@article{Demiris:2003:10.1080/09540090310001655129,
author = {Demiris, Y and Johnson, M},
doi = {10.1080/09540090310001655129},
journal = {Connection Science},
pages = {231--243},
title = {Distributed, predictive perception of actions: a biologically inspired robotics architecture for imitation and learning},
url = {http://dx.doi.org/10.1080/09540090310001655129},
volume = {15},
year = {2003}
}

Download

TY  - JOUR
AB  - One of the most important abilities for an agent's cognitive development in a social environment is the ability to recognize and imitate actions of others. In this paper we describe a cognitive architecture for action recognition and imitation, and present experiments demonstrating its implementation in robots. Inspired by neuroscientific and psychological data, and adopting a ‘simulation theory of mind’ approach, the architecture uses the motor systems of the imitator in a dual role, both for generating actions, and for understanding actions when performed by others. It consists of a distributed system of inverse and forward models that uses prediction accuracy as a means to classify demonstrated actions. The architecture is also shown to be capable of learning new composite actions from demonstration.
AU  - Demiris,Y
AU  - Johnson,M
DO  - 10.1080/09540090310001655129
EP  - 243
PY  - 2003///
SN  - 0954-0091
SP  - 231
TI  - Distributed, predictive perception of actions: a biologically inspired robotics architecture for imitation and learning
T2  - Connection Science
UR  - http://dx.doi.org/10.1080/09540090310001655129
VL  - 15
ER  - 

Download