Path Planning using Neural A* Search

Ryo Yonetani · Tatsunori Taniai · Mohammadamin Barekatain · Mai Nishimura · Asako Kanezaki

[ Abstract ] [ Livestream: Visit Reinforcement Learning and Bandits ] [ Paper ]
Wed 21 Jul 7:05 a.m. — 7:10 a.m. PDT

We present Neural A, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, machine learning approaches to search-based planning are still challenging due to the discrete nature of search algorithms. 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 encoding a problem instance to a guidance map and then performing the differentiable A* search with the guidance map. By learning to match the search results with ground-truth paths provided by experts, Neural A* can produce a path consistent with the ground truth accurately and efficiently. Our extensive experiments confirmed that Neural A* outperformed state-of-the-art data-driven planners in terms of the search optimality and efficiency trade-off. Furthermore, Neural A* successfully predicted realistic human trajectories by directly performing search-based planning on natural image inputs.

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