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We propose DINOBot, a novel imitation learning framework for robot manipulation, which leverages the image-level and pixel-level capabilities of features extracted from Vision Transformers trained with DINO.
K. Alton and M. van de Panne, “Learning to steer on winding tracks using semi-parametric control policies,” in Proceedings of the 2005 IEEE International Conference on Robotics and Automation , 2005, pp. 4588–4593
2005
Earlier work this paper cites.
D. Sharon and M. van de Panne, “Synthesis of controllers for stylized planar bipedal walking,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation , pp. 2387–2392, 2005
2005
Earlier work this paper cites.
S. Oomori, T. Nishida, and S. Kurogi, “Point cloud matching using singular value decomposition,” Artificial Life and Robotics , vol. 21, pp. 149–154, 06 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
O. Sorkine-Hornung and M. Rabinovich, “Least-squares rigid motion using svd,” Computing , vol. 1, no. 1, pp. 1–5, 2017
2017
Earlier work this paper cites.
D. Shah and Q. Xie, “Q-learning with nearest neighbors,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Earlier work this paper cites.
E. Mansimov and K. Cho, “Simple nearest neighbor policy method for continuous control tasks,” 2018. [Online]. Available: https://openreview.net/forum?id=ByL48G-AW
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
M. Argus, L. Hermann, J. Long, and T. Brox, “Flowcontrol: Optical flow based visual servoing,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 7534–7541
2020
Earlier work this paper cites.
Z. Teed and J. Deng, “Raft: Recurrent all-pairs field transforms for optical flow,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16 . Springer, 2020, pp. 402–419
2020
Earlier work this paper cites.
H. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 11 441–11 450, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:219964473
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 9650–9660
2021
Cited alongside, same era.
2021
Cited alongside, same era.
M. Vecerik, J.-B. Regli, O. Sushkov, D. Barker, R. Pevceviciute, T. Rothörl, R. Hadsell, L. Agapito, and J. Scholz, “S3k: Self-supervised semantic keypoints for robotic manipulation via multi-view consistency,” in Conference on Robot Learning . PMLR, 2021, pp. 449–460
2021
Cited alongside, same era.
2021
I. Radosavovic, T. Xiao, S. James, P. Abbeel, J. Malik, and T. Darrell, “Real-world robot learning with masked visual pre-training,” in Conference on Robot Learning . PMLR, 2023, pp. 416–426
2023
Later among the works it cites.
Y. Seo, D. Hafner, H. Liu, F. Liu, S. James, K. Lee, and P. Abbeel, “Masked world models for visual control,” in Conference on Robot Learning . PMLR, 2023, pp. 1332–1344
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
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Cited alongside, same era.
E. Johns, “Coarse-to-fine imitation learning: Robot manipulation from a single demonstration,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 4613–4619
2021
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=YicbFdNTTy
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Cited alongside, same era.
2022
Cited alongside, same era.
S. Izquierdo, M. Argus, and T. Brox, “Conditional visual servoing for multi-step tasks,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 2190–2196
2022
Cited alongside, same era.
L. Manuelli, W. Gao, P. Florence, and R. Tedrake, “kpam: Keypoint affordances for category-level robotic manipulation,” in Robotics Research: The 19th International Symposium ISRR . Springer, 2022, pp. 132–157
2022
Cited alongside, same era.
W. Goodwin, I. Havoutis, and I. Posner, “You only look at one: Category-level object representations for pose estimation from a single example,” in 6th Annual Conference on Robot Learning , 2022. [Online]. Available: https://openreview.net/forum?id=lb7B5Rw7tjw
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Later among the works it cites.
D. Hadjivelichkov, S. Zwane, L. Agapito, M. P. Deisenroth, and D. Kanoulas, “One-shot transfer of affordance regions? affcorrs!” in Conference on Robot Learning . PMLR, 2023, pp. 550–560
2023
Later among the works it cites.
V. Vosylius and E. Johns, “Few-shot in-context imitation learning via implicit graph alignment,” in Conference on Robot Learning . PMLR, 2023, pp. 3194–3213
2023
Later among the works it cites.
Y. Wang, Z. Li, M. Zhang, K. Driggs-Campbell, J. Wu, L. Fei-Fei, and Y. Li, “D 3 fields: Dynamic 3d descriptor fields for zero-shot generalizable robotic manipulation,” 2023
2023
Later among the works it cites.
Q. Wang, H. Zhang, C. Deng, Y. You, H. Dong, Y. Zhu, and L. Guibas, “Sparsedff: Sparse-view feature distillation for one-shot dexterous manipulation,” 2023
2023
Later among the works it cites.
P. Vitiello, K. Dreczkowski, and E. Johns, “One-shot imitation learning: A pose estimation perspective,” in Conference on Robot Learning . PMLR, 2023, pp. 943–970
2023
Later among the works it cites.
A. Simeonov, Y. Du, Y.-C. Lin, A. R. Garcia, L. P. Kaelbling, T. Lozano-Pérez, and P. Agrawal, “Se (3)-equivariant relational rearrangement with neural descriptor fields,” in Conference on Robot Learning . PMLR, 2023, pp. 835–846
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Ju, K. Hu, G. Zhang, G. Zhang, M. Jiang, and H. Xu, “Robo-abc: Affordance generalization beyond categories via semantic correspondence for robot manipulation,” 2024
2024
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