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Humans are able to seamlessly visually imitate others, by inferring their intentions and using past experience to achieve the same end goal.
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End to end learning for self-driving cars
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Unsupervised perceptual rewards for imitation learning
P. Sermanet, K. Xu, and S. Levine · 2016
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Deep residual learning for image recognition
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End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2016
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One-shot imitation learning
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One-shot visual imitation learning via meta-learning
Third-person visual imitation learning via decoupled hierarchical controller
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Avid: Learning multi-stage tasks via pixel-level translation of human videos
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Imitation learning from observations by minimizing inverse dynamics disagreement
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C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine · 2017
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Attention is all you need
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T. Salimans, A. Karpathy, X. Chen, and D. P. Kingma · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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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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One-shot imitation from observing humans via domain-adaptive meta-learning
T. Yu, C. Finn, A. Xie, S. Dasari, T. Zhang, P. Abbeel, and S. Levine · 2018
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Non-local neural networks
X. Wang, R. Girshick, A. Gupta, and K. He · 2018
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Improvisation through physical understanding: Using novel objects as tools with visual foresight
A. Xie, F. Ebert, S. Levine, and C. Finn · 2019
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Camera-to-robot pose estimation from a single image
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Watch, try, learn: Meta-learning from demonstrations and reward
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Learning latent plans from play
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C. Lynch and P. Sermanet · 2020
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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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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar, et al · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
A. Srinivas, M. Laskin, and P. Abbeel · 2020
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Reinforcement learning with augmented data
M. Laskin, K. Lee, A. Stooke, L. Pinto, P. Abbeel, and A. Srinivas · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
I. Kostrikov, D. Yarats, and R. Fergus · 2020
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Self-supervised policy adaptation during deployment
N. Hansen, Y. Sun, P. Abbeel, A. A. Efros, L. Pinto, and X. Wang · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
M. Tancik, P. P. Srinivasan, B. Mildenhall, S. Fridovich-Keil, N. Raghavan, U. Singhal, R. Ramamoorthi, J. T. Barron, and R. Ng · 2020
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