Learning-based Manipulation Control

Learning-Based Manipulation Control is a robotic control paradigm that employs data-driven learning methods to acquire manipulation policies directly from interaction or experience, distinguishing it from model-based or rule-based approaches by its reliance on learned representations rather than explicit dynamical models or predefined control laws.

63

Publications

2.9K

Citations

177

Authors

73

Institutions

Publications per year

2017–2026

21

Authors

177

  1. RT
    Russ Tedrake

    Massachusetts Institute of Technology, United States

    4 publications · 253 citations

    4

    h-index

  2. SA
    S. Arimoto

    Ritsumeikan University, Japan

    4 publications · 160 citations

    4

    h-index

  3. SK
    S. Kawamura

    Ritsumeikan University, Japan

    3 publications · 118 citations

    3

    h-index

  4. FM
    F. Miyazaki

    The University of Osaka, Japan

    3 publications · 122 citations

    3

    h-index

  5. SL
    Sergey Levine

    University of California, Berkeley, United States

    3 publications · 165 citations

    3

    h-index

1–5 of 177

Venues

  1. Robotics and Autonomous Systems

    4 publications · 324 citations

    4

    h-index

  2. IFAC Proceedings Volumes

    3 publications · 42 citations

    3

    h-index

  3. IEEE Robotics and Automation Letters

    3 publications · 107 citations

    3

    h-index

  4. arXiv (Cornell University)

    Repository · 3 publications · 163 citations

    3

    h-index

  5. 1

    h-index

1–5