Robotic Skill Generalization

Robotic Skill Generalization is a computational framework that enables a robot to apply learned motor skills to novel tasks, environments, or conditions beyond those encountered during training, thereby distinguishing it from task-specific robotic control strategies.

81

Publications

4.3K

Citations

462

Authors

94

Institutions

Publications per year

2017–2026

44

Authors

462

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    10 publications · 1.3K citations

    10

    h-index

  2. JP
    Jan Peters

    Technische Universität Darmstadt, Germany

    8 publications · 966 citations

    8

    h-index

  3. CF
    Chelsea Finn

    University of California, Berkeley, United States

    8 publications · 1.2K citations

    8

    h-index

  4. KH
    Karol Hausman

    Google (United States), United States

    6 publications · 1.1K citations

    6

    h-index

  5. JK
    Jens Kober

    Delft University of Technology, Netherlands

    5 publications · 751 citations

    5

    h-index

1–5 of 462

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 517 citations

    7

    h-index

  2. IEEE Robotics and Automation Letters

    5 publications · 89 citations

    5

    h-index

  3. Robotics and Autonomous Systems

    3 publications · 73 citations

    3

    h-index

  4. Journal of Robotics

    JR · 2 publications · 24 citations

    2

    h-index

  5. IEEE/ASME Transactions on Mechatronics

    2 publications · 280 citations

    2

    h-index

1–5