Reinforcement Learning Visual Servoing

Reinforcement Learning Visual Servoing is a visual servoing method that employs reinforcement learning to autonomously learn control policies mapping visual feedback to robot actions, distinguishing itself from classical and model-based visual servoing approaches by optimizing control through trial-and-error interaction with the environment.

35

Publications

1.5K

Citations

104

Authors

43

Institutions

Publications per year

2017–2026

24

Authors

104

  1. HS
    Haobin Shi

    Northwestern Polytechnical University, China

    5 publications · 282 citations

    5

    h-index

  2. SL
    Sergey Levine

    University of California, Berkeley, United States

    4 publications · 220 citations

    4

    h-index

  3. KH
    Kao‐Shing Hwang

    National Sun Yat-sen University, Taiwan

    3 publications · 147 citations

    3

    h-index

  4. CG
    Chris Gaskett

    James Cook University, Australia

    3 publications · 82 citations

    3

    h-index

  5. MJ
    Martin Jägersand

    University of Alberta, Canada

    2 publications · 49 citations

    2

    h-index

1–5 of 104

Venues

  1. IEEE/ASME Transactions on Mechatronics

    2 publications · 179 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 83 citations

    2

    h-index

  3. IEEE Transactions on Industrial Informatics

    2 publications · 198 citations

    2

    h-index

  4. IEEE Systems Journal

    ISJ · 1 publications · 22 citations

    1

    h-index

  5. IEEE Transactions on Industrial Electronics

    1 publications · 68 citations

    1

    h-index

1–5