中文标题#
基於搜索的導航規劃學習局部啟發式方法
英文标题#
Learning Local Heuristics for Search-Based Navigation Planning
中文摘要#
圖搜索規劃算法用於導航通常嚴重依賴啟發式方法以高效規劃路徑。 因此，雖然這些方法不需要訓練階段並可以直接規劃長視野路徑，但它們通常需要仔細的手動設計信息性啟發函數。 最近的研究開始通過使用機器學習來學習引導搜索算法的啟發函數，從而繞過手動設計的啟發函數。 儘管這些方法可以從原始輸入中學習複雜的啟發函數，但它們 i) 需要顯著的訓練階段，並且 ii) 在新的地圖和更長的視野路徑上泛化效果不佳。 我們的貢獻是表明，與其學習全局啟發估計，我們可以通過定義和學習局部啟發式方法，從而顯著減小學習問題並提高泛化能力。 我們證明，使用這樣的局部啟發式方法可以減少節點擴展 2-20 倍，同時保持有限的次優性，易於訓練，並能推廣到新地圖和長視野計劃。
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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