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This paper primarily focuses on evaluating and benchmarking the robustness of visual representations in the context of object assembly tasks.
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G. Wang, F. Manhardt, F. Tombari, and X. Ji, “GDR-Net: Geometry-guided direct regression network for monocular 6d object pose estimation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 16 611–16 621
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K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 000–16 009
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2023
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T. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” Robotics: Science and Systems (RSS) , 2023
2023
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C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” Robotics: Science and Systems (RSS) , 2023
2023
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