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RAS
2006

Hierarchical attentive multiple models for execution and recognition of actions

10 years 4 months ago
Hierarchical attentive multiple models for execution and recognition of actions
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.
Yiannis Demiris, Bassam Khadhouri
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where RAS
Authors Yiannis Demiris, Bassam Khadhouri
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