CoverLib: Classifiers-equipped Experience Library by Iterative Problem Distribution Coverage Maximization for Domain-tuned Motion Planning

Abstract--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. As a result, it achieves both fast planning and high success rates over the problem domain. Similarly, in home service OTION planning has been studied from two ends of the spectrum: global and local methods. Global robotics, although the tasks are diverse, the tasks that act as methods, such as sampling-based motion planners (SBMP) bottlenecks are often known in advance (e.g., reaching into a like Probabilistic Roadmap (PRM) [1] and Rapidly-exploring narrow container). Random Tree (RRT) [2], are expected to find a solution if one exists, given enough time. However, these methods often A promising approach to this end is to use a library of require long and varying amount of computational time to experiences [5]-[10] reviewed in Section II-A.