2012 IEEE RO-MAN: The 21st IEEE International Symposium on
Robot and Human Interactive Communication.
September 9-13, 2012. Paris, France.

Behavioral Turing Test using Two-axis Actuators
Hirotaka Osawa, Kunitoshi Tobita, Yuki Kuwayama, Michita Imai, and Seiji YamadaAbstract— The Turing test is an imitation game fordetermining the intelligence of an agent. In spite of its simplified
setting, the use of natural language between two agents in thetest is still too high a hurdle for achieving fruitful results in thefield of artificial intelligence. In this paper, the authors propose
a variation of the Turing test with a restricted communication
method. This modified test uses behaviors generated by two-axis
actuators for communication instead of the natural language
dialogue used in the normal Turing test. This reduction of scope
reveals what kinds of features are essential for an imitation
game, and broaden the application brought by Turing test.
When we learn what sorts of communication become possible
with restricted actuation, we can apply this knowledge to any
kind of robot or device in the real world. First, we tried todetermine what elements are critical for communication
between a user and a robot through a preliminary experiment
involving
human-humancommunication.
A
humanmanipulator received a video image as input and controlled a"robot box" with two actuators in a way that would lead a user
to put other objects into the box. The results indicated whatkinds of behavior are required to show the intention of themanipulator to the user. Second, we analyzed the result of thepreliminary experiment, organized a behavioral model from theresult, and programmed the robot box to run the model. The
behavior of the robot was programmed according to the user’s
head and hand locations as identified by a motion capturessystem. The robot automatically interact with a human without
human manipulation with this program. Third, we conducted a
behavioral Turing test in a communication task whereby thehuman collected items according to the instructions of the robot
box. In this test, two actuators on the box is controlled both byhuman manipulator and our program. The answers of users
suggests that the users could not identify which is controlled bya human manipulator or the program. This result indicates that
the Turing test succeed in a restricted behavioral level.

I. INTRODUCTION
The Turing test is a test to judge the intelligence of an
automatic system through an imitation game [1]. In it, one
participant interacts with another participant by sending text
messages. The test is passed by the participant sending themessage is if the other participant decides it (or he or she) is
human. The test was originally based entirely on natural
language exchanges.
However, the Turing test depends on the evaluator's
background knowledge, context, and conditions. Cohen notedthat this dependency on participants makes it hard to evaluate
intelligence appropriately [2]. Hayes showed that the
ambiguity of judging prevents the Turing test from
H. Osawa, K. Tobita, Y. Kuwayama and M. Imai are with Keio University,
Address
osawa@ayu.ics.keio.ac.jp,
tobita@ayu.ics.keio.ac.jp,
kuwayan@ayu.ics.keio.ac.jp, michita@ics.keio.ac.jp.
S. Yamada is with the National Institute of Informatics, Addressseiji@nii.ac.jp.
978-1-4673-4605-4/12/$31.00 ©2012 IEEE

