Affordance based word-to-meaning association

This paper presents a method to associate meanings to words in manipulation tasks. We base our model on an affordance network, i.e., a mapping between robot actions, robot perceptions and the perceived effects of these actions upon objects. We extend the affordance model to incorporate words. Using verbal descriptions of a task, the model uses temporal co-occurrence to create links between speech utterances and the involved objects, actions and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot´s own understanding of its actions. Thus they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task.