Probabilistic Dynamics Models

Probabilistic Dynamics Models is a type of dynamics model in model-based robot learning that represents system transition probabilities using probability distributions to quantify uncertainty in predictions, distinguishing it from deterministic dynamics models by explicitly accounting for epistemic and aleatoric uncertainties in state transitions.

67

Publications

4.2K

Citations

147

Authors

87

Institutions

Publications per year

2017–2026

13

Variants

Probabilistic Dynamics Model

Authors

147

  1. CS
    Carol Smidts

    University of Maryland, College Park, United States

    7 publications · 385 citations

    7

    h-index

  2. PL
    Pierre‐Etienne Labeau

    Université Libre de Bruxelles, Belgium

    4 publications · 109 citations

    4

    h-index

  3. JD
    J. Devooght

    Université Libre de Bruxelles, Belgium

    4 publications · 318 citations

    4

    h-index

  4. JI
    J.M. Izquierdo

    Universidad Politécnica de Cartagena, Spain

    3 publications · 63 citations

    3

    h-index

  5. VD
    Vikas Dhiman

    University of California San Diego, United States

    2 publications · 54 citations

    2

    h-index

1–5 of 147

Venues

  1. Nuclear Science and Engineering

    7 publications · 386 citations

    7

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 113 citations

    6

    h-index

  3. Reliability Engineering & System Safety

    5 publications · 158 citations

    5

    h-index

  4. Journal of Mathematical Chemistry

    2 publications · 40 citations

    2

    h-index

  5. IEEE Robotics and Automation Letters

    2 publications · 63 citations

    2

    h-index

1–5