Generalization In Manipulation
Generalization in Manipulation is a research contribution in robotics that enables manipulation policies to perform effectively across diverse objects, environments, or tasks beyond those encountered during training by leveraging invariant representations, modular architectures, or meta-learning strategies to capture essential physical or semantic regularities.
195
Publications
11.8K
Citations
714
Authors
145
Institutions
Publications per year
2017–2026
128
Parents
195
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
2018 · 793 citations · Full text
Learning and generalization of motor skills by learning from demonstration
Peter Pástor, H. Hoffmann, Tamim Asfour et al. · 2009 · 710 citations
Code as Policies: Language Model Programs for Embodied Control
Jacky Liang, Wenlong Huang, Fei Xia et al. · 2023 · 569 citations
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Google (United States)
Mountain View, United States
12 h-index
40 publications
2.1K citations
University of California, Berkeley
Berkeley, United States
11 h-index
36 publications
4.3K citations
Stanford University
Stanford, United States
8 h-index
40 publications
2.7K citations
Karlsruhe Institute of Technology
Karlsruhe, Germany
7 h-index
20 publications
937 citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
15 publications
767 citations
Georgia Institute of Technology
Atlanta, United States
6 h-index
16 publications
345 citations
Massachusetts Institute of Technology
Cambridge, United States
6 h-index
23 publications
1.5K citations
University of Southern California
Los Angeles, United States
5 h-index
10 publications
2.6K citations
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Repository · 46 publications · 2.2K citations
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10 publications · 415 citations
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Conference · 3 publications · 201 citations
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IJRR · 2 publications · 128 citations
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2 publications · 29 citations
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