Learning-based Manipulation Control
Learning-Based Manipulation Control is a robotic control paradigm that employs data-driven learning methods to acquire manipulation policies directly from interaction or experience, distinguishing it from model-based or rule-based approaches by its reliance on learned representations rather than explicit dynamical models or predefined control laws.
63
Publications
2.9K
Citations
177
Authors
73
Institutions
Publications per year
2017–2026
21
Parents
63
Robot manipulator control using neural networks: A survey
Long Jin, Shuai Li, Jiguo Yu et al. · Neurocomputing · 2018 · 313 citations
Combining active learning and reactive control for robot grasping
Oliver Kroemer, Renaud Detry, Justus Piater et al. · Robotics and Autonomous Systems · 2010 · 156 citations · Full text
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Technical University of Munich
Munich, Germany
4 h-index
7 publications
190 citations
The University of Tokyo
Tokyo, Japan
4 h-index
8 publications
203 citations
University of California, Berkeley
Berkeley, United States
4 h-index
15 publications
1.1K citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
11 publications
1K citations
The University of Osaka
Osaka, Japan
3 h-index
6 publications
126 citations
Ritsumeikan University
Kyoto, Japan
3 h-index
3 publications
59 citations

Nara Institute of Science and Technology
Ikoma, Japan
2 h-index
7 publications
173 citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
4 publications
107 citations
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4 publications · 324 citations
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3 publications · 42 citations
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3 publications · 107 citations
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Repository · 3 publications · 163 citations
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1 publications · 13 citations
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