Learning From Teleoperation

Learning From Teleoperation is a methodological framework in Human-Robot Interaction Learning that enables robots to acquire task-specific behaviors by extracting and generalizing control policies from human-operated teleoperated demonstrations.

84

Publications

4.4K

Citations

290

Authors

95

Institutions

Publications per year

2017–2026

48

Authors

290

  1. KG
    Ken Goldberg

    University of California, Berkeley, United States

    5 publications · 769 citations

    5

    h-index

  2. SL
    Sergey Levine

    University of California, Berkeley, United States

    3 publications · 501 citations

    5

    h-index

  3. SC
    Sylvain Calinon

    Idiap Research Institute, Switzerland

    4 publications · 145 citations

    4

    h-index

  4. GW
    Garrett Warnell

    The University of Texas at Austin, United States

    3 publications · 138 citations

    3

    h-index

  5. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    3 publications · 729 citations

    3

    h-index

1–5 of 290

Venues

  1. IEEE Robotics and Automation Letters

    8 publications · 601 citations

    8

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 146 citations

    5

    h-index

  3. IEEE Transactions on Robotics

    2 publications · 126 citations

    2

    h-index

  4. SMARTech Repository (Georgia Institute of Technology)

    Repository · 1 publications · 12 citations

    1

    h-index

  5. Applied Sciences

    1 publications · 21 citations

    1

    h-index

1–5