According to the motor theories of perception, the motor systems of an observer are actively involved in the perception of actions when these are performed by a demonstrator. In this paper we review our computational architecture, HAMMER (Hierarchical Attentive Multiple Models for Execution and Recognition), where the motor control systems of a robot are organised in a hierarchical, distributed manner, and can be used in the dual role of (a) competitively selecting and executing an action, and (b) perceiving it when performed by a demonstrator. We subsequently demonstrate that such an arrangement can provide a principled method for the top-down control of attention during action perception, resulting in significant performance gains. We assess these performance gains under a variety of resource allocation strategies.
During the perception of human actions by robotic assistants, the robotic assistant needs to direct its computational and sensor resources to relevant parts of the human action. In previous work we have intro-
During the perception of human actions by robotic assistants, the robotic assistant needs to direct its computational and sensor resources to relevant parts of the human action. In previous work we have intro-
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