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Imitation learning is a widely used policy learning method that enables intelligent agents to acquire complex skills from expert demonstrations.
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Sun, W., Venkatraman, A., Gordon, G.J., Boots, B., Bagnell, J.A.: Deeply aggrevated: Differentiable imitation learning for sequential prediction. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017. Proceedings of Machine Learning Research, vol. 70, pp. 3309–3318. PMLR (2017)
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Brantley, K., Sun, W., Henaff, M.: Disagreement-regularized imitation learning. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net (2020)
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Beery, S., Horn, G.V., Perona, P.: Recognition in terra incognita. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XVI. Lecture Notes in Computer Science, vol. 11220, pp. 472–489. Springer (2018)
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Codevilla, F., Müller, M., López, A.M., Koltun, V., Dosovitskiy, A.: End-to-end driving via conditional imitation learning. In: 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, Australia, May 21-25, 2018. pp. 1–9. IEEE (2018)
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Sun, W., Bagnell, J.A., Boots, B.: Truncated horizon policy search: Combining reinforcement learning & imitation learning. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net (2018)
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Bansal, M., Krizhevsky, A., Ogale, A.S.: Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst. In: Bicchi, A., Kress-Gazit, H., Hutchinson, S. (eds.) Robotics: Science and Systems XV, University of Freiburg, Freiburg im Breisgau, Germany, June 22-26, 2019 (2019)
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Chen, D., Zhou, B., Koltun, V., Krähenbühl, P.: Learning by cheating. In: Kaelbling, L.P., Kragic, D., Sugiura, K. (eds.) 3rd Annual Conference on Robot Learning, CoRL 2019, Osaka, Japan, October 30 - November 1, 2019, Proceedings. Proceedings of Machine Learning Research, vol. 100, pp. 66–75. PMLR (2019)
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Codevilla, F., Santana, E., López, A.M., Gaidon, A.: Exploring the limitations of behavior cloning for autonomous driving. In: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. pp. 9328–9337. IEEE (2019)
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de Haan, P., Jayaraman, D., Levine, S.: Causal confusion in imitation learning. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. pp. 11693–11704 (2019)
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Wen, C., Lin, J., Darrell, T., Jayaraman, D., Gao, Y.: Fighting copycat agents in behavioral cloning from observation histories. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual (2020)
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Zhou, X., Koltun, V., Krähenbühl, P.: Tracking objects as points. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J. (eds.) Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IV. Lecture Notes in Computer Science, vol. 12349, pp. 474–490. Springer (2020)
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Chen, D., Koltun, V., Krähenbühl, P.: Learning to drive from a world on rails. CoRR abs/2105.00636
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Hu, P., Huang, A., Dolan, J.M., Held, D., Ramanan, D.: Safe local motion planning with self-supervised freespace forecasting. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021. pp. 12732–12741. Computer Vision Foundation / IEEE (2021)
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Mandlekar, A., Xu, D., Wong, J., Nasiriany, S., Wang, C., Kulkarni, R., Fei-Fei, L., Savarese, S., Zhu, Y., Martín-Martín, R.: What matters in learning from offline human demonstrations for robot manipulation. In: Faust, A., Hsu, D., Neumann, G. (eds.) Conference on Robot Learning, 8-11 November 2021, London, UK. Proceedings of Machine Learning Research, vol. 164, pp. 1678–1690. PMLR (2021)
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Wen, C., Lin, J., Qian, J., Gao, Y., Jayaraman, D.: Keyframe-focused visual imitation learning. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp. 11123–11133. PMLR (2021)
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Wen, C., Qian, J., Lin, J., Teng, J., Jayaraman, D., Gao, Y.: Fighting fire with fire: Avoiding dnn shortcuts through priming. In: International Conference on Machine Learning. pp. 23723–23750. PMLR (2022)
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