Learning-based Manipulation
Learning-Based Manipulation is a robotic manipulation paradigm that employs data-driven learning methods to acquire control policies or models for performing manipulation tasks, distinguishing itself from traditional model-based approaches by relying on experience and sensory feedback rather than explicit physical or geometric modeling.
188
Publications
9.1K
Citations
595
Authors
176
Institutions
Publications per year
2017–2026
121
188
Solving Rubik's Cube with a Robot Hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej et al. · arXiv (Cornell University) · 2019 · 633 citations · Full text
Learning to poke by poking: experiential learning of intuitive physics
Pulkit Agrawal, Ashvin Nair, Pieter Abbeel et al. · Neural Information Processing Systems · 2016 · 281 citations
A Survey on Learning-Based Robotic Grasping
Kilian Kleeberger, Richard Bormann, Werner Kraus et al. · Current Robotics Reports · 2020 · 276 citations · Full text
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176
University of California, Berkeley
Berkeley, United States
16 h-index
84 publications
7K citations
Carnegie Mellon University
Pittsburgh, United States
10 h-index
24 publications
576 citations
University of Southern California
Los Angeles, United States
7 h-index
19 publications
564 citations
Google (United States)
Mountain View, United States
7 h-index
36 publications
1.9K citations
Georgia Institute of Technology
Atlanta, United States
6 h-index
18 publications
733 citations
Massachusetts Institute of Technology
Cambridge, United States
6 h-index
23 publications
1.6K citations
Waseda University
Tokyo, Japan
5 h-index
19 publications
402 citations
The University of Osaka
Osaka, Japan
4 h-index
10 publications
292 citations
1–8 of 176
Repository · 25 publications · 1.5K citations
16
h-index
17 publications · 1K citations
15
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7 publications · 395 citations
7
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3 publications · 77 citations
3
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3 publications · 83 citations
3
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