328

identifying useful AI applications [3].
The fields of theoretical artificial intelligence and humanagent interaction are concerned with developing intelligencein virtual and real agents like robots [4]. These agents do not
require the level of intelligence needed to pass the Turing test,
but rather intelligent seeming behaviors that would attract
users and maintain smooth interactions. For example,
artificial agents in a video game are required to behave more
intelligently because it is important to attract users to thegame. Animal robots like AIBO and PARO also require
intelligent behaviors to facilitate long-term interactions
between themselves and the user [5][6]. Semi-automatic
robots that account for human and robot behaviors also
require intelligent behaviors. Glas et al. proposed that robot
behaviors will soon be sophisticated enough for them to act as
guides for humans under the condition that if the robot cannot
understand the user's needs, it would give control to its humanoperator [7]. Semi-automatic interaction also has a potential
to improve robotic puppets [8].
The pressing requirement for some level of intelligence inan agent mandates that we look at intelligence tests from adifferent aspect. An artificial "intelligence" need not pass theTuring test on natural language in order to be useful, butrather it should be able to pass a basic version of the test, onethat tests the intelligence of, say, simple behaviors likemotions or gestures.
In spite of its potential usefulness, previous studies havenot examined the idea of a behavioral Turing test well.
Mckinstry trial to use Boolean questions for Turing test called
Minimum Intelligent Signal Test [9] is still hard challenge
because of languages. Some have posited variations that use
means other than natural language. A progressive example is
TTT (Total Turing test) [10]. This test examines interactions
with machines fully imitating humans. Ishiguro conducted a
TTT using an android that copied his appearance, called
Geminoid [11]. Although this android based study is amilestone in artificial intelligence, it is not appropriate
method for today's technology to be usefully tested, because
this variation includes all kinds of human features and is moredifficult than the original test. To make applicable Turing test,
we need to focus on ‘shrinking’ rather than expanding thefeatures of the Turing test.
Imitation and learning by demonstration studies in robotics
tried to create primitive behaviors for robots [12][13][14].
These studies focuses more about imitation of each behavior
than interaction model. It is not enough to construct Turing
test because their model was not directly compared withhuman manipulation. Gestural Turing test is one of theexample to shrink the communication channel [15]. They

used simple dots as communication channel with motion
capture system. However, their artificial algorithms are
reproduction of recorded gestures and imitation ofparticipant's gestures. Both algorithms are less interactive forusers behaviors. Their variation is too to create applicationfrom here.
To complete reasonable and applicable Turing test ininteractive task, we tried a behavioral Turing test that is
conducted with a two-axis actuator based robot. Thisvariation limits the communication method between the user
and the system. It uses the positions of the user's face andhands as the input of the system and uses a two-axis actuatoras the output. The behaviors of the two-axis actuator are very
simple. However, after referring several human robot
interaction studies [16][17], we decided that that it would be
sophisticated enough to construct useful interactions. This
simplified communication situation makes it easy to apply
this method to virtual agents and moving machinery likemobile robots and interactive home appliances.
First, we tried to determine what elements are required forcommunication between a user and a robot through apreliminary experiment. In the experiment, a humanmanipulator received a video image as input and controlled a"robot box" in a way that would lead a user to put objects into
the box. The results indicated what kinds of behavior are
required to inform the user about the intention of themanipulator. The results also indicate that there are three
distinctive phases to informing one’s intention. Second, we
programmed an automatic interaction system embodying theresults of the preliminary experiment and copying humanmanipulator's behavior. The behavior of the robot wascontrolled by the program, and the program used the user’s
head and hand locations, as picked up by motion capture
sensors. The experiment reveals what behaviors and timings
are used by humans to convey their intentions. Third, we
conducted Turing test in a communication task whereby thehuman collected items according to the instructions of therobot box.
The rest of this paper is organized as follows. Section 2
describes the details about preliminary experiment. Section 3
models the results of the preliminary experiment, and themodels were implemented in the automatic interaction system
without human operators in Section 4. In section 5, we
confirms behavior of our model. In the evaluation, the robot
led the user to pick the right object. We conducted behavioral
Turing test in section 6 and discusses the appropriateness of
our test with statistical analysis. Section 7 concludes thepaper.

