How Close? A Model of Proximity Control for

How Close? A Model of Proximity Control for Information-presenting Robots Fumitaka Yamaoka1,2

Takayuki Kanda1

Hiroshi Ishiguro1,2

1

Norihiro Hagita1

2

ATR IRC Laboratories 2-2-2- Hikaridai Keihana Science City Kyoto 610-0228 Japan

Osaka University 2-1 Yamada-oka Suita Osaka 565-0871 Japan

[email protected], [email protected], [email protected], [email protected] ABSTRACT This paper describes a model for a robot to appropriately control its position when it presents information to a user. This capability is indispensable since in the future many robots will be functioning in daily situations as shopkeepers presenting products to customers or museum guides presenting information to visitors. Research in psychology suggests that adjust control their positions to establish a joint view toward a target object. Similarly, when a robot presents an object, it should stand at an appropriate position that considers the positions of both the listener and the object to optimize the listener’s field of view and establish a joint view. We observed human-human interaction situations where people presented objects and developed a model for an informationpresenting robot to appropriately adjust its position. Our model consists of four constraints for establishing O-space: (1) proximity to listener, (2) proximity to object, (3) listener’s field of view, and (4) presenter’s field of view. We also present an experimental evaluation of the effectiveness of our model.

Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User Interfaces-Interaction styles, I.2.9 [Artificial Intelligence]: Robotics

General Terms Design, Experimentation, Human Factors

Keywords Human-robot interaction, Communication robot

1.

INTRODUCTION

Our research focus is to develop a communication robot that naturally interacts with people and works in our daily lives, such as at museums, shopping malls, and so forth [1-3] (Figure 1). We believe that humanoid robots are suitable for communicating with humans. Their human-like bodies enable humans to intuitively understand their gestures, which cause us humans to unconsciousPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. HRI'07, March 9-1, 2007, Washington DC, USA. Copyright 2007 ACM 1-59593-294-1/06/0003...$5.00.

This laptop PC is suitable for your needs!

Fig.1

An information-presenting robot that stands at appropriate position

ly behave as if communicating with other humans. If a humanoid robot effectively uses its body, people will communicate naturally with it. This could allow robots to perform communicative tasks in our society such as presenting exhibitions or products. There are many research works in psychology about human positioning during conversation. For example, Hall discovered that human distance changes to match the intimacy of the communication [4]. Kendon found that when people talk about an object, they formulate O-space to establish a joint view toward the target object [5]. We believe that communication robots should also establish such spatial relationships as O-space when interacting with people. There are several studies about robots that control their position during human interaction. For example, Nakauchi and Simmons developed a robot that stands in a line like people by using a model of personal space learned from people’s personal space in a line [6]. Tasaki et al. utilized Hall’s proximity theory to determine a combination of sensors and robot’s behavior along with the current distance with the interacting person [7]. Dautenhahn et al. investigated how a robot should best approach a seated person and found that most subjects disliked a frontal approach and preferred to be approached from either the left or right side [8]. Gockley et al. investigated the best approaches for a person-following robot and found that direction-following behavior is more natural and human-like than path-following [9]. Pacchierotti et al. investigated the passing strategy of a robot in a small corridor [10]. Their result showed that entering people’s intimate sphere causes more anxiety. Koay et al. investigated participants’ preferences for the robot’s approach distance with respect to its approach direction and appearance. Their results show that participants’ preferences change over time as the participants habituate to the robot [11]. Overall, these studies revealed how a robot should control its position in relation to the interacting person.

In contrast, when HRI (Human robot interaction) includes conversation about objects, studies are scarce about robot positioning, in particular about controlling methods. Kuzuoka et al. studied the teleoperation interface of a guide robot and suggested that an information-presenting robot must heed body orientation toward people and exhibits [12]. Huettenrauch et al. found that Kendon’s F-formation arrangement, which occurred during a human user, indicates an object to a robot [13]. Although their observation reveals that humans formulate O-space toward a robot, they did not study how a robot should formulate its spatial relationship.

