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Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology.
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2012
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2012
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2012
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2012
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J. Varley, J. Weisz, J. Weiss, and P. Allen, “Generating multi-fingered robotic grasps via deep learning,” in 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2015, pp. 4415–4420
2015
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2015
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M. Savva, A. X. Chang, and P. Hanrahan, “Semantically-Enriched 3D Models for Common-sense Knowledge,” CVPR 2015 Workshop on Functionality, Physics, Intentionality and Causality , 2015
2015
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2015
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K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 28, 2015
2015
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2015
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M. Kokic, J. A. Stork, J. A. Haustein, and D. Kragic, “Affordance detection for task-specific grasping using deep learning,” in 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) . IEEE, 2017, pp. 91–98
2017
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2017
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R. M. Murray, A mathematical introduction to robotic manipulation . CRC press, 2017
2017
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B. Calli, A. Singh, J. Bruce, A. Walsman, K. Konolige, S. Srinivasa, P. Abbeel, and A. M. Dollar, “Yale-cmu-berkeley dataset for robotic manipulation research,” The International Journal of Robotics Research , vol. 36, no. 3, pp. 261–268, 2017
2017
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J. Liang, V. Makoviychuk, A. Handa, N. Chentanez, M. Macklin, and D. Fox, “Gpu-accelerated robotic simulation for distributed reinforcement learning,” in Conference on Robot Learning . PMLR, 2018, pp. 270–282
2018
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H. Dai, A. Majumdar, and R. Tedrake, “Synthesis and optimization of force closure grasps via sequential semidefinite programming,” in Robotics Research . Springer, 2018, pp. 285–305
2018
Cited alongside, same era.
M. Gou, H.-S. Fang, Z. Zhu, S. Xu, C. Wang, and C. Lu, “Rgb matters: Learning 7-dof grasp poses on monocular rgbd images,” in Proceedings of the International Conference on Robotics and Automation (ICRA) , 2021
2021
Later among the works it cites.
C. Wang, H.-S. Fang, M. Gou, H. Fang, J. Gao, and C. Lu, “Graspness discovery in clutters for fast and accurate grasp detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 15 964–15 973
2021
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Y.-W. Chao, W. Yang, Y. Xiang, P. Molchanov, A. Handa, J. Tremblay, Y. S. Narang, K. Van Wyk, U. Iqbal, S. Birchfield, et al. , “Dexycb: A benchmark for capturing hand grasping of objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9044–9053
2021
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T. Liu, Z. Liu, Z. Jiao, Y. Zhu, and S.-C. Zhu, “Synthesizing diverse and physically stable grasps with arbitrary hand structures using differentiable force closure estimator,” IEEE Robotics and Automation Letters (RA-L) , 2021
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2019
Cited alongside, same era.
Y. Hasson, G. Varol, D. Tzionas, I. Kalevatykh, M. J. Black, I. Laptev, and C. Schmid, “Learning joint reconstruction of hands and manipulated objects,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Cited alongside, same era.
S. Brahmbhatt, C. Ham, C. C. Kemp, and J. Hays, “Contactdb: Analyzing and predicting grasp contact via thermal imaging,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Cited alongside, same era.
S. Brahmbhatt, A. Handa, J. Hays, and D. Fox, “Contactgrasp: Functional multi-finger grasp synthesis from contact,” in International Conference on Intelligent Robots and Systems (IROS) , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Cited alongside, same era.
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 444–11 453
2020
Cited alongside, same era.
C. Eppner, A. Mousavian, and D. Fox, “ACRONYM: A large-scale grasp dataset based on simulation,” in 2021 IEEE Int. Conf. on Robotics and Automation, ICRA , 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
P. Mandikal and K. Grauman, “Learning dexterous grasping with object-centric visual affordances,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 6169–6176
2021
Later among the works it cites.
H. Jiang, S. Liu, J. Wang, and X. Wang, “Hand-object contact consistency reasoning for human grasps generation,” in International Conference on Computer Vision (ICCV) , 2021
2021
Later among the works it cites.
J. Lundell, E. Corona, T. N. Le, F. Verdoja, P. Weinzaepfel, G. Rogez, F. Moreno-Noguer, and V. Kyrki, “Multi-fingan: Generative coarse-to-fine sampling of multi-finger grasps,” in International Conference on Robotics and Automation (ICRA) , 2021
2021
Later among the works it cites.
J. Lundell, F. Verdoja, and V. Kyrki, “Ddgc: Generative deep dexterous grasping in clutter,” IEEE Robotics and Automation Letters (RA-L) , 2021
2021
Later among the works it cites.
L. Yang, X. Zhan, K. Li, W. Xu, J. Li, and C. Lu, “Cpf: Learning a contact potential field to model the hand-object interaction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 11 097–11 106
2021
Later among the works it cites.
T. Zhu, R. Wu, X. Lin, and Y. Sun, “Toward human-like grasp: Dexterous grasping via semantic representation of object-hand,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 741–15 751
2021
Later among the works it cites.
2021
Later among the works it cites.
H. Fang, H.-S. Fang, S. Xu, and C. Lu, “Transcg: A large-scale real-world dataset for transparent object depth completion and a grasping baseline,” IEEE Robotics and Automation Letters , pp. 1–8, 2022
2022
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K. Li, N. Baron, X. Zhang, and N. Rojas, “Efficientgrasp: A unified data-efficient learning to grasp method for multi-fingered robot hands,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 8619–8626, 2022
2022
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D. Turpin, L. Wang, E. Heiden, Y.-C. Chen, M. Macklin, S. Tsogkas, S. Dickinson, and A. Garg, “Grasp’d: Differentiable contact-rich grasp synthesis for multi-fingered hands,” 2022
2022
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S. Hampali, S. D. Sarkar, M. Rad, and V. Lepetit, “Keypoint transformer: Solving joint identification in challenging hands and object interactions for accurate 3d pose estimation,” in CVPR , 2022
2022
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2022
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