This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model\r\n(BCM) in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a\r\ntarget and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot\r\naction domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning\r\nstructure through neural network training process represents a connection between the visual perceptions and motor sequence\r\nof actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot\r\nbehavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and\r\ncould be recognized as an idea for designing different mobile robot behaviour assistance.
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