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Current methods for estimating force from tactile sensor signals are either inaccurate analytic models or task-specific learned models.
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J. Hoelscher, J. Peters, and T. Hermans, “Evaluation of Tactile Feature Extraction for Interactive Object Recognition,” in IEEE-RAS International Conference on Humanoid Robotics , 2015
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Z. Su, K. Hausman, Y. Chebotar, A. Molchanov, G. E. Loeb, G. S. Sukhatme, and S. Schaal, “Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor,” in IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) , 2015, pp. 297–303
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Y. Gao, L. A. Hendricks, K. J. Kuchenbecker, and T. Darrell, “Deep learning for tactile understanding from visual and haptic data,” in IEEE International Conference on Robotics and Automation (ICRA) , 2016, pp. 536–543
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F. Veiga, J. Peters, and T. Hermans, “Grip stabilization of novel objects using slip prediction,” IEEE Transactions on Haptics , 2018
2018
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G. Sutanto, Z. Su, S. Schaal, and F. Meier, “Learning sensor feedback models from demonstrations via phase-modulated neural networks,” IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2018
2018
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C.-A. Cheng, M. Mukadam, J. Issac, S. Birchfield, D. Fox, B. Boots, and N. Ratliff., “RMPFlow: A computational graph for automatic motion policy generation.” in Proceedings of the 13th Annual Workshop on the Algorithmic Foundations of Robotics (WAFR) , 2018
2018
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2018
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B. Sundaralingam and T. Hermans, “Geometric in-hand regrasp planning: Alternating optimization of finger gaits and in-grasp manipulation,” IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2018
2018
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