Model-based Robot Learning

Model-Based Robot Learning is a subfield of robot learning that employs learned or predefined environmental models to predict system dynamics and plan optimal control policies, distinguishing itself from model-free approaches by explicitly representing and utilizing the underlying physical or stochastic structure of the robotic task.

552

Publications

31.6K

Citations

1.8K

Authors

401

Institutions

Publications per year

2017–2026

292

Authors

1.8K

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    37 publications · 3.6K citations

    26

    h-index

  2. JP
    Jan Peters

    Technische Universität Darmstadt, Germany

    20 publications · 4.4K citations

    19

    h-index

  3. CF
    Chelsea Finn

    University of California, Berkeley, United States

    16 publications · 2.2K citations

    15

    h-index

  4. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    17 publications · 1.2K citations

    13

    h-index

  5. SC
    Sylvain Calinon

    Idiap Research Institute, Switzerland

    13 publications · 1.1K citations

    13

    h-index

1–5 of 1.8K

Venues

  1. arXiv (Cornell University)

    Repository · 77 publications · 4K citations

    28

    h-index

  2. IEEE Robotics and Automation Letters

    40 publications · 1.6K citations

    25

    h-index

  3. Robotics and Autonomous Systems

    9 publications · 609 citations

    9

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 335 citations

    8

    h-index

  5. IEEE Transactions on Robotics

    8 publications · 692 citations

    8

    h-index

1–5