2.

MODELING OF INTERACTION

2.1. O-space constraints Kendon studied spatial arrangement during people’s conversations and found that O-space is established when two persons talk about an object. After O-space is established, they can look at the object together. He found that people formulate O-space in various ways, which are categorized into three types of spatial formation, called F-formation: Vis-à-vis, L-shape and Side-by-side (Figure 2). We believe that a robot that presents objects needs the capability to formulate O-space, we explored the constraints needed for the robot from observations of human interaction. Several possible constraints exist concerning the position of the listener and the target object. For example, a presenter needs to notice the distance to the listener and the target object. The presenter should also be concerned about both his/her own and the listener’s field of view because he/she needs to look at both the listener and object while providing information and listening. We decided to directly establish O-space rather than F-formation, since it is rather restricted by spatial arrangements of objects such as walls, shelves, and desks in a scenario where a robot would present an object. To summarize, there are four possible constrains:

(a) vis-a-vis

(b) L-shape

(c) side-by-side

Fig. 2 F-formation arrangement 2.5 meter

1. 7

2.5 meter

This paper reports the development of a robot that presents objects to people, and controls its position to the user and the object appropriately. Other issues in explaining scenes have already been studied such as pointing control [14-17] and gaze control [18, 19]. In contrast, it is largely unknown how a robot should control its position to maintain appropriate spatial relationships. Previous studies provide important insight for formulating O-space. In this study, we establish a communication robot model that stands at an appropriate position when presenting an object to a person; this is based on observation of human-human interaction about providing information on objects.

O-space

Presenter's initial pos.

Presenter's final pos.

me te r

Listener at pos. C

Listener at pos. B

45°

Listener at pos. A

45°

Target object

1 meter

Fig. 3 Analysis items

2.2. Assumptions and Simplifications We need to simplify the actual phenomena in human-human interaction because too many variables make the model too complex. Rather, it is important to find essential constraints needed for the spatial arrangement. Thus, we only focused on positions and body orientations when a person presents information on an object to another person. Once the model will be established, we believe that there are many possible directions of enhancement, such as to include three or more persons, two or more target objects, and more obstacles. It is interesting future work to integrate this model with models of other non-verbal interaction such as gaze [18, 19].

2.3. Observations of human behavior We observed the positions of presenters, listeners, and objects. 2.3.1.

Participants

Nine university students 2.3.2. Method of analysis We used a motion capturing system to accurately measure the position data. We analyzed five factors concerning the positional relationship between the presenter and others (Figure 3): (a) Distance to listener

C1: Should a presenter stand close to a listener?

(b) Distance to target object

C2: Should a presenter stand close to a target object?

(c) Listener’s field of view is between a vector from listener to object and a vector from listener to presenter

C3: Should a presenter and an object simultaneously be in the listener’s field of view? C4: Should the presenter simultaneously look at both the listener and object? In the section 2.3, we describe our process of observing humanhuman interaction situations and evaluating whether these constraints are valid and how we should parameterize and prioritize them to develop a robot that presents information.

(d) Presenter’s field of view is between a vector from presenter to object and a vector from presenter to listener (e) Presenter’s body orientation is between a frontal vector of presenter (direction presenter’s body is facing) and a vector from presenter to listener 2.3.3.

Method of observation

We created several situations in which participants presented an object to a listener. The experimenter played the role of the

2.5 meter

1. 7 Presenter's initial pos.

object

target object

Presenter's final pos.

me te r

Listener at pos. C

Presenter

P-(e) 2.5 meter

2.5 meter

2.5 meter

L: Listener P: Presenter

(a)Presenter hide object from listener's view object

P-(d)

Listener at pos. B

45°

Listener at pos. A

45°

L Target object

Listener

Listener

P-(c) P-(a)

Initial data: Listener's field of view: 0° Presenter's field of view: 180° Presenter's body orientation: 180°

P-(b)

Presenter

Initial data: Listener's field of view: 180°. Presenter's field of view: 0° Presenter's body orientation: 0°

