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UniT is an approach to tactile representation learning, using VQGAN to learn a compact latent space and serve as the tactile representation.
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M. Polic, I. Krajacic, N. Lepora, and M. Orsag, “Convolutional autoencoder for feature extraction in tactile sensing,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 3671–3678, 2019
2019
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J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar et al. , “Bootstrap your own latent-a new approach to self-supervised learning,” Advances in neural information processing systems , vol. 33, pp. 21 271–21 284, 2020
2020
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S. Wang, Y. She, B. Romero, and E. Adelson, “GelSight Wedge: Measuring high-resolution 3D contact geometry with a compact robot finger,” in Proc. IEEE Int. Conf. Robot. Autom. , 2021, pp. 6468–6475
2021
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2021
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P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021
2021
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2021
Cited alongside, same era.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 16 000–16 009
2022
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Cited alongside, same era.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in Proceedings of Robotics: Science and Systems (RSS) , 2023
2023
Cited alongside, same era.
C. Sferrazza, Y. Seo, H. Liu, Y. Lee, and P. Abbeel, “The power of the senses: Generalizable manipulation from vision and touch through masked multimodal learning,” 2023
2023
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Z. Fei, M. Fan, L. Zhu, J. Huang, X. Wei, and X. Wei, “Masked auto-encoders meet generative adversarial networks and beyond,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 24 449–24 459
2023
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2024
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2024
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
W. Yang, A. Angleraud, R. S. Pieters, J. Pajarinen, and J.-K. Kämäräinen, “Seq2seq imitation learning for tactile feedback-based manipulation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5829–5836
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
G. Cao, J. Jiang, D. Bollegala, and S. Luo, “Learn from incomplete tactile data: Tactile representation learning with masked autoencoders,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 10 800–10 805
2023
Cited alongside, same era.
J. Zhao, Y. Ma, L. Wang, and E. H. Adelson, “Transferable tactile transformers for representation learning across diverse sensors and tasks,” 2024
2024
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2024
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2024
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2024
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2024
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