Learning-based Visual Servoing

Learning-Based Visual Servoing is a visual servoing approach that employs machine learning techniques to model the control policy or vision-to-action mapping, distinguishing it from traditional model-based methods by leveraging data-driven learning to adapt to uncertainties and complex visual dynamics.

65

Publications

2.1K

Citations

206

Authors

80

Institutions

Publications per year

2017–2026

40

Authors

206

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    4 publications · 220 citations

    4

    h-index

  2. BL
    Baoquan Li

    Tiangong University, China

    3 publications · 121 citations

    3

    h-index

  3. WS
    Wuxi Shi

    Tiangong University, China

    3 publications · 121 citations

    3

    h-index

  4. FC
    François Chaumette

    Institut de Recherche en Informatique et Systèmes Aléatoires, France

    2 publications · 40 citations

    2

    h-index

  5. JC
    Joshua C. Triyonoputro

    The University of Osaka, Japan

    2 publications · 76 citations

    2

    h-index

1–5 of 206

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 127 citations

    4

    h-index

  2. IEEE Robotics and Automation Letters

    4 publications · 309 citations

    4

    h-index

  3. Mechatronics

    2 publications · 51 citations

    2

    h-index

  4. Journal of Intelligent & Robotic Systems

    2 publications · 54 citations

    2

    h-index

  5. IEEE Control Systems

    2 publications · 57 citations

    2

    h-index

1–5