Tactile Representation Learning
Tactile Representation Learning is a machine learning framework that derives latent feature representations from tactile sensor data to enable robots to understand and interpret physical interactions through learned mappings of contact, force, and texture into structured, generalizable models of object and surface properties.
76
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4.4K
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229
Authors
100
Institutions
Publications per year
2017–2026
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Learning the signatures of the human grasp using a scalable tactile glove
Subramanian Sundaram, Petr Kellnhofer, Yunzhu Li et al. · Nature · 2019 · 1.1K citations
Deep learning for tactile understanding from visual and haptic data
Yang Gao, Lisa Anne Hendricks, Katherine J. Kuchenbecker et al. · 2016 · 253 citations
Stable reinforcement learning with autoencoders for tactile and visual data
Herke van Hoof, Nutan Chen, Maximilian Karl et al. · 2016 · 142 citations
Connecting Touch and Vision via Cross-Modal Prediction
Yunzhu Li, Jun-Yan Zhu, Russ Tedrake et al. · 2019 · 127 citations
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Tsinghua University
Beijing, China
9 h-index
29 publications
1.1K citations
University of California, Berkeley
Berkeley, United States
4 h-index
8 publications
1.1K citations
National University of Singapore
Singapore, Singapore
3 h-index
5 publications
106 citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
6 publications
109 citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
14 publications
7.6K citations
University of Pennsylvania
Philadelphia, United States
3 h-index
11 publications
1.2K citations

Italian Institute of Technology
Genoa, Italy
3 h-index
11 publications
466 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
5 publications
85 citations
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3 publications · 265 citations
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2 publications · 73 citations
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2 publications · 65 citations
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