  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.
