Hierarchical Task and Motion Planning in the Now
Author(s)Kaelbling, Leslie Pack; Lozano-Perez, Tomas
Learning and Intelligent Systems
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In this paper we outline an approach to the integration of task planning and motion planning that has the following key properties: It is aggressively hierarchical. It makes choices and commits to them in a top-down fashion in an attempt to limit the length of plans that need to be constructed, and thereby exponentially decrease the amount of search required. Importantly, our approach also limits the need to project the effect of actions into the far future. It operates on detailed, continuous geometric representations and partial symbolic descriptions. It does not require a complete symbolic representation of the input geometry or of the geometric effect of the task-level operations.
Workshop on Mobile Manipulation, IEEE International Conference on Robotics and Automation
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