Movement Primitive Learning

Movement Primitive Learning is a computational framework within the field of robot skill acquisition that formalizes the process of extracting and generalizing parameterized motor primitives from expert demonstrations, distinguishing itself by enabling flexible composition of basic movement elements into complex tasks through structured, reusable, and context-adaptive representations.

190

Publications

10.4K

Citations

427

Authors

176

Institutions

Publications per year

2017–2026

95

Authors

427

  1. JP
    Jan Peters

    Technische Universität Darmstadt, Germany

    38 publications · 2.7K citations

    23

    h-index

  2. JK
    Jens Kober

    Delft University of Technology, Netherlands

    19 publications · 1.8K citations

    16

    h-index

  3. GN
    Gerhard Neumann

    Technische Universität Darmstadt, Germany

    16 publications · 932 citations

    16

    h-index

  4. AU
    Aleš Ude

    Jožef Stefan Institute, Slovenia

    17 publications · 875 citations

    15

    h-index

  5. AG
    Andrej Gams

    Jožef Stefan Institute, Slovenia

    12 publications · 372 citations

    12

    h-index

1–5 of 427

Venues

  1. IEEE Robotics and Automation Letters

    10 publications · 309 citations

    10

    h-index

  2. Robotics and Autonomous Systems

    9 publications · 424 citations

    9

    h-index

  3. The International Journal of Robotics Research

    IJRR · 7 publications · 1K citations

    7

    h-index

  4. IEEE Transactions on Robotics

    4 publications · 255 citations

    4

    h-index

  5. MPG.PuRe (Max Planck Society)

    Repository · 4 publications · 120 citations

    4

    h-index

1–5