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Imitation learning from human demonstrations is an effective means to teach robots manipulation skills.
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J. Wong, A. Tung, A. Kurenkov, A. Mandlekar, L. Fei-Fei, S. Savarese, and R. Martín-Martín, “Error-aware imitation learning from teleoperation data for mobile manipulation,” in Conference on Robot Learning . PMLR, 2022, pp. 1367–1378
2022
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2022
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S. Nasiriany, T. Gao, A. Mandlekar, and Y. Zhu, “Learning and retrieval from prior data for skill-based imitation learning,” in Conference on Robot Learning (CoRL) , 2022
2022
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P. Mitrano and D. Berenson, “Data Augmentation for Manipulation,” in Proceedings of Robotics: Science and Systems , New York City, NY, USA, 6 2022
2022
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S. Sinha, A. Mandlekar, and A. Garg, “S4rl: Surprisingly simple self-supervision for offline reinforcement learning in robotics,” in Conference on Robot Learning . PMLR, 2022, pp. 907–917
2022
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S. Pitis, E. Creager, A. Mandlekar, and A. Garg, “Mocoda: model-based counterfactual data augmentation,” in Proceedings of the 36th International Conference on Neural Information Processing Systems , 2022, pp. 18 143–18 156
2022
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Z. Mandi, H. Bharadhwaj, V. Moens, S. Song, A. Rajeswaran, and V. Kumar, “Cacti: A framework for scalable multi-task multi-scene visual imitation learning,” in CoRL 2022 Workshop on Pre-training Robot Learning , 2022
2022
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Z. Jiang, C.-C. Hsu, and Y. Zhu, “Ditto: Building digital twins of articulated objects from interaction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5616–5626
2022
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