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Recently, Large Language Models (LLMs) have witnessed remarkable performance as zero-shot task planners for robotic manipulation tasks.
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2022
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D. Driess, F. Xia, M. S. M. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, W. Huang, Y. Chebotar, P. Sermanet, D. Duckworth, S. Levine, V. Vanhoucke, K. Hausman, M. Toussaint, K. Greff, A. Zeng, I. Mordatch, and P. Florence, “Palm-e: An embodied multimodal language model,” 2023
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,” 2023
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,” 2023
2023
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H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” 2023
2023
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J. Feng, J. Lee, S. Geisler, S. Günnemann, and R. Triebel, “Topology-matching normalizing flows for out-of-distribution detection in robot learning,” in 7th Annual Conference on Robot Learning , 2023. [Online]. Available: https://openreview.net/forum?id=BzjLaVvr955
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A. Z. Ren, A. Dixit, A. Bodrova, S. Singh, S. Tu, N. Brown, P. Xu, L. Takayama, F. Xia, J. Varley, Z. Xu, D. Sadigh, A. Zeng, and A. Majumdar, “Robots that ask for help: Uncertainty alignment for large language model planners,” 2023
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Z. Liu, A. Bahety, and S. Song, “Reflect: Summarizing robot experiences for failure explanation and correction,” 2023
2023
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2023
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L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, and T. Liu, “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” 2023
2023
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J. Gawlikowski, C. R. N. Tassi, M. Ali, J. Lee, M. Humt, J. Feng, A. Kruspe, R. Triebel, P. Jung, R. Roscher et al. , “A survey of uncertainty in deep neural networks,” Artificial Intelligence Review , pp. 1–77, 2023
2023
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2023
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J. Ren, Y. Zhao, T. Vu, P. J. Liu, and B. Lakshminarayanan, “Self-evaluation improves selective generation in large language models,” 2023
2023
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S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” 2023
2023
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Z. Wang, S. Cai, G. Chen, A. Liu, X. Ma, and Y. Liang, “Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents,” 2023
2023
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N. Shinn, F. Cassano, E. Berman, A. Gopinath, K. Narasimhan, and S. Yao, “Reflexion: Language agents with verbal reinforcement learning,” 2023
2023
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H. Sun, Y. Zhuang, L. Kong, B. Dai, and C. Zhang, “Adaplanner: Adaptive planning from feedback with language models,” 2023
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S. Liu, Z. Zeng, T. Ren, F. Li, J. Y. Hao Zhang, C. Li, J. Yang, H. Su, J. Zhu, and L. Zhang, “Grounding dino: Marrying dino with grounded pre-training for open-set object detection,” 2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, L. G. C. Rolland, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, and et al. S, “Segment anything,” 2023
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M. Xiong, Z. Hu, X. Lu, Y. Li, J. Fu, J. He, and B. Hooi, “Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms,” 2024
2024
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