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    A Deep-Reinforcement-Learning Approach to the Peg-in-Hole Task with Goal Uncertainties

    Rouillard T 2021.pdf (5.728Mb)
    Access Status
    Open access
    Authors
    Rouillard, Thibault
    Date
    2020
    Supervisor
    Lei Cui
    Ian Howard
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    School of Civil and Mechanical Engineering
    URI
    http://hdl.handle.net/20.500.11937/85887
    Collection
    • Curtin Theses
    Abstract

    The thesis proposed a framework to train deep-reinforcement-learning agents for fine manipulation tasks with goal uncertainties. It consisted of three aspects: state-space formulation, artificial training-goals uncertainties, and progressive training. The framework was used in a simulation for two fine manipulation tasks, square Peg-in-Hole and round Peg-in-Hole. The resulting behaviours were then transferred a physical robotic manipulator and compared to traditional training methods. The deep-reinforcement-learning agents trained using the framework in this work outperformed those trained with definite goals.

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