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Recently simulation methods have been developed for optical tactile sensors to enable the Sim2Real learning, i.e., firstly training models in simulation before deploying them on the real robot.
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R. S. Dahiya, G. Metta, M. Valle, and G. Sandini, “Tactile sensing—from humans to humanoids,” IEEE Transactions on Robotics
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Z. Kappassov, J. A. Corrales, and V. Perdereau, “Tactile sensing in dexterous robot hands - Review,” Robotics and Autonomous Systems
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2017
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P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2017
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2017
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Z. Yi, H. Zhang, P. Tan, and M. Gong, “Dualgan: Unsupervised dual learning for image-to-image translation,” in Proceedings of the IEEE international conference on computer vision
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T. Kim, M. Cha, H. Kim, J. K. Lee, and J. Kim, “Learning to discover cross-domain relations with generative adversarial networks,” in International Conference on Machine Learning
2017
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J.-T. Lee, D. Bollegala, and S. Luo, ““touching to see” and “seeing to feel”: Robotic cross-modal sensory data generation for visual-tactile perception,” in 2019 International Conference on Robotics and Automation (ICRA)
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D. F. Gomes, Z. Lin, and S. Luo, “GelTip: A finger-shaped optical tactile sensor for robotic manipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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G. Cao, Y. Zhou, D. Bollegala, and S. Luo, “Spatio-temporal attention model for tactile texture recognition,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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D. F. Gomes, Z. Lin, and S. Luo, “Blocks world of touch: Exploiting the advantages of all-around finger sensing in robot grasping,” Frontiers in Robotics and AI
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E. Donlon, S. Dong, M. Liu, J. Li, E. Adelson, and A. Rodriguez, “Gelslim: A high-resolution, compact, robust, and calibrated tactile-sensing finger,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2018
Cited alongside, same era.
B. Ward-Cherrier, N. Pestell, L. Cramphorn, B. Winstone, M. E. Giannaccini, J. Rossiter, and N. F. Lepora, “The TacTip Family: Soft Optical Tactile Sensors with 3D-Printed Biomimetic Morphologies,” Soft Robotics
2018
Cited alongside, same era.
S. Luo, W. Yuan, E. Adelson, A. G. Cohn, and R. Fuentes, “Vitac: Feature sharing between vision and tactile sensing for cloth texture recognition,” in 2018 IEEE International Conference on Robotics and Automation (ICRA)
2018
Cited alongside, same era.
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. Efros, and T. Darrell, “Cycada: Cycle-consistent adversarial domain adaptation,” in International conference on machine learning
2018
Cited alongside, same era.
H. Yang, J. Sun, A. Carass, C. Zhao, J. Lee, Z. Xu, and J. Prince, “Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
2018
Cited alongside, same era.
D. F. Gomes, A. Wilson, and S. Luo, “Gelsight simulation for sim2real learning,” in ICRA ViTac Workshop
2019
Cited alongside, same era.
B. Romero, F. Veiga, and E. Adelson, “Soft, round, high resolution tactile fingertip sensors for dexterous robotic manipulation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA)
2020
Later among the works it cites.
D. F. Gomes, P. Paoletti, and S. Luo, “Generation of gelsight tactile images for sim2real learning,” IEEE Robotics and Automation Letters
2021
Closest in time.
F. Muratore, C. Eilers, M. Gienger, and J. Peters, “Data-efficient domain randomization with bayesian optimization,” IEEE Robotics and Automation Letters
2021
Closest in time.
2021
Closest in time.