57,882 research outputs found
Covert Perceptual Capability Development
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
Chaotic Characteristics of Discrete-time Linear Inclusion Dynamical Systems
In this paper, we study the chaotic behavior of a discrete-time linear
inclusion.Comment: 7 pages; submitte
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