Generating complex robot behavior requires reasoning simultaneously on both discrete, task-related decisions and feasibility of continuous motions.
This problem is often referred to as Task And Motion Planning (TAMP). Research into TAMP approaches has proceeded for well over a decade, yielding key results and algorithms.
Yet a prerequisite for such approaches is specification of the planning problem; beyond classic issues of knowledge engineering, varying assumptions on TAMP formulations require varying aspects to the specification, e.g., grasp information, placements, and manipulation strategies.
Addressing such issues of specification, semantics, and learning will enhance fair comparison, progress, and application in TAMP.
