経験から学ぶロボットナビゲーション：原理・手法・最近の成果
Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results
幾何学的な地図と計画に頼らず、実世界の経験からナビゲーションを学習する手法を統一的に整理し、設計原理と実験結果、今後の課題を論じた記事。
著者: Sergey Levine, Dhruv Shah
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Navigation is one of the most heavily studied problems in robotics, and is conventionally approached as a geometric mapping and planning problem. However, real-world navigation presents a complex set of physical challenges that defies simple geometric abstractions. Machine learning offers a promising way to go beyond geometry and conventional planning, allowing for navigational systems that make decisions based on actual prior experience. Such systems can reason about traversability in ways that go beyond geometry, accounting for the physical outcomes of their actions and exploiting patterns in real-world environments. They can also improve as more data is collected, potentially providing a powerful network effect. In this article, we present a general toolkit for experiential learning of robotic navigation skills that unifies several recent approaches, describe the underlying design principles, summarize experimental results from several of our recent papers, and discuss open problems and directions for future work.
