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To achieve a dexterous robotic manipulation, we need to endow our robot with tactile feedback capability, i.e.
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N. F. Lepora, K. Aquilina, and L. Cramphorn, “Exploratory tactile servoing with active touch,” IEEE Robotics and Automation Letters , vol. 2, no. 2, pp. 1156–1163, April 2017
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H. van Hoof, T. Hermans, G. Neumann, and J. Peters, “Learning robot in-hand manipulation with tactile features,” in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) , Nov 2015, pp. 121–127
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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” 2015. [Online]. Available: http://download.tensorflow.org/paper/whitepaper2015.pdf
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2015
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2016
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2016
Cited alongside, same era.
J. Sung, J. K. Salisbury, and A. Saxena, “Learning to represent haptic feedback for partially-observable tasks,” in IEEE International Conference on Robotics and Automation , 2017, pp. 2802–2809
2017
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2017
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R. Johansson, “Light a match: Normal, pre-anesthetization performance vs post-anesthetization performance,” https://www.youtube.com/watch?v=0LfJ3M3Kn80 , 2018, accessed: 2018-08-04
2018
Closest in time.
G. Sutanto, Z. Su, S. Schaal, and F. Meier, “Learning sensor feedback models from demonstrations via phase-modulated neural networks,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) , May 2018, pp. 1142–1149
2018
Closest in time.