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Diffusion models have seen rapid adoption in robotic imitation learning, enabling autonomous execution of complex dexterous tasks.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems , vol. 33. Curran Associates, Inc., pp. 6840–6851. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html
2020
Earlier work this paper cites.
M. Janner, Y. Du, J. Tenenbaum, and S. Levine, “Planning with diffusion for flexible behavior synthesis,” in Proceedings of the 39th International Conference on Machine Learning . PMLR, 2022, pp. 9902–9915, ISSN: 2640-3498. [Online]. Available: https://proceedings.mlr.press/v162/janner22a.html
2022
Earlier work this paper cites.
T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,” vol. 35, pp. 26 565–26 577. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2022/hash/a98846e9d9cc01cfb87eb694d946ce6b-Abstract-Conference.html
2022
Earlier work this paper cites.
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,” in Proceedings of the 5th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 08–11 Nov 2022, pp. 1678–1690. [Online]. Available: https://proceedings.mlr.press/v164/mandlekar22a.html
2022
Earlier work this paper cites.
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 2023 . Robotics: Science and Systems Foundation. [Online]. Available: http://www.roboticsproceedings.org/rss19/p026.pdf
2023
Earlier work this paper cites.
M. Reuss, M. Li, X. Jia, and R. Lioutikov, “Goal-conditioned imitation learning using score-based diffusion policies,” in Robotics: Science and Systems 2023 . Robotics: Science and Systems Foundation. [Online]. Available: http://www.roboticsproceedings.org/rss19/p028.pdf
2023
Earlier work this paper cites.
C. Meng, R. Rombach, R. Gao, D. Kingma, S. Ermon, J. Ho, and T. Salimans, “On distillation of guided diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 14 297–14 306
2023
Earlier work this paper cites.
Y. Du, C. Durkan, R. Strudel, J. B. Tenenbaum, S. Dieleman, R. Fergus, J. Sohl-Dickstein, A. Doucet, and W. S. Grathwohl, “Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and MCMC,” in International Conference on Machine Learning . PMLR, 2023, pp. 8489–8510
2023
Earlier work this paper cites.
J. Carvalho, A. 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) , 2023
2023
Earlier work this paper cites.
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 (CVPR) , 2023
2023
Earlier work this paper cites.
T. Pearce, T. Rashid, A. Kanervisto, D. Bignell, M. Sun, R. Georgescu, S. V. Macua, S. Z. Tan, I. Momennejad, K. Hofmann, and S. Devlin, “Imitating human behaviour with diffusion models,” in The Eleventh International Conference on Learning Representations , 2023
2023
Earlier work this paper cites.
H. Ha, P. Florence, and S. Song, “Scaling up and distilling down: Language-guided robot skill acquisition,” in Proceedings of The 7th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, J. Tan, M. Toussaint, and K. Darvish, Eds., vol. 229. PMLR, 06–09 Nov 2023, pp. 3766–3777. [Online]. Available: https://proceedings.mlr.press/v229/ha23a.html
2023
Earlier work this paper cites.
Z. Xian, N. Gkanatsios, T. Gervet, T.-W. Ke, and K. Fragkiadaki, “Chaineddiffuser: Unifying trajectory diffusion and keypose prediction for robotic manipulation,” in Proceedings of The 7th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, J. Tan, M. Toussaint, and K. Darvish, Eds., vol. 229. PMLR, 06–09 Nov 2023, pp. 2323–2339. [Online]. Available: https://proceedings.mlr.press/v229/xian23a.html
2023
Cited alongside, same era.
K. Sridhar, S. Dutta, D. Jayaraman, J. Weimer, and I. Lee, “Memory-consistent neural networks for imitation learning,” 2023
2023
Cited alongside, same era.
L. Chen, S. Bahl, and D. Pathak, “Playfusion: Skill acquisition via diffusion from language-annotated play,” in CoRL , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
L. Wang, J. Zhao, Y. Du, E. Adelson, and R. Tedrake, “PoCo: Policy Composition from and for Heterogeneous Robot Learning,” in Proceedings of Robotics: Science and Systems , Delft, Netherlands, July 2024
2024
Closest in time.
K. Chen, E. Lim, L. Kelvin, Y. Chen, and H. Soh, “Don’t Start From Scratch: Behavioral Refinement via Interpolant-based Policy Diffusion,” in Proceedings of Robotics: Science and Systems , Delft, Netherlands, July 2024
2024
Closest in time.
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 8th Annual Conference on Robot Learning , 2024. [Online]. Available: https://openreview.net/forum?id=gvdXE7ikHI
2024
Closest in time.
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 , Delft, Netherlands, July 2024
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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,” IEEE International Conference on Robotics and Automation (ICRA) , 2023
2023
Cited alongside, same era.
D. Kim, C.-H. Lai, W.-H. Liao, N. Murata, Y. Takida, T. Uesaka, Y. He, Y. Mitsufuji, and S. Ermon, “Consistency trajectory models: Learning probability flow ODE trajectory of diffusion,” in The Twelfth International Conference on Learning Representations , 2024. [Online]. Available: https://openreview.net/forum?id=ymjI8feDTD
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
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
Cited alongside, same era.
Y. Luo, C. Sun, J. B. Tenenbaum, and Y. Du, “Potential based diffusion motion planning,” in Forty-first International Conference on Machine Learning , 2024
2024
Cited alongside, same era.
K. Saha, V. Mandadi, J. Reddy, A. Srikanth, A. Agarwal, B. Sen, A. Singh, and M. Krishna, “Edmp: Ensemble-of-costs-guided diffusion for motion planning,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 10 351–10 358
2024
Cited alongside, same era.
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
Cited alongside, same era.
2024
Closest in time.
M. Reuss, Ömer Erdinç Yağmurlu, F. Wenzel, and R. Lioutikov, “Multimodal Diffusion Transformer: Learning Versatile Behavior from Multimodal Goals,” in Proceedings of Robotics: Science and Systems , Delft, Netherlands, July 2024
2024
Closest in time.
P. M. Scheikl, N. Schreiber, C. Haas, N. Freymuth, G. Neumann, R. Lioutikov, and F. Mathis-Ullrich, “Movement primitive diffusion: Learning gentle robotic manipulation of deformable objects,” IEEE Robotics and Automation Letters , vol. 9, no. 6, pp. 5338–5345, 2024
2024
Closest in time.
D. Ruhe, J. Heek, T. Salimans, and E. Hoogeboom, “Rolling diffusion models,” in Proceedings of the 41st International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, J. Scarlett, and F. Berkenkamp, Eds., vol. 235. PMLR, 21–27 Jul 2024, pp. 42 818–42 835. [Online]. Available: https://proceedings.mlr.press/v235/ruhe24a.html
2024
Closest in time.
Z. Zhang, R. Liu, R. Hanocka, and K. Aberman, “Tedi: Temporally-entangled diffusion for long-term motion synthesis,” in ACM SIGGRAPH 2024 Conference Papers , ser. SIGGRAPH ’24. New York, NY, USA: Association for Computing Machinery, 2024. [Online]. Available: https://doi.org/10.1145/3641519.3657515
2024
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2024
Closest in time.
T. Wu, Z. Fan, X. Liu, H.-T. Zheng, Y. Gong, Y. Shen, J. Jiao, J. Li, Z. Wei, J. Guo, N. Duan, and W. Chen, “Ar-diffusion: auto-regressive diffusion model for text generation,” in Proceedings of the 37th International Conference on Neural Information Processing Systems , ser. NIPS ’23. Red Hook, NY, USA: Curran Associates Inc., 2024
2024
Closest in time.