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    A Novel Continuum Manipulator

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    COCHRANE-DOCUMENT-2017.pdf (13.53Mb)
    Date
    2015-09-21
    Author
    Cochrane, Eric C
    Metadata
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    Abstract
    We address the problem of controlling continuum manipulators and evaluate Reinforcement Learning to produce a control policy for a robotic platform. Our approach discretizes the state and action spaces to reduce the training needed to converge to an optimal policy. We integrate Q-Learning, computer vision, and a pneumatic system into a single robotic platform. The agent is tasked with tracking and striking a target with a continuum manipulator modeled by a party-blower. We describe Reinforcement Learning, the methods used to train the agent, and describe the performance of the optimal policy successfully striking the target.
    URI
    http://hdl.handle.net/1969.1/164392
    Subject
    Reinforcement Learning
    Continuum Manipulator
    Artificial Intelligence
    Robotics
    Machine Learning
    Computer Science
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    • Undergraduate Research Scholars Capstone (2006–present)
    Citation
    Cochrane, Eric C (2017). A Novel Continuum Manipulator. Undergraduate Research Scholars Program. Available electronically from http : / /hdl .handle .net /1969 .1 /164392.

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