Uncertainty-aware Dynamics Learning

Uncertainty-Aware Dynamics Learning is a model-based robot learning framework that, within the class of dynamics learning methods, specifically incorporates quantified uncertainty estimates into the learning of system dynamics to improve robustness and generalization in control and planning under epistemic and aleatoric uncertainty.

16

Publications

755

Citations

50

Authors

19

Institutions

Publications per year

2017–2026

10

Authors

50

  1. ST
    Sebastian Trimpe

    Max Planck Institute for Intelligent Systems, Germany

    3 publications · 49 citations

    3

    h-index

  2. EA
    Evangelos A. Theodorou

    Georgia Institute of Technology, United States

    3 publications · 201 citations

    3

    h-index

  3. MB
    Mona Buisson-Fenet

    RWTH Aachen University, Germany

    2 publications · 39 citations

    2

    h-index

  4. YP
    Yunpeng Pan

    Georgia Institute of Technology, United States

    2 publications · 28 citations

    2

    h-index

  5. FS
    Friedrich Solowjow

    Max Planck Institute for Intelligent Systems, Germany

    2 publications · 39 citations

    2

    h-index

1–5 of 50

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 250 citations

    5

    h-index

  2. IEEE Transactions on Robotics

    2 publications · 93 citations

    2

    h-index

  3. Neurocomputing

    1 publications · 13 citations

    1

    h-index

  4. 1

    h-index

1–5