Skip to main content
Article thumbnail
Location of Repository

Covert Perceptual Capability Development

By Xiao Huang and Juyang Weng

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

Topics: Statistical Models, Machine Learning, Robotics
Publisher: Lund University Cognitive Studies
Year: 2005
OAI identifier: oai:cogprints.org:4981

Suggested articles

Citations

  1. (2001). A reinforcement learning model of selective visual attention.
  2. (1990). Active perception and reinforcement learning.
  3. (2001). Attention as selection-for-action: a scheme for active perception.
  4. (1999). Hierarchical discriminat regression.
  5. (2000). Vision-guided behavior acquisition of a mobile robot by multilayered reinforcement learning.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.