Learning-based Impedance Control

Learning-Based Impedance Control is a control framework within impedance control that employs machine learning techniques to autonomously adapt impedance parameters through interaction data, distinguishing it from traditional model-based or fixed-parameter approaches by its reliance on learned, data-driven representations of optimal impedance modulation.

87

Publications

6.7K

Citations

261

Authors

137

Institutions

Publications per year

2017–2026

49

Authors

261

  1. YL
    Yanan Li

    University of Sussex, United Kingdom

    12 publications · 555 citations

    12

    h-index

  2. CY
    Chenguang Yang

    South China University of Technology, China

    11 publications · 1.2K citations

    11

    h-index

  3. SS
    Shuzhi Sam Ge

    National University of Singapore, Singapore

    6 publications · 369 citations

    6

    h-index

  4. TH
    Tong Heng Lee

    National University of Singapore, Singapore

    4 publications · 120 citations

    4

    h-index

  5. FS
    Freek Stulp

    Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Germany

    4 publications · 474 citations

    4

    h-index

1–5 of 261

Venues

  1. Neurocomputing

    4 publications · 260 citations

    4

    h-index

  2. Robotics and Autonomous Systems

    4 publications · 194 citations

    4

    h-index

  3. 3

    h-index

  4. IEEE Robotics and Automation Letters

    3 publications · 185 citations

    3

    h-index

  5. IEEE/ASME Transactions on Mechatronics

    3 publications · 96 citations

    3

    h-index

1–5