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3D perception ability is crucial for generalizable robotic manipulation.
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2023
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2023
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Cited alongside, same era.
O. Mees, L. Hermann, E. Rosete-Beas, and W. Burgard, “Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks,” IEEE Robotics and Automation Letters (RA-L) , vol. 7, no. 3, pp. 7327–7334, 2022
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K. Lin, C. Agia, T. Migimatsu, M. Pavone, and J. Bohg, “Text2motion: From natural language instructions to feasible plans,” Autonomous Robots , vol. 47, no. 8, pp. 1345–1365, 2023
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2023
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Octo Model Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, C. Xu, J. Luo, T. Kreiman, Y. Tan, D. Sadigh, C. Finn, and S. Levine, “Octo: An open-source generalist robot policy,” https://octo-models.github.io , 2023
2023
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Y.-L. Lee, Y.-H. Tsai, W.-C. Chiu, and C.-Y. Lee, “Multimodal prompting with missing modalities for visual recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 14 943–14 952
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2023
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2023
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C. Zheng and A. Vedaldi, “Online clustered codebook,” in Proceedings of International Conference on Computer Vision (ICCV) , October 2023
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B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone, “Libero: Benchmarking knowledge transfer for lifelong robot learning,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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Z. Yang, H. Zhang, Y. Wei, Z. Wang, F. Nie, and D. Hu, “Geometric-inspired graph-based incomplete multi-view clustering,” Pattern Recognition , vol. 147, p. 110082, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320323007793
2024
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