@misc{cogprints4981,
          volume = {123},
          editor = {Luc Berthouze and Fr{\'e}d{\'e}ric Kaplan and Hideki Kozima and Hiroyuki Yano and J{\"u}rgen Konczak and Giorgio Metta and Jacqueline Nadel and Giulio Sandini and Georgi Stojanov and Christian Balkenius},
           title = {Covert Perceptual Capability Development},
          author = {Xiao Huang and Juyang Weng},
       publisher = {Lund University Cognitive Studies},
            year = {2005},
           pages = {107--110},
        keywords = {vision-based navigation, incremental hierarchical discriminant regression, K-nearest neighbor Q-learning, developmental robot},
             url = {http://cogprints.org/4981/},
        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.}
}

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