robot by it. The tasks for the manipulators were as follows.
First, the manipulator led the user to move all books on thedesk into the robot box. Second, the manipulator led the user
to sit down on the chair. Third the manipulator led the user tomove all other objects on the desk into the robot box. Fourth,
the manipulator stopped the user from standing up.
The setup of the experimental room is shown in Fig. 1. The
manipulator tried to express her/his intention just throughmoving the robot. The manipulator was isolated from the user
and could not interact with any auditory communication.
Eight participants (two females and six males) took turns as
the users of the robot. They entered the experimental roomand conducted 10 minutes of interaction. They were neither
instructed what would happen in the room nor what was
required. They also did not know that the robot was controlled
by a hidden manipulator. The absence of explanation was tokeep the user ignorant about the aim of the experiment. This
setup prevented the user from exploiting any backgroundknowledge about the interaction.
We set up a two-axis-actuator robot box in theexperimental room. Figure 2 shows the robot outside (topleft) and in the box (bottom left). The robot had two servo
motors and could rotate  15 degrees in pitch and  180
degrees in yaw. The minimum step of the servo motor was 0.9
degree. The actuators and the controller of the manipulator
were connected by wire, and the response time was less than
100 milliseconds. This delay period was short enough for theinteraction to seem natural. The figure (right) shows a scenefrom the experiment.
A. Result of the Preliminary Experiment
After 10 minutes of experimenting, we interviewed eachparticipant while they watched the recorded video of theirexperiment. All users eventually understood that the robot
wanted to them to move the target into the box. The result
suggests that the humans could interpret intentions even if thecommunication method is restricted to two-axis actuators.
The qualitative answers about the meaning of the movements
are summarized as follows.
• An effective negative response (shaking) consisted ofshaking the entire robot box left and right.
• An effective positive response to the user consisted ofmoving the box up and down (nodding).
• The a directing motion toward the user helped to indicate
front side of the object. The nodding motion also helped toindicate the front side of the object.
• Directing the upper side of the box toward the target was
useful for informing the user to move the target into thebox.

II. PRELIMINARY EXPERIMENT: HUMAN-HUMAN
INTERACTION VIA TWO-AXIS ACTUATORSThe experiment involved two participants, a manipulatorof the robot and a user of the robot. Two participants tookturns acting as the manipulator (one female and one male).
The experimenter gave the manipulator a controller for two
actuators and instructed her/him on how to manipulate the

Figure 1. Setup of the preliminary experiment329

Figure 3. Front, functional side, and behaviors of the robot• Aiming the front side at the user displays the robot’s
attention toward the user.
• Aiming the functional side at the user suggests that the user
use the function of the robot.
We show each gesture on the Fig. 3 right.
Regulative behaviors were as follows.
• Repeating an action sequence helps a user to understand therules from the motions and intention in the behaviors.
• Interruption during an action helps the user to imagine thenext action that will be performed by the robot. This
motion also supports user's understanding about the robot's
requirements.
We found that the same behaviors did not always mean thesame intention. Some behaviors are time restrictive. For
example, a robot's moves will attract the attention of a user
during a period from the start of the user's appearance andbefore user's attention toward the robot. Directing behaviors
are understood once the user understands the front side of therobot. These must be conducted after the front side isunderstood. Nodding and shaking are more time restrictive.
The shaking motion suggests the user should stop conducting
an action and or that he or she conducted an action just after
the finish time. These motions are understandable after theactions. These findings suggest that intentional gestures
behave like nouns and verbs in the interaction, and regulative
gestures control the syntax of the gestures.

Figure 2. The robot box with two-axis actuator• Users sometimes continued observing the robot even when
they understood that the robot box wanted them to movethe object into the box. The resulting misunderstanding
required a waiting behavior to the user's turn taking.
III. MODEL OF THE INTERACTION

This chapter discusses what kind of model is required tofacilitate a successful interaction between the user and therobot with previous result. The model includes shape,
behaviors, and interaction phases explained as follows.
A. Shape of the Robot: Determination of the Front Side andFunctional SideThe front side of the box was used to inform the user about
the intentional direction of the robot (Fig. 3 left). The robot
requires a front side to convey its intention to the user. The
front side is determined by the robot’s appearance and it
behaviors.
In regard to the robot box, the front side is entirely
determined by its behaviors, because the box itself iscentrosymmetric. Some participants did not initially
understand which side of the box was the front. Manipulators
also did not initially choose the front side of the box.
However, when it moved up and down (pitched up or down),
the user and the manipulator were able to understand that thepitch direction indicated the front side of the interaction.
This finding can be generalized as follows. It is easy todetermine the front side of the object if the object has adistinctive shape and texture to determine the front side.
However, if there is no distinctive information to determine
the front side of the object in it, the user determines the front
side by movements. Simple spheres, cylinders, boxes, andcones need more information to determine a front side.
On the other hand, determining the functional side is easy
for the user. The robot box has one open side to load or unload
objects. Directing the opening side towards the objects was
easily understood as the robot wanting to interact with anenvironmental object.

