Tactile Representation Learning

Tactile Representation Learning is a machine learning framework that derives latent feature representations from tactile sensor data to enable robots to understand and interpret physical interactions through learned mappings of contact, force, and texture into structured, generalizable models of object and surface properties.

76

Publications

4.4K

Citations

229

Authors

100

Institutions

Publications per year

2017–2026

49

Authors

229

  1. FS
    Fuchun Sun

    Tsinghua University, China

    7 publications · 224 citations

    7

    h-index

  2. MH
    Matej Hoffmann

    Czech Technical University in Prague, Czechia

    4 publications · 141 citations

    4

    h-index

  3. HL
    Huaping Liu

    Tsinghua University, China

    4 publications · 162 citations

    4

    h-index

  4. HS
    Harold Soh

    National University of Singapore, Singapore

    2 publications · 90 citations

    3

    h-index

  5. JZ
    Jun-Yan Zhu

    Carnegie Mellon University, United States

    3 publications · 1.3K citations

    3

    h-index

1–5 of 229

Venues

  1. IEEE Transactions on Robotics

    3 publications · 265 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 52 citations

    3

    h-index

  3. 2022 International Conference on Robotics and Automation (ICRA)

    Conference · 2 publications · 43 citations

    2

    h-index

  4. Sensors

    2 publications · 73 citations

    2

    h-index

  5. IEEE Robotics and Automation Letters

    2 publications · 65 citations

    2

    h-index

1–5