人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
論文
視覚注視と自己評価型学習の機能に基づくブートストラップ学習を通した共同注意の創発
長井 志江細田 耕森田 章生浅田 稔
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2004 年 19 巻 1 号 p. 10-19

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This study argues how human infants acquire the ability of joint attention through interactions with their caregivers from a viewpoint of cognitive developmental robotics. In this paper, a mechanism by which a robot acquires sensorimotor coordination for joint attention through bootstrap learning is described. Bootstrap learning is a process by which a learner acquires higher capabilities through interactions with its environment based on embedded lower capabilities even if the learner does not receive any external evaluation nor the environment is controlled. The proposed mechanism for bootstrap learning of joint attention consists of the robot's embedded mechanisms: visual attention and learning with self-evaluation. The former is to find and attend to a salient object in the field of the robot's view, and the latter is to evaluate the success of visual attention, not joint attention, and then to learn the sensorimotor coordination. Since the object which the robot looks at based on visual attention does not always correspond to the object which the caregiver is looking at in an environment including multiple objects, the robot may have incorrect learning situations for joint attention as well as correct ones. However, the robot is expected to statistically lose the learning data of the incorrect ones as outliers because of its weaker correlation between the sensor input and the motor output than that of the correct ones, and consequently to acquire appropriate sensorimotor coordination for joint attention even if the caregiver does not provide any task evaluation to the robot. The experimental results show the validity of the proposed mechanism. It is suggested that the proposed mechanism could explain the developmental mechanism of infants' joint attention because the learning process of the robot's joint attention can be regarded as equivalent to the developmental process of infants' one.

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© 2004 JSAI (The Japanese Society for Artificial Intelligence)
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