Real-world Adaptation

Real-World Adaptation is a methodological contribution in the field of Sim-To-Real Transfer that enables robotic systems to adjust simulated policies or models to perform effectively in physical environments by addressing discrepancies arising from sensor noise, environmental dynamics, and hardware variability.

35

Publications

2.7K

Citations

111

Authors

51

Institutions

Publications per year

2017–2026

23

Authors

111

  1. JH
    Josh H. McDermott

    Massachusetts Institute of Technology, United States

    2 publications · 94 citations

    2

    h-index

  2. DV
    David Vázquez

    Universitat Autònoma de Barcelona, Spain

    2 publications · 202 citations

    2

    h-index

  3. RJ
    Robert J. Sternberg

    Yale University, United States

    2 publications · 510 citations

    2

    h-index

  4. AF
    Andrew Francl

    McGovern Institute for Brain Research, United States

    2 publications · 94 citations

    2

    h-index

  5. DP
    Daniel Ponsa

    Universitat Autònoma de Barcelona, Spain

    2 publications · 202 citations

    2

    h-index

1–5 of 111

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 365 citations

    4

    h-index

  2. IEEE Robotics and Automation Letters

    1 publications · 18 citations

    1

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 1 publications · 13 citations

    1

    h-index

  4. eLife

    1 publications · 15 citations

    1

    h-index

  5. Education Sciences

    1 publications · 17 citations

    1

    h-index

1–5