The final publication is available at
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Alignment Approach Comparison between Implicit andExplicit Suggestions in Object Reference Conversations
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ABSTRACTThe recognition of an indicated object by an interacting
person is an essential function for a robot that acts in daily
environments. To improve recognition accuracy, clarifying
the goal of the indicating behaviors is needed. For thispurpose, we experimentally compared two kinds ofinteraction strategies: a robot that explicitly provides
instructions to people about how to refer to objects or arobot that implicitly aligns with the people’s indicating
behaviors. Even though our results showed that participantsevaluated the implicit approach to be more natural than the
explicit approach, the recognition performances of the two
approaches were not significantly different.
Author Keywords

Human-robot interaction; object recognition; alignment
ACM Classification Keywords

H.5.2. User Interfaces – Interaction styles.
INTRODUCTION

Social robots in human society need to recognize theobjects indicated by users (Figure 1). Various approaches
have been proposed to recognize such objects based on user
utterances and pointing gestures [1,2,3,4]. Nickel et al. usedthe 3D positions of a head and hands as well as the head’sorientation to recognize pointing gestures in objectreferences [1]. Schauerte et al. integrated speech andpointing gesture recognition by image processing [2].

Even though such techniques improve the sensing
capabilities of robots, recognizing the objects indicated byusers remains difficult because user references are often
ambiguous during conversations. People enjoy enormous
variability in their lexical choices in conversations [5]. Suchvariability degrades the recognition performance because
they might not always use the words contained in a
database that stores an object’s characteristics, and theyalso do not always use enough words to identify an object[6]. Even if robots can perfectly recognize speech orpointing gestures, they might not distinguish an object
indicated by humans from other objects.

How does a robot encourage a user to clarify his
references? We consider two approaches: explicit andimplicit. In the first approach, a robot explicitly instructs
the person how to refer to objects. We call this the explicit
instruction approach. For example, the robot explicitly asks
a user, “Please describe the object’s name, color, and size
when pointing at it.” The ambiguity of the user’s references
is expected to decrease if she accepts that request.

In the other approach, the robots implicitly aligns with thepeople’s indicating behaviors. We call this the implicit
alignment approach. For example, the robot implicitly
elicits pointing gestures and lexical expressions containedin the robot’s database from a user. When a person istalking with an addressee, she tends to repeat the same
lexical [7], syntax [8], expression choice [9], and body
movements [10,11] as the addressee. This phenomenon,
called alignment, occurs in interactions not only between
humans but also between a human and artificial media like
spoken dialogue systems [13,14,15,16,17] and robots[18,19]. Through alignment, humans narrow down huge
lexical choices and elicit terms, indications, or iconic
gestures to naturally identify objects for their interlocutors.
Inspired by these alignment findings, some research
proposed robotic system that aimed to improve therecognition performance of objects indicated by a user by
eliciting the user’s references by alignment [20].
However, it remains unknown which approach more
effectively decreases ambiguity and improves performance.

Figure 1. Robot recognizes an object indicated by a user

The final publication is available at
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Moreover, social robots should choose a more appropriate
approach by considering not only the recognition
performance of the indicated objects but also the user’s
impression of the interaction. Even though the performancemight be increased by either approach, such performancegain becomes worthless if the users hesitate to interact witha robot by the chosen approach and vice versa.
This paper addresses whether the explicit instruction
approach or the implicit alignment approach is better forobject recognition contexts in conversations with people.
We developed a robot system that recognizes objects
indicated by a user and experimentally compared the two
approaches with our system. Based on the experimentresults, we discuss the effectiveness of the approaches.
INTERACTION DESIGN

Object Reference Conversation

To investigate the effect of these two approaches on objectreference recognition, we used an interaction called objectreference conversations (Figure 2). Such conversations
focus on confirmation behavior, which is often observed inhuman-human communication. For example, when person
A asks person B to bring a magazine, she is referring to a
specific magazine: “Bring that magazine to me.” If person
B cannot confidently understand which magazine was beingreferenced, she is likely to ask for confirmation: “This one?”
Such conversations are already being used in human-robot
interaction research fields to explore several research
purposes, including lexical entrainment in human-robot
interaction [18] and the implementation of the implicit
alignment of reference behaviors [20].
The following are the details of object reference
conversations. First, a robot asks a person to refer to an
object in an environment where several objects are arranged(Ask). Next, she refers to an object (Refer), and the robot
confirms the object to which she referred (Confirm). Then
she answers whether the object confirmed by the robot iscorrect (Answer).

