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Integrated Task and Motion Planning

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arxiv 2010.01083 v1 pith:7AJSFJCR submitted 2020-10-02 cs.RO cs.AI

Integrated Task and Motion Planning

classification cs.RO cs.AI
keywords planningmotiontamptaskcontinuousdiscreteobjectsproblems
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the state of the objects, is known as task and motion planning (TAMP). TAMP problems contain elements of discrete task planning, discrete-continuous mathematical programming, and continuous motion planning, and thus cannot be effectively addressed by any of these fields directly. In this paper, we define a class of TAMP problems and survey algorithms for solving them, characterizing the solution methods in terms of their strategies for solving the continuous-space subproblems and their techniques for integrating the discrete and continuous components of the search.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    InCoM achieves 23-28% higher success rates in mobile manipulation tasks by inferring motion intent for adaptive perception and decoupling base-arm action generation.

  3. InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation

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    InCoM reports 23–28 percentage-point success-rate gains in mobile manipulation benchmarks by dynamically reweighting multi-scale perception via inferred motion intent and decoupling base-arm action generation with flo...

  4. InSight: Self-Guided Skill Acquisition via Steerable VLAs

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