Tactile Reinforcement Learning

Tactile Reinforcement Learning is a machine learning framework that integrates tactile sensory feedback into the reinforcement learning paradigm to enable agents to learn goal-directed manipulation behaviors through physical interaction with environments, distinguished by its reliance on haptic input as a primary source of state information for decision-making.

5

Publications

289

Citations

16

Authors

7

Institutions

Publications per year

2017–2026

2

Authors

16

  1. AG
    Axel Gräser

    University of Bremen, Germany

    1 publications · 28 citations

    1

    h-index

  2. RS
    Ricarda Scheiner

    University of Würzburg, Germany

    1 publications · 181 citations

    1

    h-index

  3. JW
    John W. Lloyd

    Bristol Robotics Laboratory, United Kingdom

    1 publications · 33 citations

    1

    h-index

  4. AC
    Alex Church

    Bristol Robotics Laboratory, United Kingdom

    1 publications · 33 citations

    1

    h-index

  5. DA
    David A. Grant

    University of Wisconsin–Madison, United States

    1 publications · 12 citations

    1

    h-index

1–5 of 16

Venues

  1. IEEE Robotics and Automation Letters

    1 publications · 33 citations

    1

    h-index

  2. Robotics

    1 publications · 28 citations

    1

    h-index

  3. Journal of Experimental Psychology

    1 publications · 12 citations

    1

    h-index

  4. Journal of Comparative Physiology A

    1 publications · 181 citations

    1

    h-index

  5. Journal of Special Education Technology

    JSET · 1 publications · 35 citations

    1

    h-index

1–5