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Grasping objects in cluttered environments remains a fundamental yet challenging problem in robotic manipulation.
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M. Kiatos, I. Sarantopoulos, L. Koutras, S. Malassiotis, and Z. Doulgeri, “Learning Push-Grasping in Dense Clutter,”
2022
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2022
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P. K. Murali, A. Dutta, M. Gentner, E. Burdet, R. Dahiya, and M. Kaboli, “Active visuo-tactile interactive robotic perception for accurate object pose estimation in dense clutter,”
2022
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W. Wan, H. Geng, Y. Liu, Z. Shan, Y. Yang, L. Yi, and H. Wang, “UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning,” Apr. 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, T. Liu, L. Yi, and H. Wang, “UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy,” Mar. 2023
2023
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2023
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2024
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2024
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2024
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X. Wang and Q. Xu, “Transferring grasping across grippers: Learning–optimization hybrid framework for generalized planar grasp generation,”
2024
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2024
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M. Kiatos, L. Koutras, I. Sarantopoulos, and Z. Doulgeri, “Learning a pre-grasp manipulation policy to effectively retrieve a target in dense clutter,” in
2024
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