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We present a large empirical investigation on the use of pre-trained visual representations (PVRs) for training downstream policies that execute real-world tasks.
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2022
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2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
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2021
Cited alongside, same era.
A. Khandelwal, L. Weihs, R. Mottaghi, and A. Kembhavi, “Simple but effective: Clip embeddings for embodied ai,” in CVPR , 2022
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S. Parisi, A. Rajeswaran, S. Purushwalkam, and A. K. Gupta, “The Unsurprising Effectiveness of Pre-Trained Vision Models for Control,” ICML , 2022
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2022
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S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta, “R3M: A Universal Visual Representation for Robot Manipulation,” CoRL , 2022
2022
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2022
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2023
Closest in time.
2023
Closest in time.
2023
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H. Bharadhwaj, J. Vakil, M. Sharma, A. Gupta, S. Tulsiani, and V. Kumar, “Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking,” 2023
2023
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V. Kumar, R. Shah, G. Zhou, V. Moens, V. Caggiano, J. Vakil, A. Gupta, and A. Rajeswaran, “Robohive: A unified framework for robot learning,” 2023
2023
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