Hierarchical attentive multiple models for execution and recognition of actions
Hierarchical attentive multiple models for execution and recognition of actions
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DOI：
10.1016/j.robot.2006.02.003
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发表时间：
2006-05-31
影响因子：
4.3
通讯作者：
Khadhouri, Bassam
中科院分区：
文献类型：
--
作者：
Demiris, Yiannis;Khadhouri, Bassam
根据知觉的运动理论，观察者的运动系统在演示者执行动作时积极地参与感知这些动作。在本文中，我们回顾了我们的计算体系结构HAMMER(用于执行和识别的分层注意多模型)，其中机器人的电机控制系统以分层、分布式的方式组织，并可用于(A)竞争性地选择和执行动作，以及(B)在由演示者执行时感知动作。我们随后证明，这样的安排可以提供一种原则性的方法，在动作知觉过程中自上而下地控制注意力，从而显著提高性能。我们在各种资源分配策略下评估这些性能提升。(C)2006爱思唯尔B.V.保留所有权利。
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. (c) 2006 Elsevier B.V. All rights reserved.