CHI 2010: Work-in-Progress (Spotlight on Posters Days 1 & 2)

April 12–13, 2010, Atlanta, GA, USA

How Do Users Interact witha Pet-Robot and a Humanoid?
Anja Austermann

Abstract

The Graduate University for Advanced Studies (SOKENDAI)In this paper, we compare users’ interaction with thehumanoid robot ASIMO and the dog-shaped robot AIBO.
We conducted a user study in which the participants
had to teach object names and simple commands andgive feedback to either AIBO or ASIMO. We did not findsignificant differences in the users’ evaluation of both
robots and in the way commands were given to the twodifferent robots. However, the way of giving positive
and negative feedback differed significantly: We foundthat for the pet-robot AIBO users tend to give reward ina similar way as giving reward to a real dog bytouching it and commenting on its performance byuttering feedback like “well done” or “that was right”.
For the humanoid ASIMO, users did not use touch as areward and rather used personal expressions like“thank you” to give positive feedback to the robot.

2-1-2 Hitotsubashi, Chiyoda.
Tokyo, 101-8430, JAPANanja@nii.ac.jp
Seiji Yamada
National Institute of Informatics
The Graduate University for Advanced Studies (SOKENDAI)
2-1-2 Hitotsubashi, Chiyoda.
Tokyo, 101-8430, JAPANseiji@nii.ac.jp
Kotaro Funakoshi
Honda Research Institute Japan
8-1 Honcho.
Wako-shi, Saitama 351-0188, JAPANfunakoshi@jp.honda-ri.com
Mikio Nakano
Honda Research Institute Japan
8-1 Honcho.

Keywords
Robots, User Studies, Human-Robot Interaction, ASIMO,AIBO

Wako-shi, Saitama 351-0188, JAPANACM Classification Keywords

nakano@jp.honda-ri.com

H5.m. Information interfaces and presentation:
Miscellaneous.

Copyright is held by the author/owner(s).

General Terms

CHI 2010, April 10–15, 2010, Atlanta, Georgia, USA.
ACM 978-1-60558-930-5/10/04.

Human Factors

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Introduction
When humans interact with each other or with theirpets they tend to adapt their way of speaking andinteracting to their interaction partner: People talk toadults in a more elaborated way than to small children,and they pet their dog as a reward while they would
rather just say “thank you” when praising a colleague.
We assume that similar mechanisms also affect how
people interact with robots. Especially the appearanceof a robot and its resemblance to familiar creatures orobjects can be an important factor which helps ahuman to anticipate the capabilities of a robot anddecide how to interact with it. The results from ourresearch can help inform the design choices thatroboticists make when considering what type ofinteraction they want with their robots.
In recent years, there have been various studies [3] [4][5] [6] investigating on the effect of a robot’s
appearance on the interaction with a user. However,most studies concerning the appearance of robots
rather deal with the uncanny valley effect [2] andusers’ impression of robots than with the effect of arobot’s appearance on its user’s communicative
behavior. Kanda et al. conducted a study with two
different humanoid robots – ASIMO and Robovie - andshowed that [4] different appearances of the robots did
not affect the participants’ verbal behavior but did
affect their non-verbal behavior such as distance anddelay of response. Goetz et al [5] investigated on users’
attribution of capabilities depending on the appearanceof a robot. They found that people systematically
preferred robots for jobs when the robot’s humanlikeness matched the sociability required in those jobs.
Similar results were obtained by Hegel et al. [3] who

April 12–13, 2010, Atlanta, GA, USA

found that the appearance of robots affected users’
attribution of possible applications.
As a part of our work on learning commands andfeedback for human-robot interaction [1], we
conducted a user study on how participants givecommands and feedback to the robots AIBO andASIMO. AIBO is a dog-shaped robot, made by Sony,which has roughly the size of a cat or a small dog.
ASIMO is a 1,30m tall humanoid robot created by
Honda.

Outline of the Study
The goal of our study is to find differences andsimilarities in user behavior when the participants givecommands and feedback to ASIMO or AIBO. The users
interacted with either ASIMO or AIBO and instructed
the robot perform different household tasks likebringing a coffee, switching on the light or the TV,tidying up etc. and gave feedback to the robot forcorrect or incorrect performance. In order to avoid

Figure 1: Visualization of the tasks

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time-consuming and error-prone task execution andbecause of the different physical capabilities of the twodifferent robots, we decided to use “virtual training
tasks”. The tasks are visualized on a screen in such a
way that the user can understand from the scene, whatcommand to give to the robot. We use a graphicalrepresentation of the scene without any text, in order toavoid influencing the participants’ wording when giving
commands to the robot.
The robot performs in front of the screen using motion
and speech. The robot’s actions are visualized on thescreen with a hand or paw icon, so that the user caneasily understand the relation between the robot’s
motions and the changes happening in the scene. A
picture of the virtual living room scene is shown in Fig. 1.
More details on the training tasks can be found in [1].
While the robots differed in shape and size we kept allother parameters as similar as possible, using the same
synthesized speech utterances, similar gestures, same
simulated learning rate etc.

