Tactile Policy Learning
Tactile Policy Learning is a computational framework within robotic learning that integrates tactile feedback into policy optimization, distinguishing itself by enabling autonomous agents to adapt motor behaviors through direct physical interaction with environments.
9
Publications
387
Citations
19
Authors
12
Institutions
Publications per year
2017–2026
7
Parents
9
Stable reinforcement learning with autoencoders for tactile and visual data
Herke van Hoof, Nutan Chen, Maximilian Karl et al. · 2016 · 142 citations
Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry
Siyuan Dong, Devesh K. Jha, Diego Romeres et al. · 2021 · 92 citations
Tactile guidance for policy refinement and reuse
Brenna Argall, Eric L. Sauser, Aude Billard · 2010 · 31 citations · Full text
1–5 of 9
19
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1
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1
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1
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Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Germany
1 publications · 142 citations
1
h-index
1–5 of 19
12
Technical University of Munich
Munich, Germany
3 h-index
7 publications
418 citations
Max Planck Institute for Intelligent Systems
Stuttgart, Germany
1 h-index
1 publications
142 citations
Mitsubishi Electric (United States)
Cypress, United States
1 h-index
3 publications
276 citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
1 h-index
3 publications
93 citations
Fortiss
Munich, Germany
1 h-index
1 publications
142 citations
Massachusetts Institute of Technology
Cambridge, United States
1 h-index
3 publications
276 citations
Seattle University
Seattle, United States
1 h-index
1 publications
17 citations
University of Southern California
Los Angeles, United States
1 h-index
3 publications
51 citations
1–8 of 12
2 publications · 60 citations
2
h-index
Repository · 1 publications · 14 citations
1
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Repository · 1 publications · 11 citations
1
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Repository · 1 publications · 20 citations
1
h-index
1 publications · 17 citations
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