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Generating dexterous grasping has been a long-standing and challenging robotic task.
A. T. Miller and P. K. Allen, “Graspit! a versatile simulator for robotic grasping,” IEEE Robotics & Automation Magazine , vol. 11, no. 4, pp. 110–122, 2004
2004
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M. Ciocarlie, C. Goldfeder, and P. Allen, “Dimensionality reduction for hand-independent dexterous robotic grasping,” in International Conference on Intelligent Robots and Systems (IROS) , 2007
2007
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C. Goldfeder, M. Ciocarlie, H. Dang, and P. K. Allen, “The columbia grasp database,” in International Conference on Robotics and Automation (ICRA) , 2009
2009
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T. Schmidt, R. A. Newcombe, and D. Fox, “Dart: Dense articulated real-time tracking.” in Robotics: Science and Systems (RSS) , 2014
2014
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K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” in Advances in Neural Information Processing Systems (NeurIPS) , 2015
2015
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T. Feix, J. Romero, H.-B. Schmiedmayer, A. M. Dollar, and D. Kragic, “The grasp taxonomy of human grasp types,” IEEE Transactions on Human-machine Systems , vol. 46, no. 1, pp. 66–77, 2015
2015
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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 Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
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J. Romero, D. Tzionas, and M. J. Black, “Embodied hands: modeling and capturing hands and bodies together,” ACM Transactions on Graphics (TOG) , vol. 36, no. 6, pp. 1–17, 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,” International Journal of Robotics Research (IJRR) , vol. 36, no. 3, pp. 261–268, 2017
2017
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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
Earlier work this paper cites.
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
Earlier work this paper cites.
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
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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
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A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in International Conference on Computer Vision (ICCV) , 2019
2019
Earlier work this paper cites.
A. Nagabandi, K. Konolige, S. Levine, and V. Kumar, “Deep dynamics models for learning dexterous manipulation,” in Conference on Robot Learning (CoRL) , 2020
2020
Earlier work this paper cites.
M. Kokic, D. Kragic, and J. Bohg, “Learning task-oriented grasping from human activity datasets,” IEEE Robotics and Automation Letters (RA-L) , vol. 5, no. 2, pp. 3352–3359, 2020
2020
Cited alongside, same era.
L. Shao, F. Ferreira, M. Jorda, V. Nambiar, J. Luo, E. Solowjow, J. A. Ojea, O. Khatib, and J. Bohg, “Unigrasp: Learning a unified model to grasp with multifingered robotic hands,” IEEE Robotics and Automation Letters (RA-L) , 2020
2020
Cited alongside, same era.
S. Brahmbhatt, C. Tang, C. D. Twigg, C. C. Kemp, and J. Hays, “Contactpose: A dataset of grasps with object contact and hand pose,” in European Conference on Computer Vision (ECCV) , 2020
2020
Cited alongside, same era.
O. Taheri, N. Ghorbani, M. J. Black, and D. Tzionas, “Grab: A dataset of whole-body human grasping of objects,” in European Conference on Computer Vision (ECCV) , 2020
2020
Cited alongside, same era.
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 Conference on Computer Vision and Pattern Recognition (CVPR) , 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, “Ddhc: Generative deep dexterous grasping in clutter,” IEEE Robotics and Automation Letters (RA-L) , vol. 6, no. 4, pp. 6899–6906, 2021
2021
Later among the works it cites.
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S. Hampali, M. Rad, M. Oberweger, and V. Lepetit, “Honnotate: A method for 3d annotation of hand and object poses,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Cited alongside, same era.
M. Liu, Z. Pan, K. Xu, K. Ganguly, and D. Manocha, “Deep differentiable grasp planner for high-dof grippers,” in Robotics: Science and Systems (RSS) , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
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
Cited alongside, same era.
I. Radosavovic, X. Wang, L. Pinto, and J. Malik, “State-only imitation learning for dexterous manipulation,” in International Conference on Intelligent Robots and Systems (IROS) , 2021
2021
Cited alongside, same era.
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) , vol. 7, no. 1, pp. 470–477, 2021
2021
Cited alongside, same era.
Z. Xu, B. Qi, S. Agrawal, and S. Song, “Adagrasp: Learning an adaptive gripper-aware grasping policy,” in International Conference on Robotics and Automation (ICRA) , 2021
2021
Cited alongside, same era.
P. Grady, C. Tang, C. D. Twigg, M. Vo, S. Brahmbhatt, and C. C. Kemp, “Contactopt: Optimizing contact to improve grasps,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
Cited alongside, same era.
2021
Later among the works it cites.
2022
Closest in time.
P. Mandikal and K. Grauman, “Dexvip: Learning dexterous grasping with human hand pose priors from video,” in Conference on Robot Learning (CoRL) , 2022
2022
Closest in time.
2022
Closest in time.
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 (RA-L) , vol. 7, no. 4, pp. 8619–8626, 2022
2022
Closest in time.
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,” in European Conference on Computer Vision (ECCV) , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
O. Taheri, V. Choutas, M. J. Black, and D. Tzionas, “Goal: Generating 4d whole-body motion for hand-object grasping,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
Closest in time.
2022
Closest in time.
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 Conference on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
Closest in time.