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This paper introduces GET-Zero, a model architecture and training procedure for learning an embodiment-aware control policy that can immediately adapt to new hardware changes without retraining.
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2021
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V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State, “Isaac gym: High performance GPU based physics simulation for robot learning,” in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) , 2021. [Online]. Available: https://openreview.net/forum?id=fgFBtYgJQX˙
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2023
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2023
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2023
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A. Stone, T. Xiao, Y. Lu, K. Gopalakrishnan, K.-H. Lee, Q. Vuong, P. Wohlhart, S. Kirmani, B. Zitkovich, F. Xia, C. Finn, and K. Hausman, “Open-world object manipulation using pre-trained vision-language models,” in arXiv preprint , 2023
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N. Bohlinger, G. Czechmanowski, M. P. Krupka, P. Kicki, K. Walas, J. Peters, and D. Tateo, “One policy to run them all: Towards an end-to-end learning approach to multi-embodiment locomotion,” in Workshop on Embodiment-Aware Robot Learning , 2024. [Online]. Available: https://openreview.net/forum?id=HVWusz2zv5
2024
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2024
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2024
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L. Y. Chen, K. Hari, K. Dharmarajan, C. Xu, Q. Vuong, and K. Goldberg, “Mirage: Cross-embodiment zero-shot policy transfer with cross-painting,” in Proceedings of Robotics: Science and Systems , Delft, Netherlands, 2024
2024
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C. Sferrazza, D.-M. Huang, F. Liu, J. Lee, and P. Abbeel, “Body transformer: Leveraging robot embodiment for policy learning,” in Workshop on Embodiment-Aware Robot Learning , 2024. [Online]. Available: https://openreview.net/forum?id=IbXqRpANPD
2024
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