Task and motion planning problems in robotics combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables. Recent works such as PDDLStream have focused on optimistic planning with an incrementally growing set of objects until a feasible trajectory is found. However, this set is exhaustively expanded in a breadth-first manner, regardless of the logical and geometric structure of the problem at hand, which makes long-horizon reasoning with large numbers of objects prohibitively time-consuming. To address this issue, we propose a geometrically informed symbolic planner that expands the set of objects and facts in a best-first manner, prioritized by a Graph Neural Network that is learned from prior search computations. We evaluate our approach on a diverse set of problems and demonstrate an improved ability to plan in difficult scenarios. We also apply our algorithm on a 7DOF robotic arm in block-stacking manipulation tasks.

翻译：机器人中的任务与运动规划问题结合了离散任务变量的符号规划与连续状态及动作变量的运动优化。近期诸如PDDLStream等研究聚焦于乐观规划，通过增量式扩展对象集直至找到可行轨迹。然而，该集合以广度优先方式穷举扩展，未考虑当前问题的逻辑与几何结构，导致在处理包含大量对象的长时域推理时极为耗时。为解决此问题，我们提出一种几何信息驱动的符号规划器，以最佳优先方式扩展对象与事实集合，并通过基于先验搜索计算学习到的图神经网络设定优先级。我们在多种问题上评估了该方法，并证明了其在困难场景下的规划能力提升。此外，我们还将该算法应用于7自由度机械臂的积木堆叠操作任务中。