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Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions.
The minimax principle in asymptotic statistical theory
Millar, P. W · 1983
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Alvinn: An autonomous land vehicle in a neural network
Pomerleau, D. A · 1989
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A framework for behavioral cloning
Bain, M. and Sammut, C · 1995
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Learning from demonstration
Schaal, S · 1997
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Learning agents for uncertain environments
Russell, S · 1998
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Reinforcement learning: An introduction , volume 1
Sutton, R. S. and Barto, A. G · 1998
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Algorithms for inverse reinforcement learning
Ng, A. Y., Russell, S. J., et al · 2000
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Computational approaches to motor learning by imitation
Schaal, S., Ijspeert, A., and Billard, A · 2003
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Apprenticeship learning via inverse reinforcement learning
Abbeel, P. and Ng, A. Y · 2004
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Robot programming by demonstration
Billard, A., Calinon, S., Dillmann, R., and Schaal, S · 2008
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Maximum entropy inverse reinforcement learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., and Dey, A. K · 2008
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A survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., and Browning, B · 2009
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Efficient reductions for imitation learning
Ross, S. and Bagnell, D · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Ross, S., Gordon, G., and Bagnell, D · 2011
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P · 2015
Cited alongside, same era.
End to end learning for self-driving cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al · 2016
Cited alongside, same era.
Learning robust rewards with adversarial inverse reinforcement learning
Fu, J., Luo, K., and Levine, S · 2017
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Learning invariant feature spaces to transfer skills with reinforcement learning
Gupta, A., Devin, C., Liu, Y., Abbeel, P., and Levine, S · 2017
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Reinforcement learning with deep energy-based policies
Haarnoja, T., Tang, H., Abbeel, P., and Levine, S · 2017
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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 · 2017
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Learning human behaviors from motion capture by adversarial imitation
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Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
Cited alongside, same era.
Guided cost learning: Deep inverse optimal control via policy optimization
Finn, C., Levine, S., and Abbeel, P · 2016
Cited alongside, same era.
A machine learning approach to visual perception of forest trails for mobile robots
Giusti, A., Guzzi, J., Cireşan, D. C., He, F.-L., Rodríguez, J. P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Di Caro, G., et al · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Ho, J. and Ermon, S · 2016
Cited alongside, same era.
Shiv: Reducing supervisor burden in dagger using support vectors for efficient learning from demonstrations in high dimensional state spaces
Laskey, M., Staszak, S., Hsieh, W. Y.-S., Mahler, J., Pokorny, F. T., Dragan, A. D., and Goldberg, K · 2016
Cited alongside, same era.
Pybullet, a python module for physics simulation for games, robotics and machine learning
Coumans, E. and Bai, Y · 2017
Cited alongside, same era.
One-shot visual imitation learning via meta-learning
Finn, C., Yu, T., Zhang, T., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Merel, J., Tassa, Y., Srinivasan, S., Lemmon, J., Wang, Z., Wayne, G., and Heess, N · 2017
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Combining self-supervised learning and imitation for vision-based rope manipulation
Nair, A., Chen, D., Agrawal, P., Isola, P., Abbeel, P., Malik, J., and Levine, S · 2017
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Time-contrastive networks: Self-supervised learning from multi-view observation
Sermanet, P., Lynch, C., Hsu, J., and Levine, S · 2017
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Towards automatic learning of procedures from web instructional videos
Zhou, L., Xu, C., and Corso, J. J · 2017
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
Closest in time.
Optiongan: Learning joint reward-policy options using generative adversarial inverse reinforcement learning
Henderson, P., Chang, W.-D., Bacon, P.-L., Meger, D., Pineau, J., and Precup, D · 2018
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Pathak, D., Mahmoudieh, P., Luo, G., Agrawal, P., Chen, D., Shentu, Y., Shelhamer, E., Malik, J., Efros, A. A., and Darrell, T · 2018
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Behavioral cloning from observation
Torabi, F., Warrnell, G., and Stone, P · 2018
Closest in time.