Generalized planning accelerates classical planning by finding an algorithm-like policy that solves multiple instances of a task. A generalized plan can be learned from a few training examples and applied to an entire domain of problems. Generalized planning approaches perform well in discrete AI planning problems that involve large numbers of objects and extended action sequences to achieve the goal. In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) and examine the unique difficulties that arise when forced to consider geometric and physical constraints as a part of the generalized plan. Additionally, we show that these simple generalized plans learned from only a handful of examples can be used to improve the search efficiency of TAMP solvers.
翻译:一般规划通过寻找一种解决多种任务情况的算法式政策,加速了典型的规划工作。从几个培训实例中可以学到一个通用计划,并适用于整个问题领域。一般规划方法在独立的AI规划问题上表现良好,涉及大量物体和实现目标的延伸行动序列。在本文件中,我们提出了关于连续机器人任务和运动规划的学习特点、抽象和通用计划的算法,并审查了在被迫将几何和物理限制作为普遍计划的一部分时出现的独特困难。此外,我们表明,这些简单的通用计划仅从少数例子中学习,可用于提高TAMP解决者的搜索效率。