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We study the question of how to imitate tasks across domains with discrepancies such as embodiment, viewpoint, and dynamics mismatch.
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Syed, U., Bowling, M., and Schapire, R. E · 2008
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The development of imitation in infancy
Jones, S. S · 2009
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An automated measure of mdp similarity for transfer in reinforcement learning
Ammar, H. B., Eaton, E., Ruvolo, P., and Taylor, M. E · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Unsupervised cross-domain transfer in policy gradient reinforcement learning via manifold alignment
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Generative adversarial imitation learning
Ho, J. and Ermon, S · 2016
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End-to-end training of deep visuomotor policies
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Learning invariant feature spaces to transfer skills with reinforcement learning
Gupta, A., Devin, C., Liu, Y. X., Abbeel, P., and Levine, S · 2017
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Third person imitation learning
Stadie, B., Abbeel, P., and Sutskever, I · 2017
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Cross-domain transfer in reinforcement learning using target apprentice
Joshi, G. and Chowdhary, G · 2018
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Imitation from observation: Learning to imitate behaviors from raw video via context translation
Liu, Y., Gupta, A., Abbeel, P., and Levine, S · 2018
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2015
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Body maps in the infant brain
Marshall, P. J. and Meltzoff, A. N · 2015
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Time-contrastive networks: Self-supervised learning from video
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NerveNet: Learning structured policy with graph neural networks
Wang, T., Liao, R., Ba, J., and Fidler, S · 2018
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State alignment-based imitation learning
Liu, F., Ling, Z., Mu, T., and Su, H · 2020
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