Lyapunov-based Learning

Lyapunov-Based Learning is a control-theoretic framework in safe robot learning that employs Lyapunov functions as stability certificates to guide or constrain the learning process, ensuring dynamical stability and safety through formal verification of system trajectories.

78

Publications

4.5K

Citations

204

Authors

100

Institutions

Publications per year

2017–2026

50

Authors

204

  1. WE
    Warren E. Dixon

    University of Florida, United States

    3 publications · 93 citations

    4

    h-index

  2. ZI
    Zachary I. Bell

    University of Florida, United States

    3 publications · 93 citations

    3

    h-index

  3. ON
    Ofir Nachum

    Google (United States), United States

    2 publications · 232 citations

    2

    h-index

  4. AL
    Andre Lemme

    Bielefeld University, Germany

    2 publications · 67 citations

    2

    h-index

  5. TJ
    Theodore J. Perkins

    Ottawa Hospital, Canada

    2 publications · 169 citations

    2

    h-index

1–5 of 204

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 452 citations

    7

    h-index

  2. Automatica

    2 publications · 67 citations

    3

    h-index

  3. 3

    h-index

  4. IEEE Transactions on Cybernetics

    3 publications · 415 citations

    3

    h-index

  5. IEEE Transactions on Automatic Control

    3 publications · 299 citations

    3

    h-index

1–5