We present Neural A*, a novel data-driven search algorithm for path planning problems. Although data-driven planning has received much attention in recent years, little work has focused on how search-based methods can learn from demonstrations to plan better. In this work, we reformulate a canonical A* search algorithm to be differentiable and couple it with a convolutional encoder to form an end-to-end trainable neural network planner. Neural A* solves a path planning problem by (1) encoding a visual representation of the problem to estimate a movement cost map and (2) performing the A* search on the cost map to output a solution path. By minimizing the difference between the search results and ground-truth paths in demonstrations, the encoder learns to capture a variety of visual planning cues in input images, such as shapes of dead-end obstacles, bypasses, and shortcuts, which makes estimated cost maps informative. Our extensive experiments confirmed that Neural A* (a) outperformed state-of-the-art data-driven planners in terms of the search optimality and efficiency trade-off and (b) predicted realistic pedestrian paths by directly performing a search on raw image inputs.

中文翻译：

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使用神经 A* 搜索的路径规划

我们提出了 Neural A*，这是一种用于路径规划问题的新型数据驱动搜索算法。尽管近年来数据驱动的规划受到了很多关注，但很少有工作关注基于搜索的方法如何从演示中学习以更好地规划。在这项工作中，我们将规范的 A* 搜索算法重新制定为可微分，并将其与卷积编码器耦合以形成端到端的可训练神经网络规划器。神经 A* 通过 (1) 对问题的视觉表示进行编码以估计移动成本图和 (2) 在成本图上执行 A* 搜索以输出解决方案路径来解决路径规划问题。通过最小化演示中搜索结果和地面实况路径之间的差异，编码器学习捕捉输入图像中的各种视觉规划线索，例如尽头障碍物、绕行和捷径的形状，这使得估计成本图提供信息。我们广泛的实验证实，神经 A* (a) 在搜索最优性和效率权衡方面优于最先进的数据驱动规划器，以及 (b) 通过直接对原始图像执行搜索来预测真实的行人路径输入。