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
378
Data-Driven Grasp Synthesis—A Survey
IEEE Transactions on Robotics · 2014 · 851 citations · Full text
Automatic grasp planning using shape primitives
Andrew Miller, Steffen Knoop, Henrik I. Christensen et al. · 2004 · 715 citations · Full text
1Data-Driven Grasp Synthesis- A Survey
Jeannette Bohg, Antonio Morales, Tamim Asfour · 2016 · 554 citations
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Carnegie Mellon University
Pittsburgh, United States
17 h-index
46 publications
2.8K citations
Karlsruhe Institute of Technology
Karlsruhe, Germany
15 h-index
51 publications
3.1K citations

KTH Royal Institute of Technology
Stockholm, Sweden
14 h-index
33 publications
2.9K citations
Universitat Politècnica de Catalunya
Barcelona, Spain
11 h-index
33 publications
927 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
10 h-index
21 publications
1.1K citations
University of California, Berkeley
Berkeley, United States
10 h-index
33 publications
3K citations

Centre National de la Recherche Scientifique
Paris, France
9 h-index
24 publications
1.5K citations
Tsinghua University
Beijing, China
8 h-index
19 publications
1.1K citations
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13 publications · 927 citations
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IJRR · 13 publications · 2.6K citations
13
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14 publications · 739 citations
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8 publications · 359 citations
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6 publications · 288 citations
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