@article{Demiris:2006:10.1016/j.robot.2006.02.003,
author = {Demiris, Y and Khadhouri, B},
doi = {10.1016/j.robot.2006.02.003},
journal = {Robotics and Autonomous Systems},
pages = {361--369},
title = {Hierarchical attentive multiple models for execution and recognition of actions},
url = {http://dx.doi.org/10.1016/j.robot.2006.02.003},
volume = {54},
year = {2006}
}

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TY  - JOUR
AB  - 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.
AU  - Demiris,Y
AU  - Khadhouri,B
DO  - 10.1016/j.robot.2006.02.003
EP  - 369
PY  - 2006///
SN  - 0921-8890
SP  - 361
TI  - Hierarchical attentive multiple models for execution and recognition of actions
T2  - Robotics and Autonomous Systems
UR  - http://dx.doi.org/10.1016/j.robot.2006.02.003
UR  - http://hdl.handle.net/10044/1/26628
VL  - 54
ER  - 

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