(b)Listner hide object from presenter's view

1 meter

Fig. 4 Setting 1

Fig. 5 Setting 2

Fig. 6 Setting 3

listener. Participants were asked to move to a suitable position to perform their presentation from a preset position. The direction of the listener’s body faced the target object in all trials. The details of several situations are described in Sections 2.3.4 to 2.3.6. 2.3.4. Setting 1: When a listener stands near the object We tested whether a presenter simultaneously satisfies constraints C1 and C2 in the simplest setting: when a listener stands near the target object. We set three standing positions of a listener toward the target object (a laptop computer), as shown in Figure 4. The three standing positions are right-front, front, and left-front of the computer. Since the presenter’s initial position is 1.5 meters from the object, she/he needs to approach the listener from behind. We analyzed the presenter’s position after she/he approached and stopped at a certain point to perform his/her task. The analysis result indicates that presenters stood near the listener at an average distance of 1185.44 mm. Presenters stood close to the object as well. The average distance between presenters and objects was 1037.84 mm. These results show that the presenter’s position simultaneously satisfied constraints C1 and C2. Based on this finding, we established a simple model for the presenter’s position. Value ( Px ) = Listener ( Px ) + Object ( Px )



(1)

 1 (1100 mm < Dist . between Px and Listener < 1300 mm ) Listener ( Px ) =   0 (otherwise )  1 (1000 mm < Dist . between Px and Object < 1200 mm ) Object ( Px ) =   0 (otherwise )

Here, Px is a possible position for a presenter. Listener (Px ) is a function that indicates whether presenter’s position Px is near enough to the listener. Based on the average distance to a listener, we set 1200 mm as the optimal inter-personal distance and model that area with optimal inter-personal distance ± 100 mm with a value of 1. Object (Px ) is a function that indicates whether presenter’s position Px is close enough to an object. When a listener stands near an object, the presenter maintains a distance to it. Based on this average distance to an object, we set 1100 mm as the optimal distance to an object and model that area with optimal inter-personal distance±100 mm with a value of 1. Listener (Px ) and Object (Px ) are merged into Value (Px ) to indicate the appropriateness of position Px . Position Px with maximum value should be chosen as the optimal standing position for an information-presenting robot, as shown in Figure 7(a). 2.3.5.

Setting 2: Cases where listener stands far from object

Fig. 7 Example of robot’s standing position However, formula (1) was too simple. As shown in Figure 7(b), multiple different areas with maximum value exist when a listener is far from the object. We do not know whether the presenter should be more concerned about being close to either a listener or an object. Both cases exist. People, both presenters and listeners, sometimes talk about an object from a far place. In a store setting, to increase attention to an object, a shopkeeper sometimes attends to it, even though the listener is far from the object. Perhaps a presenter’s final position changes along with her/his initial position. To observe this, we set five initial presenter positions shown in Figures 5(a)-(e): (a) close to listener; (b) relatively close to listener; (c) middle of listener and object; (d) relatively close to object; (e) close to object. The presenter is asked to move to the position to provide information on a computer to a listener. The direction of the listener’s body faced the target object in all trials. Observation revealed that the initial presenter positions affected the final positions. When the initial presenter position was close or relatively close to a listener, the presenter provided information on the object near the listener. When the initial presenter position was close or relatively close to an object, the presenter presented the object near it. Since we could not determine whether the presenter should stand near a listener or an object when the initial presenter position is between the listener and an object, we simplified the model. Now the model reflects whether the initial presenter position is near a listener or an object to decide the final standing position for the presenter. Based on this approach, we modified formula (1).

