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Covert Perceptual Capability Development

Huang, Xiao and Weng, Juyang (2005) Covert Perceptual Capability Development. [Conference Paper]

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Abstract

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.

Item Type:Conference Paper
Keywords:vision-based navigation, incremental hierarchical discriminant regression, K-nearest neighbor Q-learning, developmental robot
Subjects:Computer Science > Statistical Models
Computer Science > Machine Learning
Computer Science > Robotics
ID Code:4981
Deposited By:Prince, Dr Christopher G.
Deposited On:23 Jul 2006
Last Modified:11 Mar 2011 08:56

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