Sparse Demonstration Learning
Sparse Demonstration Learning is a learning paradigm within the broader domain of Learning from Demonstration that specifically addresses the challenge of acquiring effective policies from a limited and non-continuous set of demonstration instances, thereby distinguishing itself by its focus on data efficiency and generalization under sparse supervisory signals.
4
Publications
87
Citations
19
Authors
9
Institutions
Publications per year
2017–2026
4
Parents
4
Learning From Sparse Demonstrations
Wanxin Jin, Todd D. Murphey, Dana Kulić et al. · IEEE Transactions on Robotics · 2022 · 33 citations
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai et al. · arXiv (Cornell University) · 2020 · 11 citations · Full text
1–4 of 4
19
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1
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1 publications · 27 citations
1
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1
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1–5 of 19
9
Google (United States)
Mountain View, United States
1 h-index
1 publications
11 citations
University of Pennsylvania
Philadelphia, United States
1 h-index
1 publications
33 citations
Stanford University
Stanford, United States
1 h-index
4 publications
64 citations
Northwestern University
Evanston, United States
1 h-index
1 publications
33 citations
Northrop Grumman (United States)
Falls Church, United States
1 h-index
1 publications
33 citations
Tsinghua University
Beijing, China
1 h-index
1 publications
16 citations
Michigan State University
East Lansing, United States
1 h-index
3 publications
33 citations

Monash University
Melbourne, Australia
1 h-index
1 publications
33 citations
1–8 of 9
Repository · 3 publications · 54 citations
3
h-index
1 publications · 33 citations
1
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1–2