C. Interaction Phases of the Robot
We divided the interaction phase into three parts(Initialization phase, Observation phase, and Action phase),based on the findings of the preliminary experiments. The
next three paragraphs describe how the robot and user act ineach phase. Figure 4 shows the kinds of interaction in thethree phases above.
The initialization phase is for the robot to show the userthat it has an intention toward her/him. This is done by theuser moving and the robot moving according to the user's
actions. To show that the robot is an intentional agent, it
should keep its front side toward the user. The user also needs
to determine which side is the front of the object. Once theuser understands that the robot has an intention and direction,
the robot can proceed to the next state.
In the observation phase, the user pays attention to theobject and understands the information given by the robot.
Robot leads the user to use the functions of the robot. If afunction is used for a different object, the robot redirects theuser to the correct object. For example, if the robot instructs
the user to take a book from inside itself, there is no need for it
to use a directing motion. However, if the robot wants the user

B. Behaviors of the Robot
We categorized the robot's behaviors in the preliminary
experiments as intentional gestures or regulative behaviors.
Intentional gestures are:• Nodding and shaking respectively mean affirmation anddenial.
• Aiming the front side at the target suggests the direction of
the intention.
330

to move a book outside, it requires a directing behavior. At
the end of each behavior, the robot faces the function side
toward the user. This means the user should proceed to thenext phase. If there is no action from the user, the robot
repeats the above behavior.
In action phase, the user acts according to the robot's
intention and observes the robot to get information about
whether the action is right or wrong. The robot checks andwaits for the user's behavior. When the user approaches anobject, as instructed by the robot, the robot aims thefunctional side at the user. On the other hand, if the user
moved differently from the robot's intention, the robot
produces a negative gesture to prevent an incorrect action.
When the user finishes the appropriate action, he or she
finishes her/his turn and the system moves to the observation
phase.

(Fig. 5B). The robot uses nodding to indicate the front side
(Fig. 5C). The system moves to the next phase after fiveseconds of gazing by the user.
In state 2, the user observes the motion of the robot. The
robot alternately aims its front side at the object and its
functional side at the user's head (Fig. 5D and 5E). Aiming
the front side at the object implicates the object as a target of
the interaction. Aiming the functional side at the user
implicates use of the functional side of the robot. In this way,
the user learns the robot's intention regarding the object.
When the robot points the functional side at the user's head, it
waits three seconds to lead the user to the next phase. The
system goes to the next phase when the user approaches theobject.
In state 3, the system observes the behavior of the user anddetermines whether it is right or wrong. If the user takes thewrong object (Fig. 5F), the robot shakes (Fig. 5G). Thisimplies that the user did not understand the right object topick up. When the user stops moving toward the object, thesystem repeats state 2 and instructs the user about the rightobject. When the user takes the correct object (Fig. 5H), therobot aims the functional side at the user and waits for theuser's next behavior (Fig. 5I).

IV. IMPLEMENTATION OF THE AUTOMATIC INTERACTIONSYSTEM
We explain the automatic interaction system that embodies
the interaction models of section 4. The user interacted withher/his head and hands to interact with the robot box. The
system is controlled without any human manipulator.
This system used the positions of the head and hands forthe interaction, as detected by a motion capture system. The
user attached three or more markers to her/his head and both
hands. The sensors detected two specific actions (touching
the object and approaching the robot box). The system
calculated the difference in the robot's direction and the user's
head direction. If the difference was less than 45 degrees, thesystem estimated that the user faced toward the robot. Whenthe hand was within 150 mm of the object, the system
considered that the user was touching the object.
We also detected the social distance of the robot and theuser in terms of the distance between the midpoint of theuser's head and the center of the robot. We used the center of
the two markers on the user's head as the position of the user.
The system detected that the user was approaching the robot
box when the head was 500 mm from the robot. We decided
this distance according to the result of the preliminary
experiment.
Examples of the behaviors are shown in Fig. 5. The
position markers are attached as Fig. 5A. In state 1, the robot
attracts the user and directs its front toward the user's position

