Generalization In Manipulation

Generalization in Manipulation is a research contribution in robotics that enables manipulation policies to perform effectively across diverse objects, environments, or tasks beyond those encountered during training by leveraging invariant representations, modular architectures, or meta-learning strategies to capture essential physical or semantic regularities.

195

Publications

11.8K

Citations

714

Authors

145

Institutions

Publications per year

2017–2026

128

Authors

714

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    19 publications · 1.6K citations

    15

    h-index

  2. CF
    Chelsea Finn

    University of California, Berkeley, United States

    11 publications · 952 citations

    11

    h-index

  3. LF
    Li Fei-Fei

    Stanford University, United States

    9 publications · 529 citations

    9

    h-index

  4. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    8 publications · 526 citations

    8

    h-index

  5. SS
    Silvio Savarese

    Stanford University, United States

    8 publications · 468 citations

    8

    h-index

1–5 of 714

Venues

  1. arXiv (Cornell University)

    Repository · 46 publications · 2.2K citations

    25

    h-index

  2. IEEE Robotics and Automation Letters

    10 publications · 415 citations

    10

    h-index

  3. 2022 International Conference on Robotics and Automation (ICRA)

    Conference · 3 publications · 201 citations

    3

    h-index

  4. The International Journal of Robotics Research

    IJRR · 2 publications · 128 citations

    2

    h-index

  5. IEEE Transactions on Robotics

    2 publications · 29 citations

    2

    h-index

1–5