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Task and motion planning problems in robotics combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables.
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B. Kim, L. Kaelbling, and T. Lozano-Pérez, “Guiding search in continuous state-action spaces by learning an action sampler from off-target search experience,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018
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B. Kim, Z. Wang, L. P. Kaelbling, and T. Lozano-Pérez, “Learning to guide task and motion planning using score-space representation,” The International Journal of Robotics Research , vol. 38, no. 7, pp. 793–812, 2019
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C. R. Garrett, T. Lozano-Pérez, and L. P. Kaelbling, “Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning,” 2020
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C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-Pérez, “Integrated task and motion planning,” 2020
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C. R. Garrett, C. Paxton, T. Lozano-Pérez, L. P. Kaelbling, and D. Fox, “Online replanning in belief space for partially observable task and motion problems,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 5678–5684
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K. Kase, C. Paxton, H. Mazhar, T. Ogata, and D. Fox, “Transferable task execution from pixels through deep planning domain learning,” IEEE International Conference on Robotics and Automation (ICRA) , pp. 10 459–10 465, 2020
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T. Silver, R. Chitnis, A. Curtis, J. Tenenbaum, T. Lozano-Perez, and L. P. Kaelbling, “Planning with learned object importance in large problem instances using graph neural networks,” 2020
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2021
Closest in time.
2021
Closest in time.
M. Diehl, C. Paxton, and K. Ramirez-Amaro, “Automated generation of robotic planning domains from observations,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 6732–6738
2021
Closest in time.