Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large datasets that are impractical to gather. We present SPARK and FLAME , two experience-based frameworks for sampling-based planning applicable to complex manipulators in 3 D environments. Both combine samplers associated with features from a workspace decomposition into a global biased sampling distribution. SPARK decomposes the environment based on exact geometry while FLAME is more general, and uses an octree-based decomposition obtained from sensor data. We demonstrate the effectiveness of SPARK and FLAME on a Fetch robot tasked with challenging pick-and-place manipulation problems. Our approaches can be trained incrementally and significantly improve performance with only a handful of examples, generalizing better over diverse tasks and environments as compared to prior approaches.
翻译:先前的工作表明,重复使用先前运动规划问题的经验可以提高类似、未来运动规划查询的效率,但是,对于具有多种自由度的机器人来说,这些方法在不同环境中的概括性较差,往往需要收集大量不切实际的数据集。我们介绍了SPARK和FLAME,这是适用于3D环境中复杂操纵者的基于抽样规划的两个基于经验的框架。两者结合了与工作空间分解的特征有关的取样器,将其纳入全球有偏差的抽样分布。SPARK根据精确的几何学分分解环境,而FLAME则比较一般,使用从传感器数据中获得的基于octree的分解法。我们展示了SPARK和FLAME在负责挑战选用和选用操作问题的寻回机器人上的有效性。我们的方法只能通过几个例子逐步培训,大大改进绩效,比以前的方法更加概括不同的任务和环境。