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We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan.
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asymov: a planner that deals with intricate symbolic and geometric problems
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Angelic hierarchical planning: Optimal and online algorithms
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An effective framework for path planning amidst movable obstacles
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Using classical planners for tasks with continuous operators in robotics
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Fabien Lagriffoul, Dimitar Dimitrov, Julien Bidot, Alessandro Saffiotti, and Lars Karlsson · 2014
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A constraint-based method for solving sequential manipulation planning problems
Tomás Lozano-Pérez and Leslie Pack Kaelbling · 2014
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Incremental task and motion planning: A constraint-based approach
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Über die kleinste kugel die eine räumliche figur einschliesst
Heinrich Jung
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Neil T Dantam, Zachary K Kingston, Swarat Chaudhuri, and Lydia E Kavraki · 2016
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Ffrob: Leveraging symbolic planning for efficient task and motion planning
Caelan Reed Garrett, Tomas Lozano-Perez, and Leslie Pack Kaelbling · 2016
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Asymptotically optimal planning under piecewise-analytic constraints
William Vega-Brown and Nicholas Roy · 2016
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Combined task and motion planning as classical AI planning
Jonathan Ferrer-Mestres, Guillem Francès, and Hector Geffner · 2017
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Admissible abstractions for near-optimal task and motion planning
William Vega-Brown and Nicholas Roy · 2018
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