A major challenge is to design a robot that can attract and control human attention in various social situations. If a robot would like to communicate a person, it may turn its gaze to him/her for eye contact. However, it is not an easy task for the robot to make eye contact because such a turning action alone may not be enough in all situations, especially when the robot and the human are not facing each other. In this paper, we present an attention control approach through robot's behaviors that can attract a person's attention by three actions: head turning, head shaking, and uttering reference terms corresponding to three viewing situations in which the human vision senses the robot (near peripheral field of view, far peripheral field of view, and out of field of view). After gaining attention, the robot makes eye contact through showing gaze awareness by blinking its eyes, and directs the human attention by eye and head turning behaviors to share an object.

A major challenge in HRI is to design a social robot that can attract a target human's attention to control his/her attention toward a particular direction in various social situations. If a robot would like to initiate an interaction with a person, it may turn its gaze to him/her for eye contact. However, it is not an easy task for the robot to make eye contact because such a turning action alone may not be enough to initiate an interaction in all situations, especially when the robot and the human are not facing each other or the human intensely attends to his/her task. In this paper, we propose a conceptual model of attention control with four phases: attention attraction, eye contact, attention avoidance, and attention shift. In order to initiate an attention control process, the robot first tries to gain the target participant's attention toward it through head turning, or head shaking action depending on the three viewing situations where the robot is captured in his/her field of view (central field of view, near peripheral field of view, and far peripheral field of view). After gaining her/his attention, the robot makes eye contact only with the target person through showing gaze awareness by blinking its eyes, and directs her/his attention toward an object by turning its eyes and head cues. Moreover, the robot can show attention to aversion behaviors if non-target persons look at it. We design a robot based on the proposed approach, and it is confirmed as effective to control the target participant's attention in experimental evaluation.

A social robot needs to attract the attention of a target human and shift it from his/her current focus to what is sought by the robot. The robot should recognize the current target's attention level to smoothly perform this attention control. In this paper, we propose a vision-based system to detect the level of attention or willingness of the target person towards the robot and to control his/her attention. The system estimates the attention level from rich visual cues of human's face and head. Then, by timing target's attention to determine the appropriate attention level, it generates aware signals and makes eye contact with the target. Finally, the robot shifts the target's attention to an intended direction. The experimental results reveal that the proposed system is effective in controlling the target's attention.

It is a major challenge in HRI to design a social robot that is able to direct a target human's attention towards an intended direction. For this purpose, the robot may first turn its gaze to him/her in order to establish eye contact. However, such a turning action of the robot may not in itself be sufficient to make eye contact with the target person in all situations, especially when the robot and the person are not facing each other or the human is intensely engaged in a task. In this paper, we propose a conceptual model of attention control with five phases: attention attraction, eye contact, attention avoidance, gaze back, and attention shift. We conducted two experiments to validate our model in human-robot interaction scenarios.