Object Affordance Estimation

Object Affordance Estimation is a computational method that identifies the actionable possibilities of an object in an environment by predicting the set of feasible interactions between an agent and the object based on their physical and geometric properties.

28

Publications

1.8K

Citations

90

Authors

53

Institutions

Publications per year

2017–2026

12

Authors

90

  1. TL
    Timo Lüddecke

    University of Göttingen, Germany

    2 publications · 45 citations

    2

    h-index

  2. TD
    Trevor Darrell

    University of California, Berkeley, United States

    2 publications · 85 citations

    2

    h-index

  3. HO
    Hyun Oh Song

    University of California, Berkeley, United States

    2 publications · 85 citations

    2

    h-index

  4. FW
    Florentin Wörgötter

    University of Göttingen, Germany

    2 publications · 45 citations

    2

    h-index

  5. MF
    Mario Fritz

    Max Planck Institute for Informatics, Germany

    2 publications · 85 citations

    2

    h-index

1–5 of 90

Venues

  1. Neuropsychologia

    2 publications · 602 citations

    2

    h-index

  2. Neural Computing and Applications

    2 publications · 49 citations

    2

    h-index

  3. National Conference on Artificial Intelligence

    Conference · 1 publications · 11 citations

    1

    h-index

  4. Robotics and Autonomous Systems

    1 publications · 19 citations

    1

    h-index

  5. Artificial Life and Robotics

    1 publications · 32 citations

    1

    h-index

1–5