θL should not be over 90°

P

L

θL

O

L

θE

L

O

θP should not be over 150° θ P : Presenter's field of view

θ L: Listener's field of view

(a) Listener's field of view

Fig. 8

Presenter's body orientation is θP×0.5

P

(b) Presenter's field of view

View constraints

Value ( Px ) = (Listener ( Px ) + Object ( Px ) )

Dist

( 2)

Dist = dist ( Px , P _ current )

Here, dist ( A, B ) is a function that calculates the distance between positions A and position B. Value (Px ) , which is a value of virtual standing position Px , is decreased based on the distance between Px and the presenter’s current position. Setting 3: When listener’s or presenter’s field of view is blocked In this observation, we observed whether the presenter’s position satisfies constraints C3 and C4 by intentionally arranging the initial position of the presenter to block either the listener’s field of view or the presenter’s field of view. Note that, technically speaking, even the listener does not look at the presenter (e.g., presenter stands behind the listener), or even the presenter does not look at both the listener and the object at the same time (e.g., standing side-by-side), when they talk about the object. Such scenes sometimes happen in reality. Our question is whether a presenter tries to avoid these situations even when constraints C1 and C2 are satisfied. 2.3.6.

Fig.9

Moreover, we analyzed the averages of the maximum presenter’s field of view throughout all trials from settings 1 to 3. Based on the results (Ave. 148.8º, Std. 17.6º), we set 150º as the limit of the presenter’s field of view. However, when it is over 90º, the presenter cannot look at both the listener and the object while facing the listener or the object. Throughout all trials in setting 1 to 3, we analyzed the value of the presenter’s field of view divided by the presenter’s body orientation. The average of the value was 0.57 with 0.18 standard deviation. This result shows that the presenter half faces the listener and the object. We defined the presenter’s body orientation as follows: Explainer ' s body orientation = Explainer ' s field of view × 0.5

Based on this, the presenter can see both the listener and an object. When the listener’s field of view exceeds the limit, the presenter cannot simultaneously look at both the listener and the object (Figure 8(b)).

2.4. Position model of a presenter To summarize the observation analysis, we established the following model for the robot to appropriately control its position: Value( Px) =

((Listener ( Px) + Object( Px) ) × L _ View( Px) × E _ View( Px) )

We set the initial position of the presenter as shown in Figure 6(a). In this case, a listener cannot see an object because her/his field of view is 0º. The presenter also cannot see the object because her/his field of view is 180º. Moreover, we set the initial position of the presenter for each object, as shown in Figure 6(b). In this case, a listener cannot look at the presenter because the listener’s field of view is 180º. The presenter cannot look at the listener because the presenter’s field of view is 0º. For situation (a), the presenter moves to the place that satisfies constraints C3 and C4. For situation (b), the presenter moves to the place that satisfies constraints C3 and C4. Based on these findings, we modified formula (2) as follows: Value( Px) = ((Listener( Px) + Object( Px)) × L _ View( Px) × E _ View( Px))  1 ( Listener' s field of view < 90 °) L _ View( Px) =   0 (otherwise)  1 ( Explainer' s field of view < 150 °) E _ View( Px) =   0 (otherwise)

The stand position based on the model

Dist  1 (1100mm < Dist. between Px and Listener < 1300mm ) Listener ( Px) =   0 (otherwise)  1 (1000mm < Dist. between Px and Object < 1200mm) Object( Px) =   0 (otherwise)  1 ( Listner ' s field of view < 90°) L _ View( Px) =   0 (otherwise)  1 ( Explainer' s field of view < 150°) E _ View( Px) =   0 (otherwise)

Position Px with maximum value must be chosen as the optimal standing position. Moreover, when the robot stands in the optimal standing position, the direction of the robot is the following formula: Explainer ' s body orientation = Explainer ' s field of view × 0.5

Dist (3)

We analyzed the averages of the maximum listener’s field of view throughout all trials from settings 1 to 3. Based on the results (Ave. 88.3º, Std. 11.8º), we set 90º as the limit of the listener’s field of view. When the listener’s angle exceeds the limit, the listener cannot simultaneously look at both the presenter and the object (Figure 8(a)).

Thus, when the presenter follows our model, it can control the spatial relationship as shown in Figure 9.

3.

