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 large or difficult scenarios. We also apply our algorithm on a 7DOF robotic arm in several block-stacking manipulation tasks.

翻译：机器人的任务和运动规划问题将不同任务变量的象征性规划与连续状态和动作变量的动作优化结合起来。最近的工作,如PDDLStream,侧重于乐观规划,在找到可行轨迹之前,将一组逐渐增长的物体放在一起。然而,这套工作以广度第一的方式全面扩展,而不论问题目前所处的逻辑和几何结构,它使长视距推理与大量极其耗时的物体相提并论。为了解决这一问题,我们提议了一个具有几何学知识的象征性规划师,以最佳方式扩大物体和事实的组合,由从先前的搜索计算中学习的图表神经网络确定优先顺序。我们评估了对一系列不同问题的方法,并展示了在大或困难情况下进行规划的更大能力。我们还在几个块式操纵任务中使用了7DOF机器人臂的算法。