Learning-based Manipulation

Learning-Based Manipulation is a robotic manipulation paradigm that employs data-driven learning methods to acquire control policies or models for performing manipulation tasks, distinguishing itself from traditional model-based approaches by relying on experience and sensory feedback rather than explicit physical or geometric modeling.

188

Publications

9.1K

Citations

595

Authors

176

Institutions

Publications per year

2017–2026

121

Authors

595

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    15 publications · 1.1K citations

    13

    h-index

  2. KG
    Ken Goldberg

    University of California, Berkeley, United States

    9 publications · 408 citations

    9

    h-index

  3. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    7 publications · 810 citations

    7

    h-index

  4. CF
    Chelsea Finn

    University of California, Berkeley, United States

    7 publications · 296 citations

    7

    h-index

  5. AB
    Ashwin Balakrishna

    University of California, Berkeley, United States

    6 publications · 255 citations

    6

    h-index

1–5 of 595

Venues

  1. arXiv (Cornell University)

    Repository · 25 publications · 1.5K citations

    16

    h-index

  2. IEEE Robotics and Automation Letters

    17 publications · 1K citations

    15

    h-index

  3. Robotics and Autonomous Systems

    7 publications · 395 citations

    7

    h-index

  4. Autonomous Robots

    3 publications · 77 citations

    3

    h-index

  5. Advanced Robotics

    3 publications · 83 citations

    3

    h-index

1–5