Robotic planning systems model spatial relations in detail as these are needed for manipulation tasks. In contrast to this, other physical attributes of objects and the effect of devices are usually oversimplified and expressed by abstract compound attributes. This limits the ability of planners to find alternative solutions. We propose to break these compound attributes down into a shared set of elementary attributes. This strongly facilitates generalization between different tasks and environments and thus helps to find innovative solutions. On the down-side, this generalization comes with an increased complexity of the solution space. Therefore, as the main contribution of the paper, we propose a method that splits the planning problem into a sequence of views, where in each view only an increasing subset of attributes is considered. We show that this view-based strategy offers a good compromise between planning speed and quality of the found plan, and discuss its general applicability and limitations.

翻译：机器人规划系统详细建模空间关系，因为这些关系是操作任务所必需的。相比之下，物体的其他物理属性及设备的作用通常被过度简化，并以抽象的复合属性表示。这限制了规划器寻找替代解决方案的能力。我们建议将这些复合属性分解为一组共享的基本属性。这极大地促进了不同任务和环境之间的泛化，从而有助于找到创新解决方案。然而，这种泛化也带来了解空间复杂性的增加。因此，作为本文的主要贡献，我们提出了一种方法，将规划问题拆分为一系列视图，在每个视图中仅考虑递增的属性子集。我们表明，这种基于视图的策略在规划速度与所发现规划的质量之间提供了良好的折中方案，并讨论了其通用适用性和局限性。