A Systematic Analysis of Levels of Integration between High-Level Task Planning and Low-Level Feasibility Checks

ABSTRACT. We provide a systematic analysis of levels of integration between discrete high-level reasoning and continuous low-level feasibility checks to address hybrid planning problems in robotic applications. We identify four distinct strategies for such an integration: (i) low-level checks are done for all possible cases in advance and then this information is used during plan generation; (ii) low-level checks are done exactly when they are needed during the search for a plan; (iii) low-level checks are done after a plan is computed, and then a new plan is computed if the plan is found infeasible; (iv) similar to the previous strategy of replanning but a new plan is computed subject to the constraints obtained from previous low-level checks. We perform experiments on hybrid planning problems in housekeeping domain considering these four methods of integration, as well as some of their combinations. We analyze the usefulness of different levels of integration in this domain, both from the point of view of computational efficiency (in time and space) and from the point of view of plan quality relative to its feasibility. We discuss advantages and disadvantages of each strategy in the light of experimental results.