Deep Visual Servoing

Deep Visual Servoing is a visual servoing framework that integrates deep learning models to directly map high-dimensional visual inputs to robot control commands, thereby enabling end-to-end learning of closed-loop sensorimotor policies without explicit system modeling.

45

Publications

1.8K

Citations

167

Authors

68

Institutions

Publications per year

2017–2026

45

Authors

167

  1. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    4 publications · 177 citations

    4

    h-index

  2. SL
    Sergey Levine

    University of California, Berkeley, United States

    4 publications · 220 citations

    4

    h-index

  3. FC
    François Chaumette

    Institut de Recherche en Informatique et Systèmes Aléatoires, France

    3 publications · 227 citations

    3

    h-index

  4. KH
    Kensuke Harada

    National Institute of Advanced Industrial Science and Technology, Japan

    2 publications · 76 citations

    2

    h-index

  5. DO
    Daniel Ortíz-Arroyo

    Aalborg University, Denmark

    2 publications · 47 citations

    2

    h-index

1–5 of 167

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 145 citations

    5

    h-index

  2. IEEE Robotics and Automation Letters

    3 publications · 144 citations

    3

    h-index

  3. Robotics and Autonomous Systems

    2 publications · 125 citations

    2

    h-index

  4. 1

    h-index

  5. Journal of Hydroinformatics

    1 publications · 16 citations

    1

    h-index

1–5