Representation Learning For Robotics

Representation Learning for Robotics is a methodological framework within robotic machine learning that enables autonomous systems to automatically discover and structure salient features from raw sensory data to facilitate downstream tasks such as control, planning, and decision-making in real-world environments.

407

Publications

19.2K

Citations

1.5K

Authors

388

Institutions

Publications per year

2017–2026

187

Authors

1.5K

  1. CF
    Chelsea Finn

    University of California, Berkeley, United States

    13 publications · 1.4K citations

    13

    h-index

  2. DF
    Dieter Fox

    University of Washington, United States

    12 publications · 1.1K citations

    12

    h-index

  3. SL
    Sergey Levine

    University of California, Berkeley, United States

    11 publications · 1.3K citations

    11

    h-index

  4. KH
    Karol Hausman

    Google (United States), United States

    10 publications · 1.2K citations

    10

    h-index

  5. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    9 publications · 909 citations

    9

    h-index

1–5 of 1.5K

Venues

  1. arXiv (Cornell University)

    Repository · 47 publications · 1.9K citations

    23

    h-index

  2. IEEE Robotics and Automation Letters

    15 publications · 361 citations

    13

    h-index

  3. The International Journal of Robotics Research

    IJRR · 8 publications · 542 citations

    8

    h-index

  4. Robotics and Autonomous Systems

    8 publications · 242 citations

    8

    h-index

  5. International Journal of Humanoid Robotics

    6 publications · 376 citations

    6

    h-index

1–5