论文标题
使用本地3D工作区分解来学习采样分布，以高维度进行运动计划
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions
论文作者
论文摘要
较早的工作表明，先前的运动计划问题的重复经验可以提高类似的未来运动计划查询的效率。但是，对于具有多个自由度的机器人来说，这些方法在不同环境之间表现出较差的概括，并且通常需要大型数据集，而这些数据集则是不切实际的。我们介绍了Spark and Flame，这是两个基于经验的框架，用于基于抽样的计划，适用于3 d环境中的复杂操纵器。两者都将与工作区分解功能相关的采样器结合到全局偏见的采样分布中。 Spark根据精确的几何形状分解环境，而火焰更为笼统，并使用从传感器数据获得的基于OCTREE的分解。我们证明了火花和火焰对具有挑战性采摘操作问题的摘要机器人的有效性。我们的方法可以通过少数示例进行逐步训练，并显着提高性能，与先前的方法相比，更好地概括了各种任务和环境。
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.
