she canplace the dish closer to the robot. This user’s action causes
the robot to put it away. Thus, the user can perform anappropriate action without reading the robot’s manual.
III. E XPERIMENTS
We performed experiments to investigate the effects of
Action Sloping. Participants interacted with a robot thatused Action Sloping. We measured the amount of time taken
to find the robot’s function. We compared the effectivenessof four feedback signals to which the Action Sloping wasapplied. The following information is the details from ourexperiments.
A. Robot
We used a dog like robot, Sony’s AIBO ERS–7 (dimension: 180 × 278 ×319 mm; weight: 1.6Kg; color: white)
shown in Fig. 4. It had 18 joints (4 legs: 3 joints and 1 paw
each, 3 neck joints, 2 tail joints, 1 mouth joint), a wireless
Ethernet, a video camera, a stereo microphone, a monaural
speaker, 3 infrared sensors, 3 accelerometers, 28 LED lights
and touch sensors (head, back, and chin). These sensors
were updated every 32 msec. Fig. 5 shows the arrangementof the sensors.

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Fig. 7.

Fig. 4.

Feedback by LED lighting.

Sony AIBO (ERS–7).

Fig. 8.

Fig. 5.

Sensor arrangement of ERS–7.

We implemented it with the foreleg waving action, shownin Fig. 6. This function was performed when a participant
touched the head of the robot for more than one second
and then released his/her hand from it. The triggered action
was to touch the head for more than one second and release
his/her hand. This kind of operation provides efficient usageof the bottoms on electrical appliances and is implemented
in mobile phones. The robot did not perform any action
when the user performed actions other than the triggered
action. We used the Tekkotsu framework [13] to implement
the actions of the robot.
B. Action Sloping
We applied Action Sloping to the robot to encourage
participants to perform the triggered action. Light, sound,and motion were employed as the feedback modality. EachFig. 6.

Robot’s Function.

Feedback by motion.

feedback signal was provided for participants while touching the head of the robot. These signals were provided forless than one second. The details are described as follows.
1) Light feedback: The robot increased the lighting area
on its face, like that shown in Fig.7. When a participant
touched the head, it lit six LEDs. If the user continued totouch for 0.5 sec, it increased the number of lighted LEDs
from 6 to 14 and if they continued for 0.5 more sec, it
increased the number of LEDs from 14 to 20. If he/she
continued for more than 1.0 sec, it continued to light all
LEDs. When they released their hand, it always turned off
all LEDs.
2) Motion feedback: The robot performed part of the
movements that achieved the function. Fig.8 shows the
feedback method by motion. It performed a part of theforeleg lifting to express the progress of performing thefunction. The movement was performed according to thetouch time. It began to lift the foreleg when the user touched
the robot’s head, and finished the motion and lowered theforeleg when they retracted their hand. If the user continued
to touch the head for 1.0 sec, it maintained the final postureof the lifting.
3) Sound feedback: The robot made a sound when theuser touched its head. The sound was a sine wave andthe pitch was changed from 440 to 880 Hz according to
the touch time. It began to make the sound when theuser touched its head, and finished sounding and reset thepitch at 440 Hz when they released their hand. If theuser continued to touch the robot’s head for one second,
it finished sounding.
We employed the ERS–7’s own embedded feedback inaddition to these Action Sloping feedback signals. It wasdifficult to remove the embedded feedback. Fig.5 shows theLEDs that were used for the feedback. A LED was lit whena participant touched a sensor that was placed near the LED;
the head LED was connected to the head sensor and eachback LED was connected to each back sensor. The LEDs
were turned off if the user released their hand.

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TABLE ITASK ACHIEVEMENT RATIO .
Condition
No feedback
LED
MotionSound

Fig. 10.

Fig. 9.

Ratio
60.0 % (6/10)
88.9 % (8/9)
87.5 % (7/8)
100 %
(9/9)

Latency to first finding triggered action.

Photographs of experiment.

E. Results
C. Participants
Thirty-six participants were divided into four experimental groups:
1) No feedback condition: ten men (mean age: 23.4
years, S.D. = 2.5 years).
2) Light feedback condition: seven men and two women(mean age: 21.8 years, S.D. = 1.1 years).
3) Motion feedback condition: eight men (mean age:
21.6 years, S.D. = 1.3 years).
4) Sound feedback condition: eight men and one woman(mean age: 21.7 years, S.D. = 0.9 years).
D. Method
The experiments were executed in a small chamber (W:256 × D: 205 × H: 215 cm) at Kwansei Gakuin University.
Each participant entered the chamber and interacted with therobot. A participant sat on a char in front of a desk. The
robot was set on the desk facing sideways. The participants
were instructed that (1) the robot didn’t do anything of
its own volition, (2) it did something when he/she didsomething, (3) they needed to find an action that would
trigger the robot’s action, (4) it had one action, and (5) theexperiment was begun and finished when the experimentergave a signal and continued for about five minutes. In
addition, the participants were also instructed that (1) not
to lift the robot up, (2) not to push it strongly, (3) not toremove its components, (4) not to forcibly move its joints,and (5) not to push the power switch. The experimenter left
the chamber after they gave the start signal. Fig.9 showssome photos of an experiment.

