We propose in this paper a resolution scheme that is aimed to be relevant for a large class of manipulation planning problems. This endeavor complements our efforts in developing manipulation planning algorithms [2, 14, 13]. Indeed, we are convinced that a higher level of problems complexity, and particularly those involving multiple robots and multiple objects, will be accessible thanks to the introduction of a symbolic reasoning level. The resolution scheme relies on Probabilistic Roadmap Methods (PRMs) and on a reasoning level that adaptatively controls the construction and extension of a number of roadmaps. We consider this symbolic level as a step towards a systematic approach to integrate task planning and geometric planning in better conditions than trough a gross, and somewhat, artificial hierarchical decomposition. This paper describes the main ingredients of the proposed framework, and its first results.
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