V. APPLICABILITY OF THE INTERACTION MODEL

We evaluated the automatic interaction system. We placed
three objects 1 meter from the box. The robot box tried to
instruct the participant to move one of the objects into thebox.
Ten people (two females and eight males, from 22 to 24
years of age) participated in the experiment. We did not givethem any prior instruction on what would happen. Eachparticipant waited the outside, and the experimenter made anannouncement for him or her to enter the room. The robot
stopped and the experiment finished when the participant
moved the object into the box. When the participant could not

Figure 5. Examples of behaviors automatically generated

Figure 4. Three phases on the interaction
331understand the intention of the box and started to observe
other objects on the room, or her/he touched the object but left
it where it was instead of putting it into the box, theexperiment also finished as failure.

represents whether it is manipulated automatically or
manually. The score represents each sequence's believability.
For example, if the user strongly believe that the behavior iscontrolled by a human, the user gave a +3 point score. On theother hand, if the user fairly uncertainly believe that the
behavior is controlled by computer, the user gave a -1 pointscore. When the experiment finishes, the user get an reward
according to their scores. Scores are calculated bymultiplying an absolute value in believability (1 to 3) with theright/wrong (+1/-1) answers. One point score counted as 75
yen (one dollar). If a total score became minus, the user did
not get any reward from the experiment. This experimental
setting motivates each user to estimate each sequence's
believability seriously.
Fifteen people (three females and 12 males) participated in
the experiment. Their ages are from 20 to 70. The robot
stopped and the experiment finished when the participant
placed the object in the box. We collected 45 sequences.
Twenty-four manual conditions and 21 automatic conditions
are totally assigned in these sequences.

A. Result and Discussion
All participants put some objects in the box. We observed
the behaviors of the participants via a hidden camera. We
categorized four levels of success and counted how many
participants succeeded.
A) The participant touched the object targeted by the robot
box as their first action (7 participants).
B) The participant moved the target object into the box as
their first action (7 participants).
C) The participant moved the target object into the box atonce during the experiment (9 participants).
D) The participant moved the target and kept it into the box
until the end of the evaluation (8 participants).
Shaking (denial) motions occurred three times during theexperiment. Two participants stopped their actions because ofthem. Two participants took two objects and showed them tothe front side of the box.
80% of the participants succeeded in completing theexperiment (D). This result indicates that our three-stateinteraction model worked. It also suggests that simplified
interactions with two-axis actuators are enough for leadingusers.

A. Result and Discussion
All participants put correct objects in the box according to
the instruction from the box. In the manual condition, there
are 12 correct answers and 12 incorrect answers. In thecomputer sequence, there are 9 correct answers and 12
incorrect answers (shown in table 1 left). We applied
Pearson's chi-square test to these data and a p-value is 0.93.

VI. EXPERIMENT: BEHAVIORAL TURING TEST USING

TWO-AXIS ACTUATORS

Answered as

We conducted the behavioral Turing test using our modelin the demonstration booth of our lab on the workshop. We
placed the box based robot and three type of color cases on
the ground of our booth. We placed three objects 1 meterfrom the box.
Each user tried three trial sequences. Automatic condition
and manual condition is randomly assigned in these three
sequences. In automatic condition, the robot box is controlled
by the program. In manual condition, the robot box iscontrolled by a human operator who is hidden from the users.
There are two human manipulators in the test. Eachmanipulator is randomly assigned for each experimental case.
The manipulators in this experiment are different fromprevious manipulators in the preliminary experiment. They
are forced to instruct the participant to move one of theobjects into the box just by the robot box's behaviors. A user
equipped a cap and gloves with motion capture markers. The
user did not know which condition is selected in eachsequence.
The user selects two of color boxes and put it in the robot
box according to the instruction from the robot box in eachsequence. We requested the user to judge whether the robot
box is controlled by a human or a computer in the eachsequence. The user answered whether the box is controlled bya human manipulator or a computer using 6 Likert scales after
the sequence. A positive (+1 to +3) or negative (-1 to -3) score