IMPLEMENTATION

We implemented our proposed model for a humanoid robot. In this paper, we are interested in the robot’s capability for spatial arrangement. Meanwhile, robot’s recognition is often unstable in recognizing speech; thus, we adopted a WoZ (Wizard of Oz) method for speech recognition. We also used a motion capturing

system to stably recognize people’s position and body orientations.

Motion capturing system

"Target object" or "Utter" or "Listen"

target value

3D position

3.1. System configuration

Operator

Robot

Robot controller

Figure 10 shows the system configuration that consists of a humanoid robot, a motion capturing system, and a robot controller (software). We used a humanoid robot named “Robovie” that is characterized by its human-like body expressions [20]. Robovie’s body consists of eyes, a head, and arms that generate complex body movements required for communication. In addition, it has two wheels for movement. Markers are attached to its head and arms. The motion capturing system acquires body motions and outputs the position data of markers to the robot controller. Based on this data, the robot controller plans the robot’s behavior.

Recognition controller

State controller

Target Object Recog.

Present

Position Controller Present state Listen state Maintain direction module

Adjust stand position module

Fig.10

Voice Recog.

Listen

Gesture Did robot Controller arrive at target pos.? - Pointing

- Gazing etc.

Utterance Controller - Replay - Explain etc.

System configuration

3.2. Robot controller Figure 10 shows an outline of the robot controller that consists of five controllers: “recognition,” “state,” “utterance,” “gesture,” and “position.” In this paper, we are interested in the state where the robot automatically stands at the optimal position to present an object. On the other hand, it is difficult for the current robot to hear what the listener is asking. Thus, we adopted a semiautonomous system in the experiment’s next section. The operator teaches the target object to the robot controller when the listener asks the robot to present an object. The robot’s standing position and gestures are automatically performed. Recognition Controller 3.2.1. This controller recognizes the listener’s behavior, for example, which object the listener has a question about. However, it is difficult for the current robot to hear what the listener is asking. In this paper, we adopted a semi-autonomous system to focus on where a robot should stand to optimize its position for presenting an object. Instead of this controller, the operator teaches the target object to the robot controller when the listener asks the robot to present an object. Moreover, the operator gives the utterance to the robot controller based on the listener’s request. State Controller 3.2.2. This controller controls the state, which is either Present or Listen, based on the situation. In this paper, when the operator teaches the target object to the robot controller, the state changes from Listen to Present. When the operator gives a Listen command to the robot controller, the state changes from Present to Listen. Position Controller 3.2.3. The position controller consists of two modules: “adjust standing position” and “maintain the direction.” Based on the state, this controller chooses these modules. When the state is Present, this controller chooses the “adjust standing position module.” When the state is Present, this controller chooses the “maintain the direction module.”

Maintain direction module - This module makes the robot maintain the direction to the listener. Based on an order from this module, the robot faces the listener. Adjust standing position module - This module decides the optimal standing position for the information-presenting robot based on our proposed model. We set the search area for this module in Figure 11(a). This search area seeks the optimal standing position for the presenter. The position exists in this area be-

Fig. 11 Position Controller cause it should exist 1.1 m around the listener or 1.3 m around the object. The search area is divided by a grid as a possible 10 cm by 10 cm standing position. This module estimates the values all of the grids in the search area and selects the one with the highest value as the optimal standing position, as shown in Figure 11(a). This module decides a path for the calculated target position based on the positions of computers and the listener as obstacles. We set the obstacle area as 1.0 m around each obstacle and the search area for this module, as shown in Figure 11(b). This search area, which seeks the optimal standing position for the presenter, is divided by a grid into a possible 20 cm by 20 cm standing position. To search for a path, this module uses an A-star algorithm that is guaranteed to get the fastest path. An example of a path using this algorithm is shown in Figure 11(b). Finally, this module moves the robot based on the calculated path. 3.2.4.

Gesture Controller

The gesture controller manipulates the robot based on the state and the results of the position controller. When the state is Present, this controller makes the robot maintain eye contact with the lis-

tener and points at the object after the robot arrives at the target position (Figure 12). Because several researchers have already studied pointing control [14-17] and gaze control [18, 19] for presenting information, we might need to implement these findings to this controller in the future work. 3.2.5.

face dir.

