LEGO：利用路线图生成经验进行基于采样的规划
LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning
摘要：
我们考虑的问题，利用以前的经验，以产生基于采样的运动规划路线图。理想的路线图是稀疏的，允许快速搜索，节点分散在关键位置，这样就存在低成本的可行路径。一种越来越流行的方法是学习节点的分布，以产生这样的路线图。最先进的是训练一个条件变分自动编码器（CVAE）的先验数据集与最短路径作为目标输入。虽然这在许多问题上是非常有效的，但我们表明，它可能会在复杂的障碍配置或训练和测试之间的不匹配面前失败。我们提出了一个算法乐高，解决了这些问题，通过训练CVAE与目标样本，满足两个重要的标准。首先，这些样本仅属于沿接近最优路径的瓶颈区域，否则难以用均匀采样器进行采样。其次，这些样本分布在不同的区域，以最大限度地提高可行路径存在的可能性。我们正式定义这些属性，并证明乐高的性能保证。我们广泛地评估了乐高的一系列规划问题，包括机器人手臂规划，并报告了显着的收益，以及学习的基线。
英文摘要：
We consider the problem of leveraging prior experience to generate roadmaps in sampling-based motion planning. A desirable roadmap is one that is sparse, allowing for fast search, with nodes spread out at key locations such that a low-cost feasible path exists. An increasingly popular approach is to learn a distribution of nodes that would produce such a roadmap. State-of-the-art is to train a conditional variational auto-encoder (CVAE) on the prior dataset with the shortest paths as target input. While this is quite effective on many problems, we show it can fail in the face of complex obstacle configurations or mismatch between training and testing. We present an algorithm LEGO that addresses these issues by training the CVAE with target samples that satisfy two important criteria. Firstly, these samples belong only to bottleneck regions along near-optimal paths that are otherwise difficult-to-sample with a uniform sampler. Secondly, these samples are spread out across diverse regions to maximize the likelihood of a feasible path existing. We formally define these properties and prove performance guarantees for LEGO. We extensively evaluate LEGO on a range of planning problems, including robot arm planning, and report significant gains over heuristics as well as learned baselines.
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