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Task and Motion Planning (TAMP) algorithms solve long-horizon robotics tasks by integrating task planning with motion planning; the task planner proposes a sequence of actions towards a goal state and the motion planner verifies whether this action sequence is geometrically feasible for the robot.
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C. R. Garrett, T. Lozano-Pérez, and L. P. Kaelbling, “Sampling-based methods for factored task and motion planning,”
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T. Migimatsu and J. Bohg, “Object-centric task and motion planning in dynamic environments,”
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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
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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,”
2021
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T. Silver, R. Chitnis, A. Curtis, J. B. Tenenbaum, T. Lozano-Pérez, and L. P. Kaelbling, “Planning with learned object importance in large problem instances using graph neural networks,” in
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C. R. Garrett, T. Lozano-Perez, and L. P. Kaelbling, “Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning,” in
2018
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N. T. Dantam, Z. K. Kingston, S. Chaudhuri, and L. E. Kavraki, “An incremental constraint-based framework for task and motion planning,”
2018
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S. Liu and P. Liu, “A review of motion planning algorithms for robotic arm systems,” November 2020
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,”
2021
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W. Thomason, M. P. Strub, and J. D. Gammell, “Task and motion informed trees (tmit*): Almost-surely asymptotically optimal integrated task and motion planning,”
2022
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R. Chitnis, T. Silver, J. B. Tenenbaum, T. Lozano-Pérez, and L. P. Kaelbling, “Learning neuro-symbolic relational transition models for bilevel planning,” in
2022
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