Model-based Grasp Synthesis

Model-Based Grasp Synthesis is a computational approach within multi-fingered grasping that formulates grasp configurations through analytical models of object geometry and hand kinematics, distinguishing itself from data-driven or heuristic methods by relying on explicit physical and geometric constraints to predict stable and dexterous grasps.

27

Publications

1.8K

Citations

82

Authors

44

Institutions

Publications per year

2017–2026

8

Authors

82

  1. AM
    Andrew Miller

    Columbia University, United States

    2 publications · 306 citations

    2

    h-index

  2. KL
    Kevin L. Kilgore

    Case Western Reserve University, United States

    2 publications · 73 citations

    2

    h-index

  3. PH
    P. Hunter Peckham

    Case Western Reserve University, United States

    2 publications · 73 citations

    2

    h-index

  4. BI
    Boyko Iliev

    Örebro University, Sweden

    2 publications · 24 citations

    2

    h-index

  5. DK
    Danica Kragić

    KTH Royal Institute of Technology, Sweden

    2 publications · 121 citations

    2

    h-index

1–5 of 82

Venues

  1. Robotics and Autonomous Systems

    5 publications · 155 citations

    5

    h-index

  2. Medical & Biological Engineering & Computing

    2 publications · 73 citations

    2

    h-index

  3. The International Journal of Robotics Research

    IJRR · 2 publications · 798 citations

    2

    h-index

  4. IEEE Transactions on Robotics

    1 publications · 63 citations

    1

    h-index

  5. Robotica

    1 publications · 15 citations

    1

    h-index

1–5