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.

任务和动作规划方法中的抽象描述模糊和欠定性使得很难确定成功执行任务所需的物理约束。我们提出了一种替代的任务和动作规划方法，将任务和动作规划融合为一个启发式搜索，并基于基于物体的抽象的运动约束，从而利用现成的人工智能启发式搜索的计算效率，产生具有物理可行性的计划，无需进行密集的子符号几何推理。