April 12–13, 2010, Atlanta, GA, USA

style, when interacting with ASIMO. Details are given in
the results section.
Experimental Setting
We have conducted a user study with 16 participants
aged from 22 to 52. Ten participants (7 males, 3
females) interacted with ASIMO and six participants (4males, 2 females) interacted with AIBO for roughly 45
minutes. The language used in the experiments was
Japanese. All participants were employees of the Honda
Research Institute Japan. Fig. 2 shows the experimental
setting. The participants were told to teach the robot in
two phases. In the first phase, they were asked to namethe objects that the robot was pointing at. In the second
phase, they were instructed to give commands to therobot and give positive feedback if the robot reacted
correctly and negative feedback if the robot reacted
incorrectly. They were instructed to give commands andfeedback in the way they like by speech, gesture andtouch.

Assumptions
Based on the schema theory [7] in psychology, whichsuggests that people use schemas of familiar objects andsituations to understand and handle unfamiliar situations,
we assumed, that users are likely to interact with a petrobot like AIBO in a similar way as with a real dog, whileinteraction with a humanoid like ASIMO was expected toresemble more to the interaction with a human.
Moreover, we assumed that the participants are likely toconclude that ASIMO is more intelligent than AIBO,
based on its humanlike appearance. This might lead tohigher expectations and to a more elaborated speaking

Figure 2: Experimental Setting with AIBO and ASIMO

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April 12–13, 2010, Atlanta, GA, USA

Results
In our user study, we obtained two different kinds ofresults: We asked the participants to answer aquestionnaire about their subjective impression of theinteraction and we annotated the data, which wasrecorded during the interaction to find objective
similarities and differences in the participants’ behavior.
We used the T-Test to determine the statisticalsignificance of the observed differences.
Questionnaire results
From the results of the questionnaire, we can see aslight tendency towards more positive ratings for theinteraction with AIBO. However, none of the differences
is statistically significant.
Question

ASIMO

AIBO

(5: fully agree – 1: do notagree)

mean(stdev)mean(stdev)

I enjoyed teaching the robot
through the given task
The robot understood myfeedback
The robot learned through myfeedback
The robot adapted to my way ofteaching
I was able to instruct the robot ina natural way
The robot took too much time tolearn
The robot is intelligent
The robot behaves autonomously
The robot behaves cooperatively

3.5 (0.8)

4 (0.8)3.6 (0.9)

4.3 (1.1)

3.2 (1.3)

4.3 (0.5)

3.2 (1.1)

3.8 (1.3)

3.6 (1.1)

3.5 (1.5)

3.6 (1.4)

2.7 (0.9)

2.7 (1.3)
2.7 (1.4)
3.7 (0.8)

2.8 (1.5)
2.8 (0.9)
3.3 (0.7)

Table 1: Users’ evaluation of the training task

User behavior
We analyzed different aspects of the user’s commands
and feedback that we assumed to be related to theperceived intelligence and human-likeness of the robot.
We compared the speaking speed (in seconds perword) and the number of words percommand/feedback, as we assumed that people talkslower and in simpler sentences, when they consider
the robot less intelligent. However, we found, that thelength of commands was almost the same for both
robots. An average command for ASIMO was 3.75
(sd=0.42) words long, while an average command for
AIBO was 3.72 (sd=0.71) words long. The speaking
speed was also similar for AIBO with 0.45 (sd=0.09)
seconds per word, and ASIMO with 0.42 (sd=0.07)
seconds per word. This is in line with the participants’subjective evaluation of the robots’ intelligence, shownin Table 1.
MULTIMODALITY
During the interaction with both robots, we did notobserve pointing gestures from any of the users. A
possible explanation is that all objects were very easy
to distinguish verbally, so that pointing gestures would
have been redundant. We observed touch-based
rewards for only one out of ten participants for ASIMO
but for five out of the six participants who interactedwith AIBO. As touch is frequently used with real dogs,
we assume that users considered touch to beappropriate for giving feedback to AIBO because of its
dog-like appearance.
VERBAL COMMANDS
We analyzed how many commands had explanations
or polite expressions and how many commands werephrased as a question. We estimated that users might

