Image segmentation-driven sim-to-real deep reinforcement learning framework for accurate peg-in-hole assembly.

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Bibliographic Details
Title: Image segmentation-driven sim-to-real deep reinforcement learning framework for accurate peg-in-hole assembly.
Authors: Zhang, Ning1, Zhao, Yongjia1,2, zhaoyongjia@buaa.edu.cn, Yang, Minghao3, Dai, Shuling1
Source: Robotica; Aug2025, Vol. 43 Issue 8, p2783-2802, 20p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 187896084
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Image segmentation-driven sim-to-real deep reinforcement learning framework for accurate peg-in-hole assembly.
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  Data: <searchLink fieldCode="JN" term="%22Robotica%22">Robotica</searchLink>; Aug2025, Vol. 43 Issue 8, p2783-2802, 20p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=187896084
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1017/S026357472510177X
    Languages:
      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 2783
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      – TitleFull: Image segmentation-driven sim-to-real deep reinforcement learning framework for accurate peg-in-hole assembly.
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            NameFull: Zhang, Ning
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            NameFull: Zhao, Yongjia
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            NameFull: Yang, Minghao
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            NameFull: Dai, Shuling
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            – D: 01
              M: 08
              Text: Aug2025
              Type: published
              Y: 2025
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              Value: 43
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              Value: 8
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            – TitleFull: Robotica
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