Uncertainty-aware Robot Learning

Uncertainty-Aware Robot Learning is a robot learning framework that explicitly incorporates uncertainty estimation and propagation within the learning process to enable safe and adaptive decision-making under epistemic and aleatoric uncertainty.

43

Publications

2.4K

Citations

136

Authors

57

Institutions

Publications per year

2017–2026

22

Authors

136

  1. SL
    Sergey Levine

    University of California, Berkeley, United States

    3 publications · 308 citations

    3

    h-index

  2. JB
    Joshua B. Tenenbaum

    Massachusetts Institute of Technology, United States

    2 publications · 106 citations

    2

    h-index

  3. RM
    Rowan McAllister

    University of California, Berkeley, United States

    2 publications · 81 citations

    2

    h-index

  4. FC
    F.G. Callari

    McGill University, Canada

    2 publications · 41 citations

    2

    h-index

  5. JW
    Jiajun Wu

    Massachusetts Institute of Technology, United States

    2 publications · 106 citations

    2

    h-index

1–5 of 136

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 413 citations

    6

    h-index

  2. Robotics and Autonomous Systems

    2 publications · 36 citations

    2

    h-index

  3. Neural Computing and Applications

    2 publications · 26 citations

    2

    h-index

  4. IEEE Control Systems

    2 publications · 359 citations

    2

    h-index

  5. 2021 European Control Conference (ECC)

    Conference · 1 publications · 22 citations

    1

    h-index

1–5