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Reinforcement learning is a powerful framework for robots to acquire skills from experience, but often requires a substantial amount of online data collection.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine · 1910
Earlier work this paper cites.
Efficient training of artificial neural networks for autonomous navigation
D. A. Pomerleau · 1991
Earlier work this paper cites.
Survey: Robot programming by demonstration
A. Billard, S. Calinon, R. Dillmann, and S. Schaal · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey · 2008
Earlier work this paper cites.
A survey of robot learning from demonstration
B. D. Argall, S. Chernova, M. Veloso, and B. Browning · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
Earlier work this paper cites.
Adapting deep visuomotor representations with weak pairwise constraints
E. Tzeng, C. Devin, J. Hoffman, C. Finn, P. Abbeel, S. Levine, K. Saenko, and T. Darrell · 2015
Earlier work this paper cites.
Guided cost learning: Deep inverse optimal control via policy optimization
C. Finn, S. Levine, and P. Abbeel · 2016
Earlier work this paper cites.
Generative adversarial imitation learning
J. Ho and S. Ermon · 2016
Earlier work this paper cites.
C. Finn, P. Christiano, P. Abbeel, and S. Levine · 2016
Earlier work this paper cites.
Unsupervised perceptual rewards for imitation learning
P. Sermanet, K. Xu, and S. Levine · 2016
Earlier work this paper cites.
Unsupervised cross-domain image generation
Y. Taigman, A. Polyak, and L. Wolf · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Earlier work this paper cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Earlier work this paper cites.
Learning robust rewards with adversarial inverse reinforcement learning
J. Fu, K. Luo, and S. Levine · 2017
Earlier work this paper cites.
Third-person imitation learning
B. C. Stadie, P. Abbeel, and I. Sutskever · 2017
Earlier work this paper cites.
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
M. Vecerik, T. Hester, J. Scholz, F. Wang, O. Pietquin, B. Piot, N. Heess, T. Rothörl, T. Lampe, and M. Riedmiller · 2017
Earlier work this paper cites.
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Unsupervised pixel-level domain adaptation with generative adversarial networks
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2017
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Supervised representation learning with double encoding-layer autoencoder for transfer learning
F. Zhuang, X. Cheng, P. Luo, S. J. Pan, and Q. He · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
D. S. Brown, W. Goo, P. Nagarajan, and S. Niekum · 2019
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Efficient exploration via state marginal matching
L. Lee, B. Eysenbach, E. Parisotto, E. Xing, S. Levine, and R. Salakhutdinov · 2019
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Imitation learning from video by leveraging proprioception
F. Torabi, G. Warnell, and P. Stone · 2019
Later among the works it cites.
Avid: Learning multi-stage tasks via pixel-level translation of human videos
L. Smith, N. Dhawan, M. Zhang, P. Abbeel, and S. Levine · 2019
Later among the works it cites.
Sqil: Imitation learning via reinforcement learning with sparse rewards
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Behavioral cloning from observation
F. Torabi, G. Warnell, and P. Stone · 2018
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Imitating latent policies from observation
A. D. Edwards, H. Sahni, Y. Schroecker, and C. L. Isbell · 2018
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Multiple interactions made easy (mime): Large scale demonstrations data for imitation
P. Sharma, L. Mohan, L. Pinto, and A. Gupta · 2018
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Deep imitation learning for complex manipulation tasks from virtual reality teleoperation
T. Zhang, Z. McCarthy, O. Jow, D. Lee, X. Chen, K. Goldberg, and P. Abbeel · 2018
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Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration
R. Rahmatizadeh, P. Abolghasemi, L. Bölöni, and S. Levine · 2018
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Generative adversarial imitation from observation
F. Torabi, G. Warnell, and P. Stone · 2018
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S. Reddy, A. D. Dragan, and S. Levine · 2019
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
X. B. Peng, A. Kumar, G. Zhang, and S. Levine · 2019
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Vr-goggles for robots: Real-to-sim domain adaptation for visual control
J. Zhang, L. Tai, P. Yun, Y. Xiong, M. Liu, J. Boedecker, and W. Burgard · 2019
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Third-person visual imitation learning via decoupled hierarchical controller
P. Sharma, D. Pathak, and A. Gupta · 2019
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End-to-end robotic reinforcement learning without reward engineering
A. Singh, L. Yang, K. Hartikainen, C. Finn, and S. Levine · 2019
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Concept2robot: Learning manipulation concepts from instructions and human demonstrations
L. Shao, T. Migimatsu, Q. Zhang, K. Yang, and J. Bohg · 2020
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Learning one-shot imitation from humans without humans
A. Bonardi, S. James, and A. J. Davison · 2020
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Accelerating online reinforcement learning with offline datasets
A. Nair, M. Dalal, A. Gupta, and S. Levine · 2020
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Estimating q (s, s’) with deep deterministic dynamics gradients
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Learning predictive models from observation and interaction
K. Schmeckpeper, A. Xie, O. Rybkin, S. Tian, K. Daniilidis, S. Levine, and C. Finn · 2020
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Semantic visual navigation by watching youtube videos
M. Chang, A. Gupta, and S. Gupta · 2020
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Rl-cyclegan: Reinforcement learning aware simulation-to-real
K. Rao, C. Harris, A. Irpan, S. Levine, J. Ibarz, and M. Khansari · 2020
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Visual transfer for reinforcement learning via wasserstein domain confusion
J. Roy and G. Konidaris · 2020
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Cross-domain imitation learning with a dual structure
S. Choi, S. Han, W. Kim, and Y. Sung · 2020
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Domain adaptive imitation learning
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