In task and motion planning (TAMP), the ambiguity and underdetermination of abstract descriptions used by task planning methods make it difficult to characterize physical constraints needed to successfully execute a task. The usual approach is to overlook such constraints at task planning level and to implement expensive sub-symbolic geometric reasoning techniques that perform multiple calls on unfeasible actions, plan corrections, and re-planning until a feasible solution is found. We propose an alternative TAMP approach that unifies task and motion planning into a single heuristic search. Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI heuristic search to yield physically feasible plans. These plans can be directly transformed into object and motion parameters for task execution without the need of intensive sub-symbolic geometric reasoning.

翻译：在任务与运动规划（TAMP）中，任务规划方法使用的抽象描述存在模糊性与欠定性问题，这使得难以准确刻画成功执行任务所需的物理约束。传统方法倾向于在任务规划层面忽略此类约束，转而采用高代价的子符号几何推理技术——通过多次调用不可行动作、执行计划修正与重规划，直至找到可行解。我们提出一种替代性TAMP方法，将任务与运动规划统一为单次启发式搜索。该方法基于运动约束的对象中心抽象，能够利用现成AI启发式搜索的计算效率生成物理可行的规划方案。这些规划可直接转化为用于任务执行的对象与运动参数，无需大量子符号几何推理。