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We introduce DexDiffuser, a novel dexterous grasping method that generates, evaluates, and refines grasps on partial object point clouds.
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2021
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P. Li, T. Liu, Y. Li, Y. Geng, Y. Zhu, Y. Yang, and S. Huang, “Gendexgrasp: Generalizable dexterous grasping,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 8068–8074
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Cited alongside, same era.
J. Lundell, F. Verdoja, and V. Kyrki, “Ddgc: Generative deep dexterous grasping in clutter,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 6899–6906, 2021
2021
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P. Mandikal and K. Grauman, “Learning dexterous grasping with object-centric visual affordances,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 6169–6176
2021
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J. Ho and T. Salimans, “Classifier-free diffusion guidance,” in NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications , 2021
2021
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V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa et al. , “Isaac gym: High performance gpu based physics simulation for robot learning,” in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) , 2021
2021
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V. Mayer, Q. Feng, J. Deng, Y. Shi, Z. Chen, and A. Knoll, “Ffhnet: Generating multi-fingered robotic grasps for unknown objects in real-time,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 762–769
2022
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W. Wei, D. Li, P. Wang, Y. Li, W. Li, Y. Luo, and J. Zhong, “Dvgg: Deep variational grasp generation for dextrous manipulation,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1659–1666, 2022
2022
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J. Ho, C. Saharia, W. Chan, D. J. Fleet, M. Norouzi, and T. Salimans, “Cascaded diffusion models for high fidelity image generation,” The Journal of Machine Learning Research , vol. 23, no. 1, pp. 2249–2281, 2022
2022
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2022
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Y. Qin, B. Huang, Z.-H. Yin, H. Su, and X. Wang, “Dexpoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation,” in Conference on Robot Learning . PMLR, 2023, pp. 594–605
2023
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W. Wan, H. Geng, Y. Liu, Z. Shan, Y. Yang, L. Yi, and H. Wang, “Unidexgrasp++: Improving dexterous grasping policy learning via geometry-aware curriculum and iterative generalist-specialist learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3891–3902
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,” in Proceedings of Robotics: Science and Systems (RSS) , 2023
2023
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W. Liu, Y. Du, T. Hermans, S. Chernova, and C. Paxton, “Structdiffusion: Language-guided creation of physically-valid structures using unseen objects,” in Proceedings of Robotics: Science and Systems (RSS) , 2023
2023
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E. Ng, Z. Liu, and M. Kennedy, “Diffusion co-policy for synergistic human-robot collaborative tasks,” IEEE Robotics and Automation Letters , 2023
2023
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J. Urain, N. Funk, J. Peters, and G. Chalvatzaki, “Se(3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimization through diffusion,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 5923–5930
2023
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S. Huang, Z. Wang, P. Li, B. Jia, T. Liu, Y. Zhu, W. Liang, and S.-C. Zhu, “Diffusion-based generation, optimization, and planning in 3d scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 16 750–16 761
2023
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W. Liu, Y. Du, T. Hermans, S. Chernova, and C. Paxton, “Structdiffusion: Language-guided creation of physically-valid structures using unseen objects,” in RSS , vol. 1, 2023, p. 3
2023
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H. Ryu, J. Kim, H. An, J. Chang, J. Seo, T. Kim, Y. Kim, C. Hwang, J. Choi, and R. Horowitz, “Diffusion-edfs: Bi-equivariant denoising generative modeling on se (3) for visual robotic manipulation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 18 007–18 018
2024
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L. Chen, S. Bahl, and D. Pathak, “Playfusion: Skill acquisition via diffusion from language-annotated play,” in Conference on Robot Learning . PMLR, 2023, pp. 2012–2029
2029
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A. Simeonov, A. Goyal, L. Manuelli, Y.-C. Lin, A. Sarmiento, A. R. Garcia, P. Agrawal, and D. Fox, “Shelving, stacking, hanging: Relational pose diffusion for multi-modal rearrangement,” in Conference on Robot Learning . PMLR, 2023, pp. 2030–2069
2069
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