We present an approach for Task-Motion Planning (TMP) using Iterative Deepened AND/OR Graph Networks (TMP-IDAN) that uses an AND/OR graph network based novel abstraction for compactly representing the task-level states and actions. While retrieving a target object from clutter, the number of object re-arrangements required to grasp the target is not known ahead of time. To address this challenge, in contrast to traditional AND/OR graph-based planners, we grow the AND/OR graph online until the target grasp is feasible and thereby obtain a network of AND/OR graphs. The AND/OR graph network allows faster computations than traditional task planners. We validate our approach and evaluate its capabilities using a Baxter robot and a state-of-the-art robotics simulator in several challenging non-trivial cluttered table-top scenarios. The experiments show that our approach is readily scalable to increasing number of objects and different degrees of clutter.
翻译:我们提出了一个任务移动规划方法(TMP ), 使用一个基于 AND/OR 图形网络( TMP- IDAN), 使用一个基于 AND/ OR 的新型抽象网络, 来代表任务级别状态和行动。 在从 乱七八糟中获取目标对象的同时, 尚未事先知道要掌握目标所需的天体重新排列数量 。 为了应对这一挑战, 与传统的和/ 或基于图形的规划者相比, 我们将AND/ OR 图形放到网上, 直至目标掌握可行, 从而获得一个 AND/ OR 图形网络。 AND/ OR 图形网络可以比传统任务规划者更快地进行计算 。 我们验证了我们的方法, 并评估它在若干具有挑战性的非三角相交织的桌面情景中使用一个最先进的机器人模拟器的能力 。 实验显示, 我们的方法很容易扩大对象的数量和不同程度的布局。