Self-supervised Manipulation Learning
Self-Supervised Manipulation Learning is a machine learning approach within robotic manipulation that autonomously generates supervisory signals from raw, unlabeled interaction data to learn control policies without explicit human-provided labels.
40
Publications
1.9K
Citations
111
Authors
32
Institutions
Publications per year
2017–2026
38
Parents
40
Self-Supervised Visual Planning with Temporal Skip Connections
Frederik Ebert, Chelsea Finn, Alex X. Lee et al. · arXiv (Cornell University) · 2017 · 113 citations · Full text
Learning Robotic Manipulation through Visual Planning and Acting
Angelina Wang, Thanard Kurutach, Kara Liu et al. · 2019 · 91 citations · Full text
1–5 of 40
111
10
h-index
9
h-index
5
h-index
4
h-index
4
h-index
1–5 of 111
32
University of California, Berkeley
Berkeley, United States
7 h-index
30 publications
1.7K citations
Stanford University
Stanford, United States
7 h-index
35 publications
1.6K citations
Google (United States)
Mountain View, United States
3 h-index
11 publications
151 citations
Georgia Institute of Technology
Atlanta, United States
3 h-index
8 publications
120 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
3 publications
76 citations
Berkeley College
New York, United States
2 h-index
8 publications
624 citations
Toyota Research Institute
Palo Alto, United States
2 h-index
5 publications
184 citations

University of Freiburg
Freiburg im Breisgau, Germany
2 h-index
4 publications
270 citations
1–8 of 32
Repository · 14 publications · 717 citations
11
h-index
4 publications · 274 citations
4
h-index
Conference · 1 publications · 36 citations
1
h-index
1 publications · 18 citations
1
h-index
1 publications · 29 citations
1
h-index
1–5