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    Learning from Examples in Unstructured, Outdoor Environments

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    LearningJFR06.pdf (1.035Mb)
    Date
    2006
    Author
    Sun, J.
    Mehta, Tejas R.
    Wooden, David
    Powers, Matthew
    Rehg, J.
    Balch, Tucker
    Egerstedt, Magnus B.
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    Abstract
    In this paper, we present a multi-pronged approach to the "Learning from Example" problem. In particular, we present a framework for integrating learning into a standard, hybrid navigation strategy, composed of both plan-based and reactive controllers. Based on the classification of colors and textures as either good or bad, a global map is populated with estimates of preferability in conjunction with the standard obstacle information. Moreover, individual feedback mappings from learned features to learned control actions are introduced as additional behaviors in the behavioral suite. A number of real-world experiments are discussed that illustrate the viability of the proposed method.
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    http://hdl.handle.net/1853/38455
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    • Georgia Robotics and InTelligent Systems Laboratory (GRITS) [230]
    • Georgia Robotics and InTelligent Systems Laboratory (GRITS) Publications [230]

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