We present a framework for learning to guide geometric task-and-motion planning (G-TAMP). G-TAMP is a subclass of task-and-motion planning in which the goal is to move multiple objects to target regions among movable obstacles. A standard graph search algorithm is not directly applicable, because G-TAMP problems involve hybrid search spaces and expensive action feasibility checks. To handle this, we introduce a novel planner that extends basic heuristic search with random sampling and a heuristic function that prioritizes feasibility checking on promising state–action pairs. The main drawback of such pure planners is that they lack the ability to learn from planning experience to improve their efficiency. We propose two learning algorithms to address this. The first is an algorithm for learning a rank function that guides the discrete task-level search, and the second is an algorithm for learning a sampler that guides the continuous motion-level search. We propose design principles for designing data-efficient algorithms for learning from planning experience and representations for effective generalization. We evaluate our framework in challenging G-TAMP problems, and show that we can improve both planning and data efficiency.

我们提出了一个学习指导几何任务和运动规划（G - TAMP）的框架。G- TAMP是任务和运动规划的一个子类，其目标是将多个对象移动到可移动障碍物之间的目标区域。标准的图搜索算法不能直接适用，因为G- TAMP问题涉及混合搜索空间和昂贵的行动可行性检查。为了解决这个问题，我们引入了一种新颖的规划器，它通过随机抽样扩展了基本的启发式搜索，以及一个优先检查有希望的状态-动作对的可行性检查的启发式函数。这种纯计划者的主要缺点是他们缺乏从计划经验中学习以提高效率的能力。我们提出了两种学习算法来解决这个问题。第一个是学习引导离散任务级搜索的秩函数的算法，第二个是学习引导连续运动级搜索的采样器的算法。我们提出了设计数据高效算法的设计原则，以从规划经验和有效泛化的表示中学习。G- TAMP问题，并表明我们可以提高规划和数据效率。