Perception-Action Coupling via Imitation and Attention

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When we interact with objects, we give them meaning, i.e. we show what they axe potentially useful for. We believe that physical entities are anchored to perceptual representations, and through them to the actions that they 'afford'. This paper brings an imitation mechanism and an attention system together computationally, with the aim of having a system that is capable of creating and maintaining these anchors. The integrated system is implemented on two different platforms: a simulated humanoid robot learning from another how to drink a glass of beer, and a simulated mobile robot learning from another how to follow walls.