Table I shows the task achievement ratios. Four of theten participants under the no feedback condition, one of thenine participants under the LED feedback condition, andseven of the eight participants under the motion feedback
condition could not find the triggered action within the time
limit. All the participants under the sound condition could
find the triggered action.
The average latency that was the time from the first
touch on the robot’s head until the robot first waved its
foreleg is shown in Fig.10. The average time was calculated
without the participants who could not find the triggered
action. The overall difference in the latency to first finding
the triggered action among the four groups was significant(F3,26 = 3.23, p < 0.05). The difference between the no
feedback condition and the sound condition was significant(Tukey’s HSD test, p < 0.05). We confirmed that sound was
the most efficient way to help participants find the triggered
action.
Fig.11, 12, 13, and 14 show the participants’ behavior
patterns for touching the head under the no feedback, LED,
motion, and sound conditions respectively. In the figures,
each line represents a participant, and the line includes
the information about the start and finish times, and thecumulative duration of the touching of its head. The verticalaxis represents the cumulative duration for the robot’s headbeing touched, and the horizontal axis represents the time.
The solid lines and the broken lines in each figure are used
to distinguish a line from another crossing line. We find thatthe horizontal change of lines under the no feedback and
LED conditions are bigger than those under the motion andsound conditions.
Fig.15 shows the averaged ratio of the duration oftouching the robot’s head within the latency boundary. The

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Fig. 11. Pattern of Touching Robot’s Head under No Feedback Condition.

Fig. 12.

Pattern of Touching Robot’s Head under LED Condition.

overall difference in the ratio among the four groups was
not significant (F3,26 = 0.29, p = 0.83). We found that theparticipants were engaged in touching the head about 30%of the latency time. Fig.16 shows the averaged frequencyof touching the robot’s head within the latency boundary.
The participants under the motion and sound conditions
touched the robot’s head once every two seconds, and theparticipants under the no feedback condition touched therobot’s head once every four seconds. However, the overall
difference in the frequency among the four groups was not
significant (F3,26 = 1.60, p = 0.22).

Fig. 13.

Pattern of Touching Robot’s Head under Motion Condition.

Fig. 14.

Pattern of Touching Robot’s Head under Sound Condition.

condition obviously performed differently from the others.
We need to clarify why sound is the most effective meansof recognition.
There is a study about the effect of sound made by acomputer. Komatsu [10] has reported that users can infer theattitudes of a computer by listening to its chirping sounds.
Changing the pitch of the sound might lead the participants’
to a triggered action. It is necessary to perform additional
experiments using some kind of chirping sound to confirm
the effect of Action Sloping.

IV. D ISCUSSION
A. Effectiveness of Action Sloping
The effectiveness of Action Sloping was partially supported by the experimental results. We thought that Action Sloping would be an effective way to make general
guidelines for designing manual-free robots. However, theexperimental results showed that statistically only the soundcondition was significantly faster at assisting in the participants’ triggered action than the no feedback condition.
Comparing the behavioral patterns among the conditions,
we found that the participants under the sound feedback

Fig. 15.

Ratio of Touch Duration.

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V. C ONCLUSION

Fig. 16.

Frequency of Touch.

The latencies of first finding the triggered action under
the LED, motion, and sound conditions were shorter thanthose for the no feedback condition. This result corresponded to the importance of quick feedback for the users[2]. However, the most effective modality was different
from that of a previous study [11]. That study showedthat the motion of the robot was the most effective way
to lead to a user’s action for a robot. In contrast, thiswork showed that sound was the most effective modality.
The two studies used different robots, participant’s tasks,and experimental environments. We found that the biggest
difference was the tasks. The participants in the previous
study were instructed to help a robot, and the participantsin this study were instructed to find a robot’s function. The
participants in the previous study knew the function of therobot. If the knowledge of the robot’s function is taken
into consideration, we think that motion feedback would be
the most effective way, because the users would know the
robot’s functions, and that sound would be effective when
they do not.

B. Experimental environment
The conditions of the experiments were different fromthat of the real world. We did not permit participants tolift the robot. The purpose of this restriction was to avoid
handling it roughly. They might frequently perform certain
actions instead of lifting it in the experiments. Therefore,
the latencies we obtained would be different from a realworld situation without such restrictions. However, the relative differences among the modalities should be universal
because other conditions were the same.
The experimental situation was similar to playing withthe robot because we instructed participants to find therobot’s function. Such situation will be suited for a robotthat has the ability to help users and to play with them. Users
will not try to find a robot’s unknown functions during a
progression of the operation. However, applying the aspectof entertainment, it will improve sociability of robots andmake situations similar to this one.
The problem with these experiments was that the robot
had the single function. Situations in which the robot has
multiple functions will be more natural and realistic. To
improve Action Sloping and develop practical robots, itis necessary to perform experiments using a robot withmultiple functions.

When robots have a lot of functions, users will have toread thick operation manuals to use them. If users can use
robots without reading the manuals, it will improve theirefficiency. Therefore, we propose Action Sloping as a way
for users to notice a robot’s functions. It provides gradualfeedback signals when the user performs actions. Changing
the intensity of the feedback signal according to the user’s
actions encourages them to perform actions that trigger
the robot’s function. For the experiments, we made threekinds of feedback behaviors according to Action Sloping
and one no feedback behavior as the control condition.
The participants of the experiment tried to find a robot’s
function and the latencies for first finding the triggered
actions were measured. An analysis of the latencies showed
the difference between the sound feedback group by Action
Sloping and the control group. This result showed that theeffectiveness of Action Sloping was partially supported. We
are planning additional experiments to clarify why sound is
the most effective way. When it is clear, we will improvethe method for users to notice a robot’s function.
ACKNOWLEDGMENT
This research was partially supported by the Ministry of
Education, Science, Sports and Culture of Japan, Grant-inAid for Young Scientists (Start-up), 18800067, 2006.
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