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Large language models (LLMs) are accelerating the development of language-guided robot planners.
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C. Paxton, Y. Bisk, J. Thomason, A. Byravan, and D. Fox, “Prospection: Interpretable plans from language by predicting the future,” in Proceedings of the 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 6942–6948
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E. Karpas and D. Magazzeni, “Automated planning for robotics,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 3, no. 1, pp. 417–439, 2020
2020
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S. Inayoshi, K. Otani, A. Tejero-de Pablos, and T. Harada, “Bounding-box channels for visual relationship detection,” in Proceedings of the 2020 European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 682–697
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T. Silver and R. Chitnis, “PDDLGym: Gym environments from PDDL problems,” in Proceedings of the 2020 International Conference on Automated Planning and Scheduling (ICAPS) PRL Workshop , 2020
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Z. Wang, C. R. Garrett, L. P. Kaelbling, and T. Lozano-Pérez, “Learning compositional models of robot skills for task and motion planning,” The International Journal of Robotics Research , vol. 40, no. 6-7, pp. 866–894, 2021
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T. Silver, R. Chitnis, J. Tenenbaum, L. P. Kaelbling, and T. Lozano-Pérez, “Learning symbolic operators for task and motion planning,” in Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 3182–3189
2021
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A. Zareian, K. D. Rosa, D. H. Hu, and S.-F. Chang, “Open-vocabulary object detection using captions,” in Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 14 393–14 402
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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 Proceedings of the 39th International Conference on Machine Learning , ser. Proceedings of the 2022 International Conference on Machine Learning (ICML), vol. 162, 2022, pp. 9118–9147
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