Tactile Policy Learning

Tactile Policy Learning is a computational framework within robotic learning that integrates tactile feedback into policy optimization, distinguishing itself by enabling autonomous agents to adapt motor behaviors through direct physical interaction with environments.

9

Publications

387

Citations

19

Authors

12

Institutions

Publications per year

2017–2026

7

Authors

19

  1. AB
    Aude Billard

    École Polytechnique Fédérale de Lausanne, Switzerland

    1 publications · 31 citations

    1

    h-index

  2. HV
    Herke van Hoof

    University of Amsterdam, Netherlands

    1 publications · 142 citations

    1

    h-index

  3. MK
    Maximilian Karl

    Technical University of Munich, Germany

    1 publications · 142 citations

    1

    h-index

  4. AR
    Alberto Rodríguez

    Massachusetts Institute of Technology, United States

    1 publications · 92 citations

    1

    h-index

  5. PV
    Patrick van der Smagt

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

    1 publications · 142 citations

    1

    h-index

1–5 of 19

Venues

  1. IEEE Robotics and Automation Letters

    2 publications · 60 citations

    2

    h-index

  2. 1

    h-index

  3. arXiv (Cornell University)

    Repository · 1 publications · 11 citations

    1

    h-index

  4. MPG.PuRe (Max Planck Society)

    Repository · 1 publications · 20 citations

    1

    h-index

  5. IEEE Transactions on Robotics

    1 publications · 17 citations

1–5