BibTex format

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 = {},
volume = {54},
year = {2006}

RIS format (EndNote, RefMan)

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 -
UR -
VL - 54
ER -