Learning-based Grasp Synthesis
Learning-Based Grasp Synthesis is a computational framework within multi-fingered grasping that employs machine learning models to generate feasible grasp configurations by learning mappings from object or scene representations to hand poses, distinguished from analytical methods by its reliance on data-driven generalization rather than explicit physical or geometric modeling.
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3.2K
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193
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2017–2026
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Corey Goldfeder, Matei Ciocarlie, Hao Dang et al. · 2009 · 312 citations
Grasping Field: Learning Implicit Representations for Human Grasps
Korrawe Karunratanakul, Jinlong Yang, Yan Zhang et al. · 2020 · 191 citations · Full text
Interactive grasp learning based on human demonstration
Staffan Ekvall, Danica Kragić · 2004 · 122 citations
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ETH Zurich
Zurich, Switzerland
4 h-index
16 publications
1.4K citations
Aalto University
Espoo, Finland
3 h-index
8 publications
235 citations

University of Groningen
Groningen, The Netherlands
3 h-index
6 publications
105 citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
3 h-index
6 publications
708 citations
Karlsruhe Institute of Technology
Karlsruhe, Germany
2 h-index
5 publications
494 citations
Örebro University
Örebro, Sweden
2 h-index
5 publications
148 citations
University of California, Berkeley
Berkeley, United States
2 h-index
4 publications
182 citations
Columbia University
New York, United States
2 h-index
7 publications
1.3K citations
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4 publications · 205 citations
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4 publications · 562 citations
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Repository · 3 publications · 88 citations
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Conference · 2 publications · 47 citations
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1 publications · 14 citations
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