Explicit Instruction Approach

Explicit instruction requires the information that the robot
wants the interlocutors to use for recognizing the indicatedobject, because it limits the references and reduces
unexpected references. If an interlocutor refers to an object
as the robot instructed, the robot will probably recognize it

Figure 2. Object reference conversation: white and blackboxes denote robot and human turns

Figure 3. Example of explicit robot instructions: robot
explicitly instructs how to refer to objects by encouraging
interlocutor to use information that was missing inprevious referenceswith high performance. A robot should also give
instructions about how to refer to objects in such a way thatasks an interlocutor to make a reference that includes asmuch information as possible that the robot can recognize.
If the robot’s recognition fails partly because of noise,insufficient speech volume, unclear pointing gestures, andso on, references that include sufficient information
increase the chances that the robot will correctly recognize
the referenced object.
In addition, if the interlocutor does not follow the robot
instructions, the robot should request that she use all of the
instructed information for the object references. Thissuggestion reminds the interlocutor of the instructions andencourages her to use all of the information in subsequentconversations.

Based on these considerations, we designed the explicit
instructions of the reference behavior as follows. A robot
provides instructions about how to refer to objects in a waythat asks the interlocutors to make a reference that includes
as much information as possible and requests that theinterlocutor use the information that was missing from theprevious references. Figure 3 shows an example of explicit
instructions from a robot.
Implicit Alignment Approach

We adopted the implicit alignment approach proposed by
Kimoto et al. [20]. This approach exploits alignment inobject reference conversations. Based on these threealignment phenomena, lexical alignment, gesturalalignment, and alignment inhibition, they designed robot
behavior where the robot should use minimum information
for distinguishing among objects in the environment.
Alignment inhibition is the phenomenon of alignment
becoming substandard in conversations in some cases.
Through this design, humans learn to use references that
include enough information to identify the objects by
reducing the alignment inhibitions. They implemented thisdesign in the Confirm part in the object reference
conversation described in the previous section. Theirexperimental results suggested the possibility of improving

The final publication is available at
http://dl.acm.org/citation.cfm?id=2974814

Figure 5. System architecture to recognize objects indicated by user

indicated object and asks an interlocutor to refer to it in thenext conversation in a way decided by the robot behavior
control function.

The system can also have object reference conversations as
a basic function. In its Ask and Confirm parts, the robot
performs a behavior that corresponds to which approach isused by the robot.
Figure 4. Example of object reference conversation with
implicit alignment approach: robot confirmed indicatedobject using minimum information for distinguishingobjects

the recognition performance by the implicit alignment ofreference behavior. However, even though they discussed
the effectiveness of reference behavior, they didn’t evaluateuser’s impression of the interaction.

Therefore, we used this design of robot behavior as animplicit alignment of reference behavior; a robot shouldmake confirmations that contain minimum information for
distinguishing objects. Figure 4 shows an example of an
object reference conversation with an implicit alignment
approach.
SYSTEM

Figure 5 shows the architecture of our developed systemthat recognizes the objects indicated by a user. We
developed it by referring to past work that implemented theimplicit alignment of reference behaviors for objectreference conversations [20]. The system consists of fiveparts: sensors, an object position detection function, an
indicated object recognition function, an object information
database, and a robot behavior control function. First, thesystem detects the positions of the objects arranged in theenvironment and saves them in the object information
database. When a user refers to an object, the indicatedobject recognition function recognizes the interlocutor’sreference behavior and estimates the indicated object. The
robot behavior control function chooses a robot behavior
that corresponds to the implemented approach and sends a
behavior command to the robot. The robot confirms the

Robot

In this study, we used Robovie-R ver.2, which is ahumanoid robot developed by the Intelligent Robotics andCommunication Labs, ATR, that has a human-like upper
body designed for communication with humans. The
speaker in its mouth can output recorded sound files fromthe internally-controlled PC located in its body. We used
XIMERA for speech synthesis [21]. It has three DOFs for
its neck and four DOFs for each arm. Its body has sufficient
expressive ability for object reference conversations. It is1100 mm tall, 560 mm wide, 500 mm deep, and weighs
about 57 kg.
Object Position Detection Function

