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Recently, diffusion policy has shown impressive results in handling multi-modal tasks in robotic manipulation.
J. Carvalho, A. T. Le, M. Baierl, D. Koert, and J. Peters, “Motion planning diffusion: Learning and planning of robot motions with diffusion models,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 1916–1923
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S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” Journal of Machine Learning Research , vol. 17, no. 39, pp. 1–40, 2016
2016
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2018
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M. Kelly, C. Sidrane, K. Driggs-Campbell, and M. J. Kochenderfer, “HG-DAgger: Interactive imitation learning with human experts,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 8077–8083
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Y. Zhou, C. Barnes, J. Lu, J. Yang, and H. Li, “On the continuity of rotation representations in neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 5745–5753
2019
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Cited alongside, same era.
R. Hoque, A. Balakrishna, E. Novoseller, A. Wilcox, D. S. Brown, and K. Goldberg, “ThriftyDAgger: Budget-aware novelty and risk gating for interactive imitation learning,” in Conference on Robot Learning , 2021
2021
Cited alongside, same era.
A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín, “What matters in learning from offline human demonstrations for robot manipulation,” 2021
2021
Cited alongside, same era.
S. Dass, K. Pertsch, H. Zhang, Y. Lee, J. J. Lim, and S. Nikolaidis, “Pato: Policy assisted teleoperation for scalable robot data collection,” in Proceedings of Robotics: Science and Systems , 2023
2023
Later among the works it cites.
H. Liu, S. Dass, R. Martín-Martín, and Y. Zhu, “Model-based runtime monitoring with interactive imitation learning,” in International Conference on Robotics and Automation (ICRA) , 2023
2023
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A. C. Li, M. Prabhudesai, S. Duggal, E. Brown, and D. Pathak, “Your diffusion model is secretly a zero-shot classifier,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2206–2217
2023
Later among the works it cites.
S. E. Ada, E. Oztop, and E. Ugur, “Diffusion policies for out-of-distribution generalization in offline reinforcement learning,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
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J. Wong, A. Tung, A. Kurenkov, A. Mandlekar, L. Fei-Fei, S. Savarese, and R. Martín-Martín, “Error-aware imitation learning from teleoperation data for mobile manipulation,” in Conference on Robot Learning , 2022, pp. 1367–1378
2022
Cited alongside, same era.
T. Salimans and J. Ho, “Progressive distillation for fast sampling of diffusion models,” 2022
2022
Cited alongside, same era.
S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta, “R3M: A universal visual representation for robot manipulation,” 2022
2022
Cited alongside, same era.
T. Z. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware,” in Proceedings of Robotics: Science and Systems , July 2023
2023
Cited alongside, same era.
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 , July 2023
2023
Cited alongside, same era.
R. Hoque, L. Y. Chen, S. Sharma, K. Dharmarajan, B. Thananjeyan, P. Abbeel, and K. Goldberg, “Fleet-dagger: Interactive robot fleet learning with scalable human supervision,” in Conference on Robot Learning , 2023, pp. 368–380
2023
Cited alongside, same era.
S. Zhou, Y. Du, S. Zhang, M. Xu, Y. Shen, W. Xiao, D.-Y. Yeung, and C. Gan, “Adaptive online replanning with diffusion models,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
B. Wang, G. Wu, T. Pang, Y. Zhang, and Y. Yin, “Diffail: Diffusion adversarial imitation learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 14, 2024, pp. 15 447–15 455
2024
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2024
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C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song, “Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots,” in Proceedings of Robotics: Science and Systems (RSS) , 2024
2024
Closest in time.
Y. Ze, G. Zhang, K. Zhang, C. Hu, M. Wang, and H. Xu, “3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations,” in Proceedings of Robotics: Science and Systems (RSS) , 2024
2024
Closest in time.