PDDLStream solvers have recently emerged as viable solutions for Task and Motion Planning (TAMP) problems, extending PDDL to problems with continuous action spaces. Prior work has shown how PDDLStream problems can be reduced to a sequence of PDDL planning problems, which can then be solved using off-the-shelf planners. However, this approach can suffer from long runtimes. In this paper we propose LAZY, a solver for PDDLStream problems that maintains a single integrated search over action skeletons, which gets progressively more geometrically informed, as samples of possible motions are lazily drawn during motion planning. We explore how learned models of goal-directed policies and current motion sampling data can be incorporated in LAZY to adaptively guide the task planner. We show that this leads to significant speed-ups in the search for a feasible solution evaluated over unseen test environments of varying numbers of objects, goals, and initial conditions. We evaluate our TAMP approach by comparing to existing solvers for PDDLStream problems on a range of simulated 7DoF rearrangement/manipulation problems.

翻译：PDDLStream求解器最近成为解决任务与运动规划（Task and Motion Planning, TAMP）问题的可行方案，将PDDL扩展至具有连续动作空间的问题。先前研究已表明PDDLStream问题可简化为一系列PDDL规划问题，并利用现成规划器求解。然而，该方法可能面临运行时间较长的问题。本文提出LAZY——一种PDDLStream问题的求解器，它维护一个对动作骨架进行单一集成搜索的框架，随着运动规划过程中对可能运动样本的惰性抽取，该搜索逐步获得更丰富的几何信息。我们探索如何将目标导向策略的学习模型与当前运动采样数据融入LAZY，以自适应引导任务规划器。实验表明，该方法在包含不同数量物体、目标及初始条件的未见测试环境中，能显著加速可行解的搜索过程。通过在一系列模拟的7自由度重排/操作问题中与现有PDDLStream问题求解器进行对比，我们评估了所提出的TAMP方法。