Learning-based Force Control

Learning-Based Force Control is a force control paradigm that employs data-driven learning algorithms to adaptively regulate contact forces by generalizing from experience, distinguishing it from model-based or fixed-gain control approaches through its reliance on learned representations of system dynamics and environmental interactions.

61

Publications

2.6K

Citations

172

Authors

78

Institutions

Publications per year

2017–2026

22

Authors

172

  1. CY
    Chenguang Yang

    South China University of Technology, China

    3 publications · 441 citations

    4

    h-index

  2. KH
    Kensuke Harada

    National Institute of Advanced Industrial Science and Technology, Japan

    3 publications · 203 citations

    3

    h-index

  3. CC
    Cristian C. Beltran-Hernandez

    National Institute of Advanced Industrial Science and Technology, Japan

    3 publications · 203 citations

    3

    h-index

  4. GX
    Guihua Xia

    Harbin Engineering University, China

    2 publications · 57 citations

    2

    h-index

  5. MK
    Mitsuo Kawato

    Advanced Telecommunications Research Institute International, Japan

    2 publications · 51 citations

    2

    h-index

1–5 of 172

Venues

  1. IEEE Robotics and Automation Letters

    3 publications · 226 citations

    3

    h-index

  2. Automatica

    2 publications · 57 citations

    2

    h-index

  3. IEEE Transactions on Robotics

    2 publications · 429 citations

    2

    h-index

  4. Mechatronics

    2 publications · 48 citations

    2

    h-index

  5. 2

    h-index

1–5