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Grasp detection requires flexibility to handle objects of various shapes without relying on prior knowledge of the object, while also offering intuitive, user-guided control.
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S. Wang, Z. Zhou, and Z. Kan, “When transformer meets robotic grasping: Exploits context for efficient grasp detection,” IEEE robotics and automation letters , vol. 7, no. 3, pp. 8170–8177, 2022
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M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in Conference on robot learning . PMLR, 2022, pp. 894–906
2022
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2024
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2024
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Y. Xiong, B. Varadarajan, L. Wu, X. Xiang, F. Xiao, C. Zhu, X. Dai, D. Wang, F. Sun, F. Iandola et al. , “Efficientsam: Leveraged masked image pretraining for efficient segment anything,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 16 111–16 121
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Z. Wei, L. Chen, Y. Jin, X. Ma, T. Liu, P. Ling, B. Wang, H. Chen, and J. Zheng, “Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 28 619–28 630
2024
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