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Imitation Learning (IL) is a sample efficient paradigm for robot learning using expert demonstrations.
Efficient reductions for imitation learning
S. Ross and D. Bagnell · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. Gordon, and D. Bagnell · 2011
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Keyframe-based learning from demonstration: Method and evaluation
B. Akgun, M. Cakmak, K. Jiang, and A. L. Thomaz · 2012
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Discovering event structure in continuous narrative perception and memory, 2016
C. Baldassano, J. Chen, A. Zadbood, J. Pillow, U. Hasson, and K. Norman · 2016
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Constructing experience: Event models from perception to action
L. L. Richmond and J. M. Zacks · 2017
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Dart: Noise injection for robust imitation learning
M. Laskey, J. Lee, R. Fox, A. Dragan, and K. Goldberg · 2017
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Hindsight experience replay
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. Pieter Abbeel, and W. Zaremba · 2017
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A framework for robotic clothing assistance by imitation learning
R. P. Joshi, N. Koganti, and T. Shibata · 2019
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Uncertainty-aware imitation learning using kernelized movement primitives
J. Silvério, Y. Huang, F. J. Abu-Dakka, L. Rozo, and D. G. Caldwell · 2019
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Uncertainty-aware data aggregation for deep imitation learning
Y. Cui, D. Isele, S. Niekum, and K. Fujimura · 2019
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Hg-dagger: Interactive imitation learning with human experts
M. Kelly, C. Sidrane, K. Driggs-Campbell, and M. J. Kochenderfer · 2019
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CompILE: Compositional imitation learning and execution
T. Kipf, Y. Li, H. Dai, V. Zambaldi, A. Sanchez-Gonzalez, E. Grefenstette, P. Kohli, and P. Battaglia · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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Human-in-the-loop imitation learning using remote teleoperation
A. Mandlekar, D. Xu, R. Martín-Martín, Y. Zhu, L. Fei-Fei, and S. Savarese · 2020
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Accelerating reinforcement learning with learned skill priors
K. Pertsch, Y. Lee, and J. J. Lim · 2020
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Learning latent plans from play
C. Lynch, M. Khansari, T. Xiao, V. Kumar, J. Tompson, S. Levine, and P. Sermanet · 2020
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
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Feedback in imitation learning: The three regimes of covariate shift
J. Spencer, S. Choudhury, A. Venkatraman, B. Ziebart, and J. A. Bagnell · 2021
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Implicit behavioral cloning
P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson · 2022
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Perceiver-actor: A multi-task transformer for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2022
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Learning visuo-haptic skewering strategies for robot-assisted feeding
P. Sundaresan, S. Belkhale, and D. Sadigh · 2022
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Eliciting compatible demonstrations for multi-human imitation learning
K. Gandhi, S. Karamcheti, M. Liao, and D. Sadigh · 2022
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Thriftydagger: Budget-aware novelty and risk gating for interactive imitation learning
R. Hoque, A. Balakrishna, E. Novoseller, A. Wilcox, D. S. Brown, and K. Goldberg · 2022
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Mind meld: Personalized meta-learning for robot-centric imitation learning
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A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín · 2021
Cited alongside, same era.
Coarse-to-fine imitation learning: Robot manipulation from a single demonstration
E. Johns · 2021
Cited alongside, same era.
Polymetis
Y. Lin, A. S. Wang, G. Sutanto, A. Rai, and F. Meier · 2021
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Masked autoencoders are scalable vision learners
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick · 2022
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Lamda: Language models for dialog applications
R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H.-T. Cheng, A. Jin, T. Bos, L. Baker, Y. Du, et al · 2022
Cited alongside, same era.
Palm: Scaling language modeling with pathways
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al · 2022
Cited alongside, same era.
S. Reed, K. Zolna, E. Parisotto, S. G. Colmenarejo, A. Novikov, G. Barth-Maron, M. Gimenez, Y. Sulsky, J. Kay, J. T. Springenberg, et al · 2022
Cited alongside, same era.
M. L. Schrum, E. Hedlund-Botti, N. Moorman, and M. C. Gombolay · 2022
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R3m: A universal visual representation for robot manipulation
S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta · 2022
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Perceiver-actor: A multi-task transformer for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2022
Later among the works it cites.
Learning multi-stage tasks with one demonstration via self-replay
N. Di Palo and E. Johns · 2022
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Understanding hindsight goal relabeling requires rethinking divergence minimization
L. Zhang and B. C. Stadie · 2022
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Palm-e: An embodied multimodal language model
D. Driess, F. Xia, M. S. M. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, W. Huang, Y. Chebotar, P. Sermanet, D. Duckworth, S. Levine, V. Vanhoucke, K. Hausman, M. Toussaint, K. Greff, A. Zeng, I. Mordatch, and P. Florence · 2023
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Language-driven representation learning for robotics
S. Karamcheti, S. Nair, A. S. Chen, T. Kollar, C. Finn, D. Sadigh, and P. Liang · 2023
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Diffusion policy: Visuomotor policy learning via action diffusion
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song · 2023
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Data quality in imitation learning, 2023
S. Belkhale, Y. Cui, and D. Sadigh · 2023
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