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Dexterous manipulation of arbitrary objects, a fundamental daily task for humans, has been a grand challenge for autonomous robotic systems.
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2015
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Z. Xu and E. Todorov, “Design of a highly biomimetic anthropomorphic robotic hand towards artificial limb regeneration,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 3485–3492
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R. Kolbert, N. Chavan-Dafle, and A. Rodriguez, “Experimental validation of contact dynamics for in-hand manipulation,” in International Symposium on Experimental Robotics . Springer, 2016, pp. 633–645
2016
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A. Gupta, C. Eppner, S. Levine, and P. Abbeel, “Learning dexterous manipulation for a soft robotic hand from human demonstrations,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 3786–3793
2016
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2016
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2016
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2016
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2017
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M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, P. Abbeel, and W. Zaremba, “Hindsight experience replay,” in NIPS , 2017
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S. Brahmbhatt, C. Ham, C. C. Kemp, and J. Hays, “Contactdb: Analyzing and predicting grasp contact via thermal imaging,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8709–8719
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H. Zhu, A. Gupta, A. Rajeswaran, S. Levine, and V. Kumar, “Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 3651–3657
2019
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C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
2017
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2017
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Y. Teh, V. Bapst, W. M. Czarnecki, J. Quan, J. Kirkpatrick, R. Hadsell, N. Heess, and R. Pascanu, “Distral: Robust multitask reinforcement learning,” in NIPS , 2017
2017
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2017
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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,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 231–238
2018
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2018
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M. Hessel, H. Soyer, L. Espeholt, W. Czarnecki, S. Schmitt, and H. van Hasselt, “Multi-task deep reinforcement learning with popart,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 3796–3803
2019
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K. Rakelly, A. Zhou, C. Finn, S. Levine, and D. Quillen, “Efficient off-policy meta-reinforcement learning via probabilistic context variables,” in International conference on machine learning . PMLR, 2019, pp. 5331–5340
2019
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O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray et al. , “Learning dexterous in-hand manipulation,” The International Journal of Robotics Research , vol. 39, no. 1, pp. 3–20, 2020
2020
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2020
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2020
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A. Nagabandi, K. Konolige, S. Levine, and V. Kumar, “Deep dynamics models for learning dexterous manipulation,” in Conference on Robot Learning , 2020, pp. 1101–1112
2020
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2020
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H. J. Charlesworth and G. Montana, “Solving challenging dexterous manipulation tasks with trajectory optimisation and reinforcement learning,” in International Conference on Machine Learning . PMLR, 2021, pp. 1496–1506
2021
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T. Chen, J. Xu, and P. Agrawal, “A system for general in-hand object re-orientation,” Conference on Robot Learning , 2021
2021
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