Utterance Controller

4.1. Method 4.1.1. Participants There are twenty-two paid undergraduate students (13 men and 9 women with an average age of 21) participated in this experiment. They are not familiar with robotics.

Experimental condition 4.1.2. To verify the effectiveness of our proposed model, we set three conditions. In one, the information-presenting robot stands based on our proposed model. For comparison, we also prepared two other conditions where the robot only stands near the listener or near an object, since most existing robots only seem to care about the distance to the listener. ・

Near-Listener condition The robot only stands near the listener. In this condition, the search standing position module only uses the constraint of the distance to a listener. After arriving at the optimal position, the robot faces the listener.

・

O-Space condition The robot stands based on our proposed model.

・

Near-Object condition The robot only stands near the object. The search standing position module only uses the constraint of the distance to an object. After arriving at the optimal position, the robot faces the listener.

The experiment was a within-subject design, and the order of all experimental trials was counterbalanced. Every participant experienced in all three conditions. Experimental environment 4.1.3. The experiment was performed at ATR-IRC in a 7.5 [m]×10.0 [m] room. Due to the limitations of the motion capturing system, participants only interacted with the robot within a 3 [m]×3 [m] area. Four laptop computers were set in area shown in Figure 4. Procedure 4.1.4. In the situation the listener/customer (participants) enters the shop and the presenter/shopkeeper robot presents four laptop computers. To verify the effectiveness of the model in various

Target object

Presenter

Fig. 12 Gesture Controller

Target PC

EVALUATION

We conducted an experiment to verify that our proposed model based on observations of human-human interaction is useful for the information-presenting robot.

pointing dir.

Listener

For the experiment, we very simplified this function. That is, a human operator simply chose the sentences to utter from prepared candidates. When a system is prepared as fully autonomous, Utterance controller will be in complex form to optimize explanations to people. It should integrate the result from speech recognizer, previous conversation with the target person, and appropriate strategy to talk about products and exhibits depending on the target person.

4.

body dir.

(a) Setting 1: listener stands near PC

Target PC

(b) Setting 2: listener stands far from PC

Fig. 13 Experimental scene situations, we prepared two settings. In setting 1, the listener was instructed to listen and approach the computer while asking for an explanation (Figure 13(a)). In setting 2, the listener was instructed to listen far from a computer when asking for an explanation (Figure 13(b)). In setting 1, the listener moves in front of the computer about which he/she would like to ask a question. After arriving at the position, the participant asks the robot to provide an explanation of the product. In setting 2, the participant moves somewhere in the limited area that we set and asks the robot to present information on a computer far from his/her current standing position. The following procedure is identical in settings 1 and 2. The participant asks the robot “Please explain this/that computer” and points to it. Fallowing participant’s request, the robot begins to move to the target position based on each condition while giving a brief introduction of the computer, for example, “That computer is a new model from SONY.” After the robot arrives at the position, the participant asks about the characteristics of the computer and points to it. The robot presents the characteristics of the computer, for example, “One characteristic of this computer is high spec. If you want to play games, this computer is suitable for you.” After the explanation, the robot asks, “Would you like to hear about some other computers?” The participant moves to the next position and asks the robot about another computer. In this way, the participant requests and receives information on four different computers. Evaluation method 4.1.5. We administered a questionnaire to obtain participant impressions. Participants answered the following questions on a 1-to-7 scale, where 1 is the lowest and 7 is the highest:

・

Comfortable for speaking Were you comfortable with the robot’s standing position when it was speaking?

・

Comfortable for listening Were you comfortable with the robot’s standing position when you were listening?

・

Likable

Did you like the robot?

