FineGrasp: Towards Robust Grasping for Delicate Objects

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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

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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 , 2021, pp. 15 964–15 973

2021

Earlier work this paper cites.

M. Haoxiang and D. Huang, “Towards scale balanced 6-dof grasp detection in cluttered scenes,” in Conference on Robot Learning (CoRL) , 2022

2022

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H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y. Xie, and C. Lu, “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,” IEEE Transactions on Robotics , vol. 39, no. 5, pp. 3929–3945, 2023

2023

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S. Chen, W. Tang, P. Xie, W. Yang, and G. Wang, “Efficient heatmap-guided 6-dof grasp detection in cluttered scenes,” IEEE Robotics and Automation Letters , vol. 8, no. 8, pp. 4895–4902, 2023

2023

Cited alongside, same era.

X.-M. Wu, J.-F. Cai, J.-J. Jiang, D. Zheng, Y.-L. Wei, and W.-S. Zheng, “An economic framework for 6-dof grasp detection,” in European Conference on Computer Vision . Springer, 2024, pp. 357–375

2024

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H. Huang, F. Lin, Y. Hu, S. Wang, and Y. Gao, “Copa: General robotic manipulation through spatial constraints of parts with foundation models,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 9488–9495

2024

Cited alongside, same era.

Y. Qian, X. Zhu, O. Biza, S. Jiang, L. Zhao, H. Huang, Y. Qi, and R. Platt, “Thinkgrasp: A vision-language system for strategic part grasping in clutter,” arXiv preprint arXiv:2407.11298 , 2024

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2024

Cited alongside, same era.

J. Zhang, H. Liu, D. Li, X. Yu, H. Geng, Y. Ding, J. Chen, and H. Wang, “Dexgraspnet 2.0: Learning generative dexterous grasping in large-scale synthetic cluttered scenes,” in 8th Annual Conference on Robot Learning , 2024

2024

Later among the works it cites.

P. Liu, Y. Orru, J. Vakil, C. Paxton, N. M. M. Shafiullah, and L. Pinto, “Ok-robot: What really matters in integrating open-knowledge models for robotics,” arXiv preprint arXiv:2401.12202 , 2024

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2024

Later among the works it cites.

M. Pan, J. Zhang, T. Wu, Y. Zhao, W. Gao, and H. Dong, “Omnimanip: Towards general robotic manipulation via object-centric interaction primitives as spatial constraints,” arXiv preprint arXiv:2501.03841 , 2025

Original

2025

Closest in time.

H. Yuan, X. Li, T. Zhang, Z. Huang, S. Xu, S. Ji, Y. Tong, L. Qi, J. Feng, and M.-H. Yang, “Sa2va: Marrying sam2 with llava for dense grounded understanding of images and videos,” arXiv preprint arXiv:2501.04001 , 2025

Original

2025

Closest in time.