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  • Hierarchical learning of robotic contact policies [Elektronski vir]
    Simonič, Mihael ; Ude, Aleš ; Nemec, Bojan
    The paper addresses the issue of learning tasks where a robot maintains permanent contact with the environment. We propose a new methodology based on a hierarchical learning scheme coupled with task ... representation through directed graphs. These graphs are constituted of nodes and branches that correspond to the states and robotic actions, respectively. The upper level of the hierarchy essentially operates as a decision-making algorithm. It leverages reinforcement learning (RL) techniques to facilitate optimal decision-making. The actions are generated by a constraint-space following (CSF) controller that autonomously identifies feasible directions for motion. The controller generates robot motion by adjusting its stiffness in the direction defined by the Frenet–Serret frame, which is aligned with the robot path. The proposed framework was experimentally verified through a series of challenging robotic tasks such as maze learning, door opening, learning to shift the manual car gear, and learning car license plate light assembly by disassembly.
    Source: Robotics and computer-integrated manufacturing [Elektronski vir]. - ISSN 1879-2537 (Vol. 86, [article no.] 102657, Apr. 2024, 1-12 str.)
    Type of material - e-article ; adult, serious
    Publish date - 2024
    Language - english
    COBISS.SI-ID - 164894211

    Link(s):

    https://www.sciencedirect.com/science/article/pii/S0736584523001321
    Repository of the University of Ljubljana – RUL
    DiRROS - Digital repository of Slovenian research organizations
    DOI

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