TY  - GEN
ID  - cogprints4981
UR  - http://cogprints.org/4981/
A1  - Huang, Xiao
A1  - Weng, Juyang
Y1  - 2005///
N2  - In this paper, we propose a model to develop
robots? covert perceptual capability using reinforcement learning. Covert perceptual behavior is treated as action selected by a motivational system. We apply this model to
vision-based navigation. The goal is to enable
a robot to learn road boundary type. Instead
of dealing with problems in controlled environments with a low-dimensional state space,
we test the model on images captured in non-stationary environments. Incremental Hierarchical Discriminant Regression is used to
generate states on the fly. Its coarse-to-fine
tree structure guarantees real-time retrieval
in high-dimensional state space. K Nearest-Neighbor strategy is adopted to further reduce training time complexity.
PB  - Lund University Cognitive Studies
KW  - vision-based navigation
KW  -  incremental hierarchical discriminant regression
KW  -  K-nearest neighbor Q-learning
KW  -  developmental robot
TI  - Covert Perceptual Capability Development
SP  - 107
AV  - public
EP  - 110
ER  -

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