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The path planning problems arising in manipulation planning and in task andmotion planning settings are typically repetitive: the same manipulator movesin a space that only changes slightly. Despite this potential for reuse ofinformation, few planners fully exploit the available information. To betterenable this reuse, we decompose the collision checking into reusable, andnon-reusable parts. We then treat the sequences of path planning problems inmanipulation planning as a multiquery path planning problem. This allows theusage of planners that actively minimize planning effort over multiple queries,and by doing so, actively reuse previous knowledge. We implement this approachin EIRM* and effort ordered LazyPRM*, and benchmark it on multiple simulatedrobotic examples. Further, we show that the approach of decomposing collisionchecks additionally enables the reuse of the gained knowledge over multipledifferent instances of the same problem, i.e., in a multiquery manipulationplanning scenario. The planners using the decomposed collision checkingoutperform the other planners in initial solution time by up to a factor of twowhile providing a similar solution quality.

Efficient Path Planning In Manipulation Planning Problems by Actively Reusing Validation Effort