论文标题
政策引导的懒惰搜索，并提供了对任务和运动计划的反馈
Policy-Guided Lazy Search with Feedback for Task and Motion Planning
论文作者
论文摘要
PDDLSTREAM求解器最近成为了用于任务和运动计划（TAMP）问题的可行解决方案，将PDDL扩展到连续动作空间的问题。先前的工作表明，如何将PDDLSTREAM问题减少到一系列PDDL计划问题，然后可以使用现成的计划者解决这些问题。但是，这种方法可能会遇到长时间的时间。在本文中，我们提出了懒惰的懒惰，这是一个解决PDDLStream问题的求解器，该求解器在动作骨架上保持了单个集成的搜索，该搜索在运动计划期间懒惰地绘制了可能的动作样本，该动作骨架逐渐变得越来越几何。我们探讨了如何将目标定向策略的模型和当前的运动抽样数据纳入懒惰以适应性指导任务计划者。我们表明，这导致在寻找可行的解决方案时，可以通过不同的对象，目标和初始条件来评估的可行解决方案。我们通过与现有的求解器进行比较，以在一系列模拟的7DOF重排/操作问题上与现有求解器进行比较。
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.