The “comfortable for speaking” and “comfortable for listening” questions measured how they felt speaking or listening to the

*

* +

*

7 6 5 4 3 2 1

+

* *

7 6 5 4 3 2

7 6 5 4 3 2 Near-Listener

Near-Object

O-Space

(a) Comfortable for speaking

1

Near-Listener Near-Object

O-Space

1

Near-Object

Near-Listener

(b) Comfortable for listening

O-Space

(b) Likable

Fig. 14 Experimental result (“*” denotes significant difference at p Near-Object (p < .05)

”Comfortable for talking” - We conducted a repeated-measure ANOVA. There was a significant difference among each condition (F (2, 42) =14.90, p < .01). Multiple comparisons with Bonferroni methods gave the following results: O-Space > Near-Listener (p < .05) O-Space > Near-Object (p < .05) Near-Object > Near-Listener (p < .1)

”Likable” - We conducted a repeated-measure ANOVA. There was a significant difference among each condition (F (2, 42) =4.64, p < .05). Multiple comparisons with Bonferroni methods gave the following results: O-Space > Near-Object (p < .05) O-Space > Near-Listener (p < .1)

“Which condition is the best?” - We conducted a chi-square test ( x 2 (2 ) = 14.000, p < 0.01). Multiple comparisons with Ryan methods gave the following results: O-Space > Near-Listener (p < .05)

Number of choice

4

17

1

O-Space > Near-Object (p < .05) Participants gave the highest evaluation to the O-Space condition concerning “comfortable for speaking” and “comfortable for listening.” Moreover, they gave higher evaluations to the O-Space condition than the Near-Object concerning “likable.” Because there was marginally significant difference between the O-Space and Near-Listener conditions concerning “likable,” we found that that the participant marginally liked the O-Space condition better than the Near-Listener condition. We believe that the participants did prefer the O-Space condition better than the Near-Listener condition because most participants chose the O-Space condition as the best condition. Based on these results, we verified our prediction that the participants will feel that the O-space condition is most comfortable for speaking and listening and more likable in the three conditions.

5.

DISCUSSION AND CONCLUSION

5.1. Summary of findings Based on our observation of human-human interaction situations, we found that it is important for a presenter to ensure the listener’s and presenter’s field of view. We established a model for information-presenting robots to appropriately control adjust their position. The model consists of four constraints for establishing O-space: proximity to listener, proximity to object, listener’s field of view, and presenter’s field of view. We implemented our mode for a humanoid robot and verified its effectiveness with experimental results. We found that an information-presenting robot using our model presents an object better.

5.1. Limitations We have only tested this system with a humanoid robot, Robovie; thus, the findings may not apply to other robots. We do believe, however, that similar behavior will result even using other robots, because the experimental method is mostly independent of Robovie’s appearance, except that it has an anthropomorphic head and arms. Robovie has a relatively simple appearance compared with other humanoid robots, such as Asimo [21]. In nonverbal interaction, Kanda et al. demonstrated that people’s responses were similar for different humanoid robots or humans [22]. Thus, we believe that people will behave in a similar way even if we use a humanoid robot with a different appearance, which will result in similar trends of impressions. We might de-

velop a robot whose size is different from Robovie’s. Some participants did not care even though the robot hides the object from them because Robovie is much shorter than most humans. A person may not be concerned if a short robot stands in front of the object.

5.2. Future work Because we simplified the position model for our experiment, one future work is to improve the model for daily realistic settings. For example, the constraint of distance to listener must be improved because the interpersonal distance of the constraint is set as a constant value. Many previous studies revealed that the robot must adjust its distance to humans based on its personality or the situation. The interpersonal distance should be adjusted based on the partner’s situation and so forth. A gaze model is also important for an information-presenting robot. In this experiment, the robot always maintained eye contact with the listener. However, humans look at target objects as well as the listener based on the situation. Kuno et al. suggest that a robot’s head movement encourages museum visitors to interact with the robot [20]. Thus, our future works also include controlling the robot’s eye gaze, which is now enabled by our model as the robot stands at a position to look at both a person and the target object.

6.

ACKNOWLEDGMENT

This work was supported by the Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research No. 18680024.

7.

REFERENCES

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