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求解器近年来已成为任务与运动规划（TAMP）问题领域中有效的解决方案，它将PDDL扩展至连续动作空间问题。先前研究表明，PDDLStream问题可简化为一系列PDDL规划问题，继而可使用现成规划器求解。然而，该方法存在运行时间较长的问题。本文提出LAZY求解器，它针对PDDLStream问题保持基于动作骨架的单一集成搜索，随着运动规划过程中懒惰式抽取的可能运动样本，该搜索会逐步获得更充分的几何信息支撑。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。研究表明，在包含不同数量物体、目标及初始条件的未见测试环境中，该方法在加快可行性解搜索速度方面效果显著。我们通过对比现有PDDLStream问题求解器，在一系列7自由度重排/操作模拟问题上评估了本TAMP方法。