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April 12–13, 2010, Atlanta, GA, USAbe more polite, explain more and use more questions
when talking to a humanoid robot, while they rather
give plain commands to a dog-like robot. We
considered commands that contain words like“…kudasai”, “…kureru?”, “…moraeru?” etc., which aresimilar to the English word “please” as polite
commands. We also analyzed, how many commands
were implicit ones like saying “it is too dark here” tomake the robot switch the light on, and in how many
commands some expected parameters were left out
like in “put away the toy car” instead of “put the toy car
into the box”, because we assumed that this kind ofverbal behavior might be related to the perceived
intelligence of the robot. The results can be found in
Table 2. The values do not add up to 100% because not
all types of commands are mutually exclusive (e.g. apolite command can have parameters left out):Type
Plain commands
Polite commands
Questions incommands
Implicitcommands
Parameters leftout
Explanations incommands

ASIMO
75.01 (14.00)
9.86 (10.88)
10.23 (3.51)

AIBO
60.83 (41.04)
26.23 (41.99)
8.34 (6.73)

3.40 (4.82)

4.10 (7.23)

6.78 (2.25)

4.13 (4.77)

1.81 (3.90)

0.95 (2.32)

Table 2: Types of commands used in the interaction withASIMO and AIBO (All values in percent, value in brackets is thestandard deviation)

While we observed quite different utterances fordifferent users, the differences seemed to be rather
caused by personal preferences, than by theappearance of the robots. This assumption is supportedby the high standard deviations between users. None ofthe observed differences was statistically significant.
VERBAL POSITIVE AND NEGATIVE FEEDBACK
We distinguished three different types of feedback:
Personal rewards like “Thank you”, which emphasize,that the robot has done something for the user,
feedback which directly comments on the performanceof the robot, like “Well done.” or “That was wrong.” andexplanations used as rewards like “That is not a toy
car, it is a ball.” or “That is a toy car.”.
Type
Personal
Performance
evaluation
Explanations

ASIMO
52.78 (17.99)
38.39 (18.28)

AIBO
24.83 (27.41)
70.02 (28.16)

11.10 (14.29)

3.56 (3.90)

Table 3: Types of feedback used in the interaction with ASIMO
and AIBO (All values in percent, value in brackets is thestandard deviation)

We found statistically significant differences for theusage of personal rewards (df=14, t=2.480, p=0.026)
and rewards, which comment on the robots’performance (df=14, t=2.745, p=0.016). While theusers usually gave feedback like “well done (yokudekimashita)” or “good (ii yo)” to AIBO, they used
more personal rewards like “Thank you (arigatou)” for
ASIMO, especially for positive reward. While theparticipants gave more explanations when talking to
ASIMO, especially for negative rewards, the difference
between both robots was not significant.

Discussion and Conclusion
In our experiments, we observed less than expecteddifferences in users’ behavior toward AIBO and ASIMO.
Our initial t-test shows some interesting differences

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between the feedbacks that were statistically significant.
However, we need to study more people including
participants from outside Honda Research Institute toconfirm this trend scientifically. While especially theway of uttering commands seems to depend rather on
the personal preferences of the user, than on theappearance of the robot, we found robot-dependent
differences in the feedback, given by the participants.
The most obvious one was the frequent use of touch forgiving feedback to AIBO, while touch was almost not
used for ASIMO. Moreover, we found, that users tended
to give personal feedback like “Thank you” to ASIMO,
while they rather commented on the performance forgiving feedback AIBO. These findings suggest thatpeople actually use their experience with real dogs as a
guideline when giving feedback to AIBO.
The users’ subjective evaluation did not reveal
significant differences between ASIMO and AIBO. As
both robots were programmed to behave in the same
way on the same task, we assume that the users’
impression of the robot’s behavior on the given task
depends rather on its actual performance than on its
appearance.
There are different possible explanations, why nosignificant differences were observed for giving
commands. One of them is that both robots used
speech to communicate with the user. As speech is atypical human modality of interacting, differences might
have been stronger, if AIBO had communicated withthe user in a more dog-like non-verbal way. As there
was no significant difference in users’ evaluation ofboth robots’ intelligence, users may have consideredsimilar types of commands acceptable for both robots.

April 12–13, 2010, Atlanta, GA, USAIn our future work, we are planning to further analyze
the variability and robot-dependence of given
commands and feedback. The results will be applied to
improve our method for learning to understandcommands and feedback through a training task.

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