Human

Computer

Believability

1

2

3

Right

12

9

right

10

5

6

Wrong

12

12

wrong

9

6

9

Table 1. Validity in manual and automatic cases(left),and Relationship between believability and answers (right)

Table 1 right shows relationship between believability foreach answer and its result. These results suggest that eachbelievability is not related to whether it is correct or not. The
average score of believability multiplied by right/wrong
answers (+1/-1) is -0.13 (SD = 2.27) in manual condition, and-0.33 (SD = 2.00) in automatic condition. We applied t-test
for these scores and the p-value is 0.74.
All participants succeeded in completing the task to collectobjects. This result indicates that our three-state interaction
model could instruct all the users to put objects in the robot
box. The p-values show that the statistical method cannot
distinguish user's answers from random answers because bothp-values are more than 0.70. Because statistical analysis
method cannot directly confirm the similarity of two groups,
there is left a possibility that the data is not enough for finding
a difference. However, this result roughly indicates thatbehavioral Turing test with two-axis actuators was successful.
B. Contribution and Limitation
The result of evaluation and experiment show that ourinteraction model for two-axis actuators could convey the
332

[3]

Hayes, P., and Ford, K. M. 1995. Turing test Considered Harmful. In
Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, 972-977. Menlo Park, Calif.: International Joint
Conferences on Artificial Intelligence, Inc.
[4] Fong, T. W. 2003. A survey of social interactive robots, Robotics andAutonomous Systems 42, 143-166.
[5] Fujita, M. 2004. On activating human communications with pet-type
robot AIBO, Proceedings of the IEEE, 92(11), 1804–1813.
[6] Shibata, T., Mitsui, T., Wada, K., and Tanie, K. 2002. Subjective
Evaluation of Seal Robot: Paro - Tabulation and Analysis of
Questionnaire Results -. Journal of Robotics and Mechatronics, 14(1),13-19.

intention of the robot to the user, and also indiscernible fromthe behavior controlled by human operator.
The most important contribution of our study is to show an
running behavioral Turing test in interactive and real-world
based application. We think that our success is supported byminimization of the task between agents and simplified
interaction channel. The model is generalized and separately
applicable. For example, if we apply our model to two
actuators in humanoid's head, the humanoid could lead a user
to pick up object as same as human's control, even if otherbehaviors are not enough to achieve Turing test.
Simplified task and channels could engrave acceptable
handrail toward steep mountain in Turing test. Our result
propose another view to the Hayes's proposition that Turing
test did not created fruitful goal in artificial intelligence field(Hayes et al. 1995). If we selected limited task and limited
communication channel, we can set appropriate engineering
goal with Turing test approach.
VII. CONCLUSION

We proposed a variation of the Turing test that involves a
restricted communication method. This modified test uses
two-axis actuators for communication instead of natural
language dialogue. This reduction of scope reveals whatkinds of features are essential for an imitation game. Thisapproach has the potential to evaluate several kinds ofinterfaces because two-axis actuators are used in many
real-world applications.
First, we tried to determine what elements are critical forcommunication between a user and a robot through apreliminary
experiment
involving
human-humancommunication. A human manipulator received a video
image as input and controlled a "robot box" in a way thatwould lead a user to put objects into the box. The results
revealed gestures, regulative behaviors, and three distinctive
phases in the interaction that are used to make intentions clear.
Second, we created an automatic interaction systemembodying the results of the models. The behavior of therobot was programmed using the user’s head and handlocations, as identified by a motion capture system. Third, we
conducted Turing test in a communication task whereby thehuman collected items according to the instructions of therobot box. In this test, two actuators on the box is controlled
both by human manipulator and our program. The answers ofusers suggests that the users could not identify which iscontrolled human or computer. This result indicates that
Turing test is achieved in restricted behavioral level.
ACKNOWLEDGMENT
This work was supported by the JST PRESTO program.
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