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In-context imitation learning (ICIL) is a new paradigm that enables robots to generalize from demonstrations to unseen tasks without retraining.
B. K. Horn, “The curve of least energy,” TOMS , 1983
1983
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
A. Vaswani, “Attention is all you need,” NeurIPS , 2017
2017
Earlier work this paper cites.
A. Van Den Oord, O. Vinyals, et al. , “Neural discrete representation learning,” NeurIPS , 2017
2017
Earlier work this paper cites.
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, “Spectral normalization for generative adversarial networks,” in ICML , 2018
2018
Earlier work this paper cites.
Y. Chandak, G. Theocharous, J. Kostas, S. Jordan, and P. Thomas, “Learning action representations for reinforcement learning,” in ICML , 2019
2019
Earlier work this paper cites.
P. Zech, E. Renaudo, S. Haller, X. Zhang, and J. Piater, “Action representations in robotics: A taxonomy and systematic classification,” IJRR , 2019
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. , “Language models are few-shot learners,” NeurIPS , 2020
2020
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 CoRL , 2021
2021
Earlier work this paper cites.
S. Mysore, B. Mabsout, R. Mancuso, and K. Saenko, “Regularizing action policies for smooth control with reinforcement learning,” in ICRA , 2021
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
Earlier work this paper cites.
A. Allshire, R. Martín-Martín, C. Lin, S. Manuel, S. Savarese, and A. Garg, “Laser: Learning a latent action space for efficient reinforcement learning,” in ICRA , 2021
2021
Earlier work this paper cites.
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 ICML , 2021
2021
Earlier work this paper cites.
P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in CVPR , 2021
2021
Earlier work this paper cites.
2022
Earlier work this paper cites.
H.-T. D. Liu, F. Williams, A. Jacobson, S. Fidler, and O. Litany, “Learning smooth neural functions via lipschitz regularization,” in SIGGRAPH , 2022
2022
Earlier work this paper cites.
C. Duan, J. Ding, S. Chen, Z. Yu, and T. Huang, “Temporal effective batch normalization in spiking neural networks,” NeurIPS , 2022
2022
Earlier work this paper cites.
N. M. Shafiullah, Z. Cui, A. A. Altanzaya, and L. Pinto, “Behavior transformers: Cloning k k modes with one stone,” NeurIPS , 2022
2022
Earlier work this paper cites.
M. Alwani, Y. Wang, and V. Madhavan, “Decore: Deep compression with reinforcement learning,” in CVPR , 2022
2022
Cited alongside, same era.
D. Zhang, N. Malkin, Z. Liu, A. Volokhova, A. Courville, and Y. Bengio, “Generative flow networks for discrete probabilistic modeling,” in ICML , 2022
2022
Cited alongside, same era.
T. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” RSS , 2023
2023
Cited alongside, same era.
X. Song, J. Duan, W. Wang, S. E. Li, C. Chen, B. Cheng, B. Zhang, J. Wei, and X. S. Wang, “Lipsnet: a smooth and robust neural network with adaptive lipschitz constant for high accuracy optimal control,” in ICLR , 2023
2023
Cited alongside, same era.
S. Mirchandani, F. Xia, P. Florence, B. Ichter, D. Driess, M. G. Arenas, K. Rao, D. Sadigh, and A. Zeng, “Large language models as general pattern machines,” in Conference on Robot Learning . PMLR, 2023, pp. 2498–2518
S. Fujimoto, W.-D. Chang, E. Smith, S. S. Gu, D. Precup, and D. Meger, “For sale: State-action representation learning for deep reinforcement learning,” NeurIPS , 2024
2024
Later among the works it cites.
L. Yu, J. Lezama, N. B. Gundavarapu, L. Versari, K. Sohn, D. Minnen, Y. Cheng, A. Gupta, X. Gu, A. G. Hauptmann, et al. , “Language model beats diffusion-tokenizer is key to visual generation,” in ICLR , 2024
2024
Later among the works it cites.
H. Bharadhwaj, J. Vakil, M. Sharma, A. Gupta, S. Tulsiani, and V. Kumar, “Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking,” in ICRA , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
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2023
Cited alongside, same era.
V. Vosylius and E. Johns, “Few-shot in-context imitation learning via implicit graph alignment,” in CoRL , 2023
2023
Cited alongside, same era.
J. Watson and J. Peters, “Inferring smooth control: Monte carlo posterior policy iteration with gaussian processes,” in CoRL , 2023
2023
Cited alongside, same era.
A. Mandlekar, S. Nasiriany, B. Wen, I. Akinola, Y. Narang, L. Fan, Y. Zhu, and D. Fox, “Mimicgen: A data generation system for scalable robot learning using human demonstrations,” in CoRL , 2023
2023
Cited alongside, same era.
2024
Cited alongside, same era.
O’Neill et al. , “Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0,” in ICRA , 2024
2024
Cited alongside, same era.
A. Khazatsky et al. , “DROID: A large-scale in-the-wild robot manipulation dataset,” in RSS , 2024
2024
Cited alongside, same era.
2024
Cited alongside, same era.
S. Nasiriany, A. Maddukuri, L. Zhang, A. Parikh, A. Lo, A. Joshi, A. Mandlekar, and Y. Zhu, “Robocasa: Large-scale simulation of household tasks for generalist robots,” in RSS , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
T. Kwon, N. Di Palo, and E. Johns, “Language models as zero-shot trajectory generators,” IEEE Robotics and Automation Letters , 2024
2024
Later among the works it cites.
M. Xu, Z. Xu, Y. Xu, C. Chi, G. Wetzstein, M. Veloso, and S. Song, “Flow as the cross-domain manipulation interface,” in CoRL , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
J. Styrud, M. Mayr, E. Hellsten, V. Krueger, and C. Smith, “Bebop-combining reactive planning and bayesian optimization to solve robotic manipulation tasks,” in ICRA , 2024
2024
Later among the works it cites.
V. Vosylius and E. Johns, “Instant policy: In-context imitation learning via graph diffusion,” in ICLR , 2025
2025
Closest in time.
C. F. Park, A. Lee, E. S. Lubana, Y. Yang, M. Okawa, K. Nishi, M. Wattenberg, and H. Tanaka, “In-context learning of representations,” in ICLR , 2025
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
X. Zhang, S. Liu, P. Huang, W. J. Han, Y. Lyu, M. Xu, and D. Zhao, “Dynamics as prompts: In-context learning for sim-to-real system identifications,” RA-L , 2025
2025
Closest in time.
G. Jiang, Y. Sun, T. Huang, H. Li, Y. Liang, and H. Xu, “Robots pre-train robots: Manipulation-centric robotic representation from large-scale robot dataset,” in ICLR , 2025
2025
Closest in time.