中文标题#
基于搜索的导航规划学习局部启发式方法
英文标题#
Learning Local Heuristics for Search-Based Navigation Planning
中文摘要#
图搜索规划算法用于导航通常严重依赖启发式方法以高效规划路径。 因此，虽然这些方法不需要训练阶段并可以直接规划长视野路径，但它们通常需要仔细的手动设计信息性启发函数。 最近的研究开始通过使用机器学习来学习引导搜索算法的启发函数，从而绕过手动设计的启发函数。 尽管这些方法可以从原始输入中学习复杂的启发函数，但它们 i) 需要显著的训练阶段，并且 ii) 在新的地图和更长的视野路径上泛化效果不佳。 我们的贡献是表明，与其学习全局启发估计，我们可以通过定义和学习局部启发式方法，从而显著减小学习问题并提高泛化能力。 我们证明，使用这样的局部启发式方法可以减少节点扩展 2-20 倍，同时保持有限的次优性，易于训练，并能推广到新地图和长视野计划。
英文摘要#
Graph search planning algorithms for navigation typically rely heavily on heuristics to efficiently plan paths. As a result, while such approaches require no training phase and can directly plan long horizon paths, they often require careful hand designing of informative heuristic functions. Recent works have started bypassing hand designed heuristics by using machine learning to learn heuristic functions that guide the search algorithm. While these methods can learn complex heuristic functions from raw input, they i) require a significant training phase and ii) do not generalize well to new maps and longer horizon paths. Our contribution is showing that instead of learning a global heuristic estimate, we can define and learn local heuristics which results in a significantly smaller learning problem and improves generalization. We show that using such local heuristics can reduce node expansions by 2-20x while maintaining bounded suboptimality, are easy to train, and generalize to new maps & long horizon plans.
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