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Recently, Large Language Models (LLMs) have achieved remarkable success using in-context learning (ICL) in the language domain.
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2020
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S. James, Z. Ma, D. Rovick Arrojo, and A. J. Davison, “Rlbench: The robot learning benchmark & learning environment,”
2020
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J. Liu, D. Shen, Y. Zhang, B. Dolan, L. Carin, and W. Chen, “What makes good in-context examples for gpt-
2021
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T. Z. Zhao, E. Wallace, S. Feng, D. Klein, and S. Singh, “Calibrate before use: Improving few-shot performance of language models,” in
2021
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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,
2021
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2021
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M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba, “Evaluating large language models trained on code,” in
2021
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Y. Lin, A. S. Wang, G. Sutanto, A. Rai, and F. Meier, “Polymetis,”
2021
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S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi, and L. Zettlemoyer, “Rethinking the role of demonstrations: What makes in-context learning work?” in
2022
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J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” in
2022
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M. Minderer, A. Gritsenko, A. Stone, M. Neumann, D. Weissenborn, A. Dosovitskiy, A. Mahendran, A. Arnab, M. Dehghani, Z. Shen, X. Wang, X. Zhai, T. Kipf, and N. Houlsby, “Simple open-vocabulary object detection with vision transformers,” in
2022
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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
2022
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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
2022
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H. J. Kim, H. Cho, J. Kim, T. Kim, K. M. Yoo, and S. goo Lee, “Self-generated in-context learning: Leveraging auto-regressive language models as a demonstration generator,” in
2022
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S. James and A. J. Davison, “Q-attention: Enabling efficient learning for vision-based robotic manipulation,”
2022
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M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” in
2022
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2022
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I. Levy, B. Bogin, and J. Berant, “Diverse demonstrations improve in-context compositional generalization,” in
2023
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2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo,
2023
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W. Huang, C. Wang, R. Zhang, Y. Li, J. Wu, and L. Fei-Fei, “Voxposer: Composable 3d value maps for robotic manipulation with language models,” in
2023
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I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg, “Progprompt: Generating situated robot task plans using large language models,” in
2023
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M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman,
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A. Zeng, M. Attarian, B. Ichter, K. Choromanski, A. Wong, S. Welker, F. Tombari, A. Purohit, M. Ryoo, V. Sindhwani,
2023
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B. Chen, F. Xia, B. Ichter, K. Rao, K. Gopalakrishnan, M. S. Ryoo, A. Stone, and D. Kappler, “Open-vocabulary queryable scene representations for real world planning,” in
2023
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J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” in
2023
Google, “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,”
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H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” in
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F. Liu, K. Fang, P. Abbeel, and S. Levine, “Moka: Open-vocabulary robotic manipulation through mark-based visual prompting,” in
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S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu,
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J. Duan, W. Yuan, W. Pumacay, Y. R. Wang, K. Ehsani, D. Fox, and R. Krishna, “Manipulate-anything: Automating real-world robots using vision-language models,” in
2024
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C. Huang, O. Mees, A. Zeng, and W. Burgard, “Visual language maps for robot navigation,” in
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K. Lin, C. Agia, T. Migimatsu, M. Pavone, and J. Bohg, “Text2motion: From natural language instructions to feasible plans,”
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Y.-J. Wang, B. Zhang, J. Chen, and K. Sreenath, “Prompt a robot to walk with large language models,”
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A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn,
2023
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H. Zhou, A. Nova, H. Larochelle, A. Courville, B. Neyshabur, and H. Sedghi, “Teaching algorithmic reasoning via in-context learning,” in
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J. Mao, Y. Qian, J. Ye, H. Zhao, and Y. Wang, “Gpt-driver: Learning to drive with gpt,” in
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G. Wang, Y. Xie, Y. Jiang, A. Mandlekar, C. Xiao, Y. Zhu, L. Fan, and A. Anandkumar, “Voyager: An open-ended embodied agent with large language models,”
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W. Huang, C. Wang, Y. Li, R. Zhang, and L. Fei-Fei, “Rekep: Spatio-temporal reasoning of relational keypoint constraints for robotic manipulation,” in
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N. Di Palo and E. Johns, “Keypoint action tokens enable in-context imitation learning in robotics,” in
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A. Sohn, A. Nagabandi, C. Florensa, D. Adelberg, D. Wu, H. Farooq, I. Clavera, J. Welborn, J. Chen, N. Mishra, P. Chen, P. Qian, P. Abbeel, R. Duan, V. Vijay, and Y. Liu, “Introducing rfm-1: Giving robots human-like reason- ing capabilities,”
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D. Niu, Y. Sharma, G. Biamby, J. Quenum, Y. Bai, B. Shi, T. Darrell, and R. Herzig, “Llarva: Vision-action instruction tuning enhances robot learning,” in
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