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Robots hold great promise for performing repetitive or hazardous tasks, but achieving human-like dexterity, especially in contact-rich and dynamic environments, remains challenging.
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 33, pp. 6840–6851, 2020
2020
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Y. Wang, C. C. Beltran-Hernandez, W. Wan, and K. Harada, “Robotic imitation of human assembly skills using hybrid trajectory and force learning,” in IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 11 278–11 284
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T. Kamijo, C. C. Beltran-Hernandez, and M. Hamaya, “Learning variable compliance control from a few demonstrations for bimanual robot with haptic feedback teleoperation system,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2024
2024
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X. Jia, D. Blessing, X. Jiang, M. Reuss, A. Donat, R. Lioutikov, and G. Neumann, “Towards diverse behaviors: A benchmark for imitation learning with human demonstrations,” in International Conference on Learning Representations (ICLR) , 2024. [Online]. Available: https://openreview.net/forum?id=6pPYRXKPpw
2024
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T. Z. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” in Robotics: Science and Systems (RSS) , 2023. [Online]. Available: https://doi.org/10.15607/RSS.2023.XIX.016
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 Robotics: Science and Systems (RSS) , 2023. [Online]. Available: https://doi.org/10.15607/RSS.2023.XIX.026
2023
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L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, and M.-H. Yang, “Diffusion models: A comprehensive survey of methods and applications,” ACM Computing Surveys , vol. 56, no. 4, pp. 1–39, 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 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 5923–5930
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 Robotics: Science and Systems (RSS) , 2023. [Online]. Available: https://doi.org/10.15607/RSS.2023.XIX.031
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2024
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M. Drolet, S. Stepputtis, S. Kailas, A. Jain, J. Peters, S. Schaal, and H. Ben Amor, “A comparison of imitation learning algorithms for bimanual manipulation,” IEEE Robotics and Automation Letters (RA-L) , vol. 9, no. 10, pp. 8579–8586, 2024
2024
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T. Z. Zhao, J. Tompson, D. Driess, P. Florence, S. K. S. Ghasemipour, C. Finn, and A. Wahid, “ALOHA Unleashed: A simple recipe for robot dexterity,” in Annual Conference on Robot Learning (CoRL) , 2024
2024
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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 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024, pp. 18 007–18 018
2024
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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 (RA-L) , vol. 9, no. 1, pp. 215–222, 2024
2024
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