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While large-scale robot datasets have propelled recent progress in imitation learning, learning from smaller task specific datasets remains critical for deployment in new environments and unseen tasks.
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What matters in learning from offline human demonstrations for robot manipulation
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K. Choi, C. Meng, Y. Song, and S. Ermon · 2022
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M. Beliaev, A. Shih, S. Ermon, D. Sadigh, and R. Pedarsani · 2022
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M. Du, S. Nair, D. Sadigh, and C. Finn · 2023
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How to leverage diverse demonstrations in offline imitation learning
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S. M. Xie, S. Santurkar, T. Ma, and P. S. Liang · 2023
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Learning to discern: Imitating heterogeneous human demonstrations with preference and representation learning
S. Kuhar, S. Cheng, S. Chopra, M. Bronars, and D. Xu · 2023
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Scaling up and distilling down: Language-guided robot skill acquisition
H. Ha, P. Florence, and S. Song · 2023
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Mimicgen: A data generation system for scalable robot learning using human demonstrations
A. Mandlekar, S. Nasiriany, B. Wen, I. Akinola, Y. Narang, L. Fan, Y. Zhu, and D. Fox · 2023
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Libero: Benchmarking knowledge transfer for lifelong robot learning
B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone · 2023
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Flowretrieval: Flow-guided data retrieval for few-shot imitation learning
L.-H. Lin, Y. Cui, A. Xie, T. Hua, and D. Sadigh
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Remix: Optimizing data mixtures for large scale imitation learning
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Data quality in imitation learning
S. Belkhale, Y. Cui, and D. Sadigh · 2024
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STRAP: Robot sub-trajectory retrieval for augmented policy learning
M. Memmel, J. Berg, B. Chen, A. Gupta, and J. Francis · 2025
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