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Functional grasping is essential for humans to perform specific tasks, such as grasping scissors by the finger holes to cut materials or by the blade to safely hand them over.
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2021
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2021
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S. Christen, M. Kocabas, E. Aksan, J. Hwangbo, J. Song, and O. Hilliges, “D-grasp: Physically plausible dynamic grasp synthesis for hand-object interactions,” in Computer Vision and Pattern Recognition (CVPR) , 2022
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Q. Liu, Y. Cui, Q. Ye, Z. Sun, H. Li, G. Li, L. Shao, and J. Chen, “DexRepNet: Learning dexterous robotic grasping network with geometric and spatial hand-object representations,” in International Conference on Intelligent Robots and Systems (IROS) , 2023
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R. Wang, J. Zhang, J. Chen, Y. Xu, P. Li, T. Liu, and H. Wang, “Dexgraspnet: A large-scale robotic dexterous grasp dataset for general objects based on simulation,” in International Conference on Robotics and Automation (ICRA) , 2023
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H. Zhang, S. Christen, Z. Fan, O. Hilliges, and J. Song, “GraspXL: Generating grasping motions for diverse objects at scale,” in European Conference on Computer Vision (ECCV) , 2024
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B. Wen, W. Yang, J. Kautz, and S. Birchfield, “FoundationPose: Unified 6d pose estimation and tracking of novel objects,” in Computer Vision and Pattern Recognition (CVPR) , 2024
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2022
Cited alongside, same era.
T. Chen, J. Xu, and P. Agrawal, “A system for general in-hand object re-orientation,” Conference on Robot Learning (CoRL) , 2022
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P. Mandikal and K. Grauman, “Dexvip: Learning dexterous grasping with human hand pose priors from video,” in Conference on Robot Learning (CoRL) , 2022
2022
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Y. Qin, Y.-H. Wu, S. Liu, H. Jiang, R. Yang, Y. Fu, and X. Wang, “Dexmv: Imitation learning for dexterous manipulation from human videos,” in European Conference on Computer Vision (ECCV) , 2022
2022
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2023
Cited alongside, same era.
Y. Qin, B. Huang, Z.-H. Yin, H. Su, and X. Wang, “Dexpoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation,” in Conference on Robot Learning (CoRL) , 2023
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A. Agarwal, S. Uppal, K. Shaw, and D. Pathak, “Dexterous functional grasping,” in Conference on Robot Learning (CoRL) , 2023
2023
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Y. Xu, W. Wan, J. Zhang, H. Liu, Z. Shan, H. Shen, R. Wang, H. Geng, Y. Weng, J. Chen et al. , “Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy,” in Computer Vision and Pattern Recognition (CVPR) , 2023
2023
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H. Zhang, S. Christen, Z. Fan, L. Zheng, J. Hwangbo, J. Song, and O. Hilliges, “ArtiGrasp: Physically plausible synthesis of bi-manual dexterous grasping and articulation,” in International Conference on 3D Vision (3DV) , 2024
2024
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2024
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Y. Ze, G. Zhang, K. Zhang, C. Hu, M. Wang, and H. Xu, “3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations,” in Robotics: Science and Systems (RSS) , 2024
2024
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Y. Chen, C. Wang, Y. Yang, and K. Liu, “Object-centric dexterous manipulation from human motion data,” in Conference on Robot Learning (CoRL) , 2024
2024
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Y. Li, B. Liu, Y. Geng, P. Li, Y. Yang, Y. Zhu, T. Liu, and S. Huang, “Grasp multiple objects with one hand,” Robotics and Automation Letters (RA-L) , 2024
2024
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F. Ceola, L. Rosasco, and L. Natale, “Resprect: Speeding-up multi-fingered grasping with residual reinforcement learning,” Robotics and Automation Letters (RA-L) , 2024
2024
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2024
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2024
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S. Yang, M. Liu, Y. Qin, D. Runyu, L. Jialong, X. Cheng, R. Yang, S. Yi, and X. Wang, “Ace: A cross-platfrom visual-exoskeletons for low-cost dexterous teleoperation,” arXiv preprint arXiv:240 , 2024
2024
Closest in time.