Grasp Candidate Generation

Grasp Candidate Generation is a computational process in robotic manipulation that identifies a set of plausible grasp configurations on an object by sampling potential contact points and orientations, distinguishing itself from related methods through its focus on efficiently producing diverse, physically feasible candidates as a precursor to grasp evaluation and selection.

378

Publications

19.8K

Citations

922

Authors

313

Institutions

Publications per year

2017–2026

121

Authors

922

  1. TA
    Tamim Asfour

    Karlsruhe Institute of Technology, Germany

    16 publications · 1.4K citations

    15

    h-index

  2. DK
    Danica Kragić

    KTH Royal Institute of Technology, Sweden

    13 publications · 647 citations

    13

    h-index

  3. RS
    Raúl Suárez

    Universitat Politècnica de Catalunya, Spain

    12 publications · 320 citations

    11

    h-index

  4. DF
    Dieter Fox

    University of Washington, United States

    8 publications · 238 citations

    8

    h-index

  5. RD
    Rüdiger Dillmann

    Karlsruhe Institute of Technology, Germany

    8 publications · 468 citations

    8

    h-index

1–5 of 922

Venues

  1. Robotics and Autonomous Systems

    13 publications · 927 citations

    13

    h-index

  2. The International Journal of Robotics Research

    IJRR · 13 publications · 2.6K citations

    13

    h-index

  3. IEEE Robotics and Automation Letters

    14 publications · 739 citations

    12

    h-index

  4. Robotics and Computer-Integrated Manufacturing

    8 publications · 359 citations

    9

    h-index

  5. IEEE Transactions on Robotics

    6 publications · 288 citations

    7

    h-index

1–5