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iA∗: Imperative Learning-based A* Search for Pathfinding

Core Concepts
iA∗ proposes a novel pathfinding framework combining imperative learning and A* search to improve efficiency and reduce search area.
I. Introduction Pathfinding is crucial for robot navigation. Classic methods like A* face scalability issues. Data-driven approaches lack theoretical guarantees. II. Methodology iA∗ combines imperative learning with A* search. Bilevel optimization process enhances efficiency. III. Experiments iA∗ surpasses classical and data-driven methods in efficiency. Demonstrates superior robustness across tasks and environments.
"Our comprehensive experiments demonstrate that iA∗ surpasses both classical and data-driven methods in pathfinding efficiency." "The experimental results demonstrate that iA∗ outperforms both classical and learning-based pathfinding methods."
"To improve search efficiency and add interpretability, we propose an IL-based A* search framework (iA*) that includes a bilevel optimization process." "Experimental results demonstrate that the proposed iA* outperforms both classical and learning-based pathfinding methods."

Key Insights Distilled From

by Xiangyu Chen... at 03-26-2024

Deeper Inquiries

How can the integration of imperative learning enhance traditional pathfinding algorithms


What are the potential limitations of relying solely on data-driven approaches for pathfinding

純粋にデータ駆動アプローチだけに依存することの潜在的な制限は何でしょうか? パス探索の場合、「ブラックボックス」特性や大量の正確なラベリング作業への依存度が挙げられます。これらの方法はしばしば解釈可能性や一般化能力に欠けるため、新しい状況や変数条件下で十分なパフォーマンスを発揮しづらい傾向があります。

How might the principles of imperative learning be applied to other areas beyond robotics