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A flurry of recent work has demonstrated that pre-trained large language models (LLMs) can be effective task planners for a variety of single-robot tasks.
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W. Huang, P. Abbeel, D. Pathak, and I. Mordatch, “Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,” in International Conference on Machine Learning . PMLR, 2022, pp. 9118–9147
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J. X. Liu, Z. Yang, B. Schornstein, S. Liang, I. Idrees, S. Tellex, and A. Shah, “Lang2LTL: Translating natural language commands to temporal specification with large language models,” in Workshop on Language and Robotics at CoRL 2022 , 2022. [Online]. Available: https://openreview.net/forum?id=VxfjGZzrdn
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V. N. Hartmann, A. Orthey, D. Driess, O. S. Oguz, and M. Toussaint, “Long-horizon multi-robot rearrangement planning for construction assembly,” IEEE Transactions on Robotics , vol. 39, no. 1, pp. 239–252, 2022
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W. Liu, K. Leahy, Z. Serlin, and C. Belta, “Robust multi-agent coordination from catl+ specifications,” in 2023 American Control Conference (ACC) . IEEE, 2023, pp. 3529–3534
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