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In robotic task planning, symbolic planners using rule-based representations like PDDL are effective but struggle with long-sequential tasks in complicated environments due to exponentially increasing search space.
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T. Silver, V. Hariprasad, R. S. Shuttleworth, N. Kumar, T. Lozano-Pérez, and L. P. Kaelbling, “Pddl planning with pretrained large language models,” in NeurIPS 2022 foundation models for decision making workshop , 2022
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Z. Zhao, W. S. Lee, and D. Hsu, “Large language models as commonsense knowledge for large-scale task planning,” Advances in Neural Information Processing Systems , vol. 36, 2024
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J. Gao, B. Sarkar, F. Xia, T. Xiao, J. Wu, B. Ichter, A. Majumdar, and D. Sadigh, “Physically grounded vision-language models for robotic manipulation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 12 462–12 469
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2023
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H. Guo, F. Wu, Y. Qin, R. Li, K. Li, and K. Li, “Recent trends in task and motion planning for robotics: A survey,” ACM Computing Surveys , vol. 55, pp. 1 – 36, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:256630415
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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 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 523–11 530
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Z. Zhou, J. Song, K. Yao, Z. Shu, and L. Ma, “Isr-llm: Iterative self-refined large language model for long-horizon sequential task planning,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 2081–2088
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