Tidy-Up Tasks Using Trajectory-based Imitation Learning

Doo Jun Kim, Hyun Jun Jo, Jae Bok Song

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    When performing reinforcement learning using a robot arm in the real environment, it is important to perform reinforcement learning safely and quickly. This is because unexpected behaviors during reinforcement learning and long-term learning can damage the robot arm or surrounding objects. In this study, trajectory-based imitation learning that suppresses unexpected situations and quickly learns the policies suitable for the robots is proposed by limiting the workspace to be explored through one human demonstration. Trajectory-based imitation learning consists of two stages. First, a reference trajectory is generated considering the position of a target object and the expert trajectory from the human demonstration. Second, the target task is trained by performing reinforcement learning based on the generated reference trajectory. Experiments were conducted in simulation and real environments to verify the proposed imitation learning algorithm. In the simulation, a laptop folding task was performed with a success rate of 97% to verify the performance of the algorithm. In addition, it was shown that safe and fast learning is possible with only one demonstration video on the drawer arrangement in a real environment.

    Original languageEnglish
    Title of host publication2021 21st International Conference on Control, Automation and Systems, ICCAS 2021
    PublisherIEEE Computer Society
    Pages496-499
    Number of pages4
    ISBN (Electronic)9788993215212
    DOIs
    Publication statusPublished - 2021
    Event21st International Conference on Control, Automation and Systems, ICCAS 2021 - Jeju, Korea, Republic of
    Duration: 2021 Oct 122021 Oct 15

    Publication series

    NameInternational Conference on Control, Automation and Systems
    Volume2021-October
    ISSN (Print)1598-7833

    Conference

    Conference21st International Conference on Control, Automation and Systems, ICCAS 2021
    Country/TerritoryKorea, Republic of
    CityJeju
    Period21/10/1221/10/15

    Bibliographical note

    Funding Information:
    This work was supported by IITP grant funded by the Korea Government MSIT. (No. 2018-0-00622)

    Publisher Copyright:
    © 2021 ICROS.

    Keywords

    • Human demonstration
    • Manipulation
    • Reinforcement learning

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Control and Systems Engineering
    • Electrical and Electronic Engineering

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