Preference-based Robot Learning
Preference-Based Robot Learning is a robot learning paradigm that belongs to the class of data-efficient learning methods, distinguished by its use of human or agent preference comparisons over alternative behaviors as supervisory signals to guide policy optimization in the absence of explicit reward functions.
18
Publications
746
Citations
63
Authors
28
Institutions
Publications per year
2017–2026
14
18
A Kendama Learning Robot Based on Bi-directional Theory
Hiroyuki Miyamoto, Stefan Schaal, Hiroaki Gomi et al. · Neural Networks · 1996 · 175 citations
Robot, organize my shelves! Tidying up objects by predicting user preferences
Nichola Abdo, Cyrill Stachniss, Luciano Spinello et al. · 2015 · 58 citations
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28

Cornell University
Ithaca, United States
2 h-index
8 publications
624 citations
University of California, Berkeley
Berkeley, United States
2 h-index
6 publications
138 citations

National Taiwan University
Taipei, Taiwan
1 h-index
3 publications
48 citations
Research Organization of Information and Systems
Tokyo, Japan
1 h-index
5 publications
875 citations
Texas Tech University
Lubbock, United States
1 h-index
1 publications
27 citations

Kanagawa Institute of Technology
Atsugi, Japan
1 h-index
1 publications
175 citations
Georgia Institute of Technology
Atlanta, United States
1 h-index
1 publications
175 citations
University of Washington
Seattle, United States
1 h-index
1 publications
31 citations
1–8 of 28
2 publications · 54 citations
2
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IJRR · 2 publications · 125 citations
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Repository · 2 publications · 74 citations
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THRI · 1 publications · 12 citations
1
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Conference · 1 publications · 23 citations
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1–5