Forward Dynamics Modeling
Forward Dynamics Modeling is a computational framework within model-based robot learning that predicts a system's future state given its current state and applied actions, distinguishing itself by explicitly encoding the causal physical relationships governing robotic motion under environmental interactions.
152
Publications
5.9K
Citations
351
Authors
166
Institutions
Publications per year
2017–2026
36
Parents
152
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
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166
The University of Texas at Austin
Austin, United States
6 h-index
10 publications
302 citations
The University of Tokyo
Tokyo, Japan
5 h-index
8 publications
234 citations
California Institute of Technology
Pasadena, United States
5 h-index
6 publications
153 citations

University of British Columbia
Vancouver, Canada
5 h-index
10 publications
333 citations
Jet Propulsion Laboratory
Pasadena, United States
5 h-index
7 publications
173 citations

University of Waterloo
Waterloo, Canada
4 h-index
9 publications
347 citations
University of California, Berkeley
Berkeley, United States
4 h-index
12 publications
2.1K citations
Massachusetts Institute of Technology
Cambridge, United States
4 h-index
8 publications
150 citations
1–8 of 166
Repository · 9 publications · 306 citations
9
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6 publications · 245 citations
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IJRR · 4 publications · 410 citations
4
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4 publications · 153 citations
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CMBBE · 4 publications · 95 citations
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