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Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action trajectories.
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2023
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2024
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2024
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A. Gu and T. Dao, “Mamba: Linear-time sequence modeling with selective state spaces,”
2023
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C. Bao, H. Xu, Y. Qin, and X. Wang, “Dexart: Benchmarking generalizable dexterous manipulation with articulated objects,” in
2023
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T. Gervet, Z. Xian, N. Gkanatsios, and K. Fragkiadaki, “Act3d: Infinite resolution action detection transformer for robotic manipulation,”
2023
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K. Rana, J. Haviland, S. Garg, J. Abou-Chakra, I. Reid, and N. Suenderhauf, “Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning,” in
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