Transfer Learning For Robotics

Transfer Learning for Robotics is a machine learning paradigm that enables a robotic system to leverage knowledge acquired from a source domain, such as simulation or a related task, to improve learning efficiency and performance in a target domain with limited data, thereby reducing the need for extensive real-world training.

138

Publications

8.7K

Citations

686

Authors

160

Institutions

Publications per year

2017–2026

113

Authors

686

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    7 publications · 891 citations

    7

    h-index

  2. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    6 publications · 3.2K citations

    6

    h-index

  3. AI
    Alex Irpan

    Google (United States), United States

    6 publications · 853 citations

    6

    h-index

  4. KR
    Kanishka Rao

    Google (United States), United States

    5 publications · 828 citations

    5

    h-index

  5. JI
    Julian Ibarz

    Google (United States), United States

    5 publications · 752 citations

    5

    h-index

1–5 of 686

Venues

  1. arXiv (Cornell University)

    Repository · 32 publications · 1.4K citations

    21

    h-index

  2. IEEE Robotics and Automation Letters

    9 publications · 315 citations

    8

    h-index

  3. Robotics and Autonomous Systems

    4 publications · 162 citations

    4

    h-index

  4. IEEE Transactions on Cognitive and Developmental Systems

    TCDS · 3 publications · 63 citations

    3

    h-index

  5. IEEE/ASME Transactions on Mechatronics

    2 publications · 92 citations

    2

    h-index

1–5