CoverLib: 問題分布の反復的被覆最大化による分類器付き経験ライブラリとドメイン特化型動作計画
CoverLib: Classifiers-equipped Experience Library by Iterative Problem Distribution Coverage Maximization for Domain-tuned Motion Planning
経験と分類器のペアを反復的に追加して問題空間を効率的に被覆するライブラリを構築し、高速かつ高成功率な動作計画を実現する手法を提案。
著者: Hirokazu Ishida, Naoki Hiraoka, Kei Okada, Masayuki Inaba
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Library-based methods are known to be very effective for fast motion planning by adapting an experience retrieved from a precomputed library. This article presents CoverLib, a principled approach for constructing and utilizing such a library. CoverLib iteratively adds an experience-classifier-pair to the library, where each classifier corresponds to an adaptable region of the experience within the problem space. This iterative process is an active procedure, as it selects the next experience based on its ability to effectively cover the uncovered region. During the query phase, these classifiers are utilized to select an experience that is expected to be adaptable for a given problem. Experimental results demonstrate that CoverLib effectively mitigates the trade-off between plannability and speed observed in global (e.g. sampling-based) and local (e.g. optimization-based) methods. As a result, it achieves both fast planning and high success rates over the problem domain. Moreover, due to its adaptation-algorithm-agnostic nature, CoverLib seamlessly integrates with various adaptation methods, including nonlinear programming-based and sampling-based algorithms.