The object position detection function locates the positionsof the objects arranged in the environment through ARmarkers and saves its ID and positions in the object
information database. The AR-markers are read using an
RGB camera in the ceiling. We previously saved theattributes of the objects in the database, and the system canextract the attributes of objects from it by IDs.
Indicated Object Recognition Function

To develop this function, we implemented an algorithm[20] that combines the results of speech recognition,
pointing gesture recognition, and face direction recognition.
Note that this past work [20] only used the speech andpointing gesture recognition results; in this work we added
face direction features to increase the performance of theobject reference recognition.
Speech Recognition

The speech recognition module receives human speech thatrefers to an object and outputs the normalized reference
likelihood of each object based on speech recognition. To

The final publication is available at
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calculate the likelihood, we used a previously proposed
method [20] that uses the number of attributes in the humanspeech, which is captured by a microphone attached to thehuman’s collar. In this system, we used a speech
recognition engine called Julius, which gives good
performance in Japanese [22].
Pointing Gesture Recognition

The pointing gesture recognition module obtains the body
frame data from a depth image sensor called Kinect for
Windows v2 and outputs the normalized reference
likelihood of each object based on pointing gesturerecognition. We modeled the likelihood as the difference
from the pointing vector (between the human head and thetip of the human hand) to a vector between the human head
and an object with a normal distribution function N(0, 1).
Face Direction Recognition

The face direction recognition module obtains the face
direction vector from the depth image sensor and outputs
the reference likelihood based on the face direction
recognition. We modeled the likelihood based on an angle
parallel to the plane of the floor between the face direction
vector and a vector between a human head and an object. If
the vector is less than 110 degrees, the person is considered
to be viewing the object; its likelihood is 1, otherwise 0.
This is because a human’s field of view is 110 degrees atmost [23]. The likelihoods are finally normalized from 0 to
1.
Integration

The integration module merges the reference likelihoods ofthe speech and both the pointing gesture and face direction
recognitions. These three likelihoods are summed andnormalized based on previous work [20]. The object withthe highest likelihood is estimated to be the object indicatedby the interlocutor.
Robot Behavior Control Function

The robot behavior control function determines how the
robot confirms the indicated object (Confirm behavior) and
how it asks an interlocutor to refer to an object (Askbehavior) in subsequent conversations. The conversation
contents of the Confirm and Ask behaviors reflect whether
the explicit instruction approach or the implicit alignment
approach is used.

When using the explicit instruction approach, this function
chooses the Ask behavior and adopts the explicit instruction
approach, and the robot explicitly provides instructions
about how to refer to objects. The Confirm behavior does
not adopt a particular approach, and the robot confirms theindicated object by pointing and verifying all of theinformation about the indicated object.

When using the implicit alignment approach, this function
chooses the Confirm behavior and adopts the implicit

alignment approach, and the robot confirms the indicatedobject with minimum information for distinguishing among
objects. The Ask behavior does not adopt a particularapproach, and the robot does not explicitly instruct how tomake references.
EXPERIMENT

We experimentally compared the two interactive
approaches: explicit instruction and implicit alignment.
Hypotheses and Predictions

If the robot explicitly instructs a particular reference way,
the interlocutor knows how to refer to an object and may
use it in object reference conversation. This enables therobot to recognize the indicated objects more accurately.
However, referencing to an object based on explicit
instruction is not common in daily conversations, and so theinterlocutor might deem the conversation unnatural.
Similarly, the interlocutor might feel the conversation is aload and troublesome because of the unaccustomed
conversations.
On the other hand, if there is no explicit instruction about
the reference way, the interlocutor might not know how torefer to an object, complicating the robot’s ability torecognize indicated objects. However, the interlocutor
might feel the conversation is more natural than explicit
instructions. Based on these considerations, we made thefollowing two predictions:

Prediction 1: Conversations with the implicit alignment
approach will be perceived as a less load, less troublesome,and more natural by the interlocutor than conversations
with the explicit instruction approach.
Prediction 2: Object reference recognition performance will
outperform the conversations with the explicit instruction
approach than conversations with the implicit alignment
approach.
Conditions

We controlled an approach that was applied to ourdeveloped system (applied approach factor).

The applied approach factor had two levels: explicit
instruction and implicit alignment. Both were respectively
applied to the Ask and Confirm parts of the object reference
conversation described in the interaction design section.
The applied approach factor was a within-participant
condition.
Explicit Instruction Condition

In the explicit instruction condition, the robot gives
instructions about how to refer to objects in a way that asksinterlocutors to make a reference that includes as muchinformation as possible with comments that encourage theinterlocutor to use the information missing in the Ask part.

The final publication is available at
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Figure 6. Experimental environment

The speech format of the explicit instructions includes two
sentences. The first sentence is used every time; the second
sentence is only used when a participant did not use all of
the information requested by the first sentence in theprevious conversation.
For example, the robot says, “Can you refer to a book using
its color, a shape on its cover, a letter on its cover as well as
by pointing and looking at it? Please refer to a letter andpoint.”In the Confirm part of this condition, since the robot
confirmed the objects with all of the information, it gave
every attribute of an object and pointed during theconfirmations.
Implicit Alignment Condition

In the implicit alignment condition, unlike the explicit
instruction condition, the robot does not explicitly provide
instructions about the reference way; it just says, “Pleasechoose a book” in the Ask part.

On the other hand, in the Confirm part the robot utters a
different sentence. For this purpose, we implemented an
implicit alignment design for the reference behavior. In thiscondition, the robot confirmed the object with minimum
information for distinguishing among objects; theconfirmations were based on the implicit alignment
approach of references proposed by Kimoto et al. [20]. Thisapproach determines robot’s object reference behaviors, i.e.,
pointing behavior and speech, by considering objects’
position relationships and characteristics. A robot used
pointing gesture when it would be useful to decrease
candidates of referenced objects. For example, if robot’s
pointing gesture would become vague to an interlocutor, therobot did not use the gesture.
The speech format of the confirmations is the sequence of
the attributes of objects. For example, the robot says, “Thatblue book with a circle on its cover?” or “That yellow
book?”
Environment

Figure 6 shows the environment. The participants wereseated in front of the robot. Five objects were placed in a
1.5 m by 3.3 m rectangular area between the robot and theparticipant. The books were grouped close together without
overlapping. These objects were approximately 0.6-2.6 mfrom the participants.

Figure 7. Example of book arrangement

We controlled the attributes of the books, all of which were
21 cm by 27.5 cm. Their attributes were color and the shapeand the letter on the cover. There were three colors: red,
blue, or yellow. Three shapes were placed on the bookcovers: a circle, a triangle, or a square. There were twoletters: Q and B. We prepared 18 books to satisfy all
combinations of the attributes.
Procedure

We conducted our experiment as follows. First, we
explained it to participants who signed consent forms. Next,
we gave them the following oral instructions: “The robot
can recognize human speech, pointing gestures, and face
direction. The robot will ask you to indicate a book.
Indicate it as if you were dealing with a person.”After the instructions, the participants selected five books
among the 18 and arranged them based on theexperimenter’s instruction: “Please arrange the books in
one place.” An example of the arrangement is shown in
Figure 7. After that the participant repeated the objectreference conversations ten times. We call this set of ten
object references the conversation sessions, which wereconducted in both applied approach conditions: explicit
instruction and implicit alignment. The participants
answered questionnaires about their impressions of theconversations after each conversation session. We
counterbalanced the order of the interactive approachconditions.
Participants

Twenty (ten females and ten males) native Japanese
speakers whose average age was 35.5 (SD = 9.9)
participated in our experiment.
Measurement

Impression of Conversations

To investigate the participant’s impressions of theconversations, we prepared the following threequestionnaire items and evaluated them on a 7-point scale:1.
2.

The conversation with the robot was a load (loadfeeling).

The conversation with the robot was troublesome(troublesome feeling).

*: p < .05 **: p < .01 ***: p < .001

7
6

*

5

***

**

4
3
2
1

Load feeling(7: most load)

Troublesome feeling(7: most troublesome)Explicit

Implicit

Natural feeling(7: most natural)

Figure 8. Impression of conversation (mean ± SE)

3.

The conversation with the robot was natural (natural
feeling).

Recognition Performance

The recognition performance is the success rate of theobject reference recognition. We calculated it from thenumber of object references correctly recognized by the
robot per conversation session, which was a set of ten
object reference recognitions.
RESULTS

Verification of Prediction 1

Figure 8 shows the results of the questionnaire items. To
verify the effect of each condition, we conducted a paired ttest for each of the questionnaire items. For the load feeling,
we found a significant difference among the conditions(t(19) = 2.1, p = .049, d = .72). For the troublesome feeling,
we found a significant difference among the conditions(t(19) = 4.2, p < .001, d = 1.1). For the natural feeling, we
also found a significant difference among the conditions(t(19) = -3.0, p = .008, d = .70). These results supported
Prediction 1.
Verification of Prediction 2

Figure 9 shows the recognition performance results. To
verify the effect of each condition, we conducted a paired ttest and found no significant difference between the twointeractive approach conditions (t(19) = -.58, p = .57, d= .18). This result indicated that Prediction 2 was notsupported.
DISCUSSIONInterpretation of Results

From the experiment results, the interlocutor impressions of
the conversations with the implicit alignment approachwere perceived to be significantly a less load, lesstroublesome, and more natural than the explicit instruction
approach. Accordingly, explicit instruction is not natural for
people and created feelings of uneasy interaction in theinterlocutors.

We identified no significant difference in the recognition
performances between the two approaches. This result can

Object recognition performance (%)

Average score of questionnaire items

The final publication is available at
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100%

90%
80%
70%
60%
50%
40%
30%
20%
10%

0%

Explicit

Implicit

Figure 9. Object recognition performance (mean ± SE)

be interpreted in two ways. First, the implicit alignment
approach improved the recognition performance at the samelevel as the explicit instruction approach. Although wecannot verify the effects of the implicit alignment approachin our experimental settings, a counter condition of an
implicit alignment approach is not a no-implicit alignment
approach but an explicit instruction approach, and theimplicit alignment approach probably improves therecognition performance, as argued by past work [20].

Second, the explicit instruction approach did not improve
the recognition performance very much. We predicted thatthe interlocutors would refer to an object as the robot
instructed; but in the experiment 28% of references in the
explicit instruction condition did not follow the robot’s
instructions. Some instructed with information that was
dropped out, for example, a pointing gesture and a color.
Explicit instruction is probably not an effective way to
induce interlocutors to encourage users to adopt clear but
recognizable references.

For these reasons, we conclude that the implicit alignment
approach is better than the explicit instruction approach for
object recognition contexts in conversations with people.
Our findings are useful for designing interactions for social
robots because good impressions of conversations areimportant for them because they often interact with people.
These findings can be integrated not only for object
recognition contexts but also for many other contexts since
determining whether to use an explicit or implicit approachis conceivable in other contexts.
Limitation

We conducted this experiment’s study in a limited situation.
The participants referred to objects with only three features:
color, a geometric shape, and a letter. In real environments,
the features of objects are not limited and obviously
influence the reference ways. But since the interaction way
between a robot and an interlocutor does not depend on
features, our findings are general for other objects.

The final publication is available at
http://dl.acm.org/citation.cfm?id=2974814

CONCLUSION

In this study, we focused on two interactive approaches forobject recognition contexts in conversations with people:
explicit instruction and implicit alignment. We developed asystem that recognized the indicated objects by integrating
the speech, pointing, and face direction recognition results
and experimentally compared the performance and feelingperspectives between the two interactive approaches.

Experimental results indicated that the participants
perceived the conversations with the implicit alignment
approach to be a less load, less troublesome, and morenatural than the explicit instruction approach. The overall
impression of the conversations with the implicit alignment
approach exceeded the explicit instruction approach. The
object reference recognition performance did not differ
between the two approaches, indicating that the implicit
alignment approach is better than the explicit instruction
approach for object recognition contexts in conversations
with people. We believe that our findings are useful for thedesign interaction of social robots that frequently interact
with people.
ACKNOWLEDGMENTS

Omitted for anonymized review.
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