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：机器人领域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。近期如PDDLStream等研究工作聚焦于乐观规划，通过逐步增长的对象集合进行搜索，直至找到可行轨迹。然而，该方法以广度优先方式枚举所有对象，未考虑问题本身的逻辑与几何结构，导致在长时域推理及大量对象场景中耗时巨大。为解决此问题，我们提出一种几何信息增强的符号规划器，以最佳优先方式扩展对象与事实集合，并通过从先前搜索计算中学习的图神经网络确定优先级。我们在多种问题上评估了该方法，展示了在复杂场景下更强的规划能力。此外，我们将算法应用于7自由度机械臂的积木堆叠操作任务中。