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Tactile representation learning (TRL) equips robots with the ability to leverage touch information, boosting performance in tasks such as environment perception and object manipulation.
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Jeremy A Fishel and Gerald E Loeb · 2012
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Harold Soh, Yanyu Su, and Yiannis Demiris · 2012
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Michal Haindl and Jiří Filip · 2013
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Tactile identification of objects using bayesian exploration
Danfei Xu, Gerald E. Loeb, and Jeremy A. Fishel · 2013
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Incrementally learning objects by touch: Online discriminative and generative models for tactile-based recognition
Harold Soh and Yiannis Demiris · 2014
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Tactile object recognition with semi-supervised learning
Shan Luo, Xiaozhou Liu, Kaspar Althoefer, and Hongbin Liu · 2015
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Evaluation of tactile feature extraction for interactive object recognition
Janine Hoelscher, Jan Peters, and Tucker Hermans · 2015
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Tactile sensing in dexterous robot hands
Zhanat Kappassov, Juan-Antonio Corrales, and Véronique Perdereau · 2015
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A. Mordvintsev, Christopher Olah, and Mike Tyka · 2015
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Shiv S Baishya and Berthold Bäuml · 2016
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Towards effective tactile identification of textures using a hybrid touch approach
Tasbolat Taunyazov, Hui Fang Koh, Yan Wu, Caixia Cai, and Harold Soh · 2019
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Fast texture classification using tactile neural coding and spiking neural network
Tasbolat Taunyazov, Yansong Chua, Ruihan Gao, Harold Soh, and Yan Wu · 2020
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Supervised autoencoder joint learning on heterogeneous tactile sensory data: Improving material classification performance
Ruihan Gao, Tasbolat Taunyazov, Zhiping Lin, and Yan Wu · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Ilya Loshchilov and Frank Hutter · 2016
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Recent progress on tactile object recognition
Huaping Liu, Yupei Wu, Fuchun Sun, and Di Guo · 2017
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Gelsight: High-resolution robot tactile sensors for estimating geometry and force
Wenzhen Yuan, Siyuan Dong, and Edward H. Adelson · 2017
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Heba Khamis, Raquel Izquierdo Albero, Matteo Salerno, Ahmad Shah Idil, Andrew Loizou, and Stephen J Redmond · 2018
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Event-driven visual-tactile sensing and learning for robots
Tasbolat Taunyazoz, Weicong Sng, Hian Hian See, Brian Lim, Jethro Kuan, Abdul Fatir Ansari, Benjamin Tee, and Harold Soh · 2020
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Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 2020
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Spatio-temporal encoding improves neuromorphic tactile texture classification
Anupam Kumar Gupta, Andrei Nakagawa-Silva, Nathan F. Lepora, and Nitish V. Thakor · 2021
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On the opportunities and risks of foundation models
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On explainability and sensor-adaptability of a robot tactile texture representation using a two-stage recurrent networks
Ruihan Gao, Tian Tian, Zhiping Lin, and Yan Wu · 2021
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Artificial sa-i, ra-i and ra-ii/vibrotactile afferents for tactile sensing of texture
Nicholas Pestell and Nathan Lepora · 2021
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A continual learning survey: Defying forgetting in classification tasks
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Schedule-robust online continual learning
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