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Recent works have shown that Large Language Models (LLMs) can be applied to ground natural language to a wide variety of robot skills.
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E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn, “Bc-z: Zero-shot task generalization with robotic imitation learning,” in Conference on Robot Learning . PMLR, 2022, pp. 991–1002
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. , “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning . PMLR, 2021, pp. 8748–8763
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H. Zhang, Y. Lu, C. Yu, D. Hsu, X. La, and N. Zheng, “Invigorate: Interactive visual grounding and grasping in clutter,” in RSS , 2021
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O. Mees and W. Burgard, “Composing pick-and-place tasks by grounding language,” in ISER , 2021
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S. Nair, E. Mitchell, K. Chen, B. Ichter, S. Savarese, and C. Finn, “Learning language-conditioned robot behavior from offline data and crowd-sourced annotation,” in CoRL , 2021
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V. Blukis, R. Knepper, and Y. Artzi, “Few-shot object grounding and mapping for natural language robot instruction following,” in Conference on Robot Learning . PMLR, 2021, pp. 1829–1854
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