title: Covert Perceptual Capability Development
creator: Huang, Xiao
creator: Weng, Juyang
subject: Statistical Models
subject: Machine Learning
subject: Robotics
description: 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.
publisher: Lund University Cognitive Studies
contributor: Berthouze, Luc
contributor: Kaplan, Frédéric
contributor: Kozima, Hideki
contributor: Yano, Hiroyuki
contributor: Konczak, Jürgen
contributor: Metta, Giorgio
contributor: Nadel, Jacqueline
contributor: Sandini, Giulio
contributor: Stojanov, Georgi
contributor: Balkenius, Christian
date: 2005
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/4981/1/huang.pdf
identifier:   Huang, Xiao and Weng, Juyang  (2005) Covert Perceptual Capability Development.  [Conference Paper]     
relation: http://cogprints.org/4981/