Robust Robot Learning

Robust Robot Learning is a methodological framework in safe robot learning that ensures reliable adaptation and performance of robotic systems under environmental uncertainties, model inaccuracies, and unexpected disturbances by explicitly incorporating resilience to variations and failures during the learning process.

34

Publications

1.3K

Citations

111

Authors

55

Institutions

Publications per year

2017–2026

17

Authors

111

  1. SC
    Sylvain Calinon

    Idiap Research Institute, Switzerland

    2 publications · 323 citations

    2

    h-index

  2. AB
    Aude Billard

    École Polytechnique Fédérale de Lausanne, Switzerland

    2 publications · 255 citations

    2

    h-index

  3. PG
    Philippe Gaussier

    CY Cergy Paris Université, France

    2 publications · 52 citations

    2

    h-index

  4. RK
    Robert K. Katzschmann

    ETH Zurich, Switzerland

    1 publications · 23 citations

    1

    h-index

  5. WH
    Wenhui Huang

    Nanyang Technological University, Singapore

    1 publications · 49 citations

    1

    h-index

1–5 of 111

Venues

  1. IEEE Robotics and Automation Letters

    3 publications · 192 citations

    3

    h-index

  2. Adaptive Behavior

    1 publications · 20 citations

    1

    h-index

  3. 1

    h-index

  4. Science

    1 publications · 43 citations

    1

    h-index

  5. Mathematics and Computers in Simulation

    1 publications · 50 citations

    1

    h-index

1–5