Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Path Planning using Neural A* Search   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. Project page:
https://omron-sinicx.github.io/neural-astar/

28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci