Inverse Model Learning
Inverse Model Learning is a method in self-supervised robot learning that predicts the actions required to achieve a desired state transition by modeling the inverse of the environment dynamics, distinguishing itself from forward models by inferring causes from effects rather than predicting effects from causes.
328
Publications
15K
Citations
975
Authors
395
Institutions
Publications per year
2017–2026
170
328
1–5 of 328
975
7
h-index
6
h-index
Advanced Telecommunications Research Institute International, Japan
6 publications · 1.2K citations
6
h-index
5
h-index
5
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1–5 of 975
395
Bielefeld University
Bielefeld, Germany
9 h-index
24 publications
603 citations
University of California, Berkeley
Berkeley, United States
7 h-index
28 publications
3.1K citations
Massachusetts Institute of Technology
Cambridge, United States
6 h-index
16 publications
1K citations

Centre National de la Recherche Scientifique
Paris, France
6 h-index
15 publications
395 citations
Max Planck Society
Munich, Germany
6 h-index
12 publications
1.7K citations
Tsinghua University
Beijing, China
5 h-index
14 publications
480 citations
University of Southern California
Los Angeles, United States
5 h-index
15 publications
1.1K citations
Northwestern University
Evanston, United States
5 h-index
11 publications
484 citations
1–8 of 395
Repository · 18 publications · 696 citations
13
h-index
7 publications · 143 citations
7
h-index
4 publications · 108 citations
4
h-index
3 publications · 93 citations
4
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3 publications · 154 citations
3
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