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Due to burdensome data requirements, learning from demonstration often falls short of its promise to allow users to quickly and naturally program robots.
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S. Niekum, S. Osentoski, G. Konidaris, and A. G. Barto, “Learning and generalization of complex tasks from unstructured demonstrations,” in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on . IEEE, 2012, pp. 5239–5246
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D. K. Misra, J. Sung, K. Lee, and A. Saxena, “Tell me dave: Contextsensitive grounding of natural language to mobile manipulation instructions,” in in RSS . Citeseer, 2014
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2014
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2015
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D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Computer Vision (ICCV), 2015 IEEE International Conference on . IEEE, 2015, pp. 4489–4497
2015
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X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “Infogan: Interpretable representation learning by information maximizing generative adversarial nets,” in Advances in Neural Information Processing Systems , 2016, pp. 2172–2180
2016
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I. Misra, C. L. Zitnick, and M. Hebert, “Shuffle and Learn: Unsupervised Learning using Temporal Order Verification,” in ECCV , 2016
2016
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Y. Duan, M. Andrychowicz, B. Stadie, J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba, “One-shot imitation learning,” in Advances in Neural Information Processing Systems (NIPS) , 2017
2017
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K. Hausman, Y. Chebotar, S. Schaal, G. Sukhatme, and J. Lim, “Multi-modal imitation learning from unstructured demonstrations using generative adversarial nets,” in Advances in Neural Information Processing Systems , 2017
2017
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C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proceedings of the 34th International Conference on Machine Learning , 2017, pp. 1126–1135
2017
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2017
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P. Sermanet, K. Xu, and S. Levine, “Unsupervised perceptual reward for imitation learning,” in Proceedings of Robotics: Science and Systems , 7 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
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C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine, “One-shot visual imitation learning via meta-learning,” in Conference on Robot Learning , 2017
2017
Cited alongside, same era.
E. e. a. Coumans, “Bullet physics sdk,” https://github.com/bulletphysics/bullet3
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P. Dhariwal, C. Hesse, M. Plappert, A. Radford, J. Schulman, S. Sidor, and Y. Wu, “Openai baselines,” https://github.com/openai/baselines , 2017
2017
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2018
Closest in time.
H. Yang, X. He, and F. Porikli, “One-shot action localization by learning sequence matching network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
Closest in time.