We have recently introduced a system that automatically generates robotic planning operators from human demonstrations. One feature of our system is the operator count, which keeps track of the application frequency of every operator within the demonstrations. In this extended abstract, we show that we can use the count to slim down domains with the goal of decreasing the search time for long-horizon planning goals. The conceptual idea behind our approach is that we would like to prioritize operators that have occurred more often in the demonstrations over those that were not observed so frequently. We, therefore, propose to limit the domain only to the most popular operators. If this subset of operators is not sufficient to find a plan, we iteratively expand this subset of operators. We show that this significantly reduces the search time for long-horizon planning goals.
翻译:我们最近引入了一个自动生成人类演示中机器人规划操作员的系统,我们系统的一个特征是操作员的计数,它跟踪了示威中每个操作员的应用频率。在这个漫长的抽象中,我们显示我们可以利用计数缩小范围,目的是减少长视距规划目标的搜索时间。我们的方法背后的概念思想是,我们希望优先考虑那些在演示中更经常发生的操作员,而不是那些没有经常观察到的操作员。因此,我们提议只将域限为最受欢迎的操作员。如果这一组操作员不足以找到一个计划,我们就反复扩大这一组操作员。我们表明,这大大缩短了长视距规划目标的搜索时间。