  Lazy search algorithms can efficiently solve problems where edge evaluation
is the bottleneck in computation, as is the case for robotic motion planning.
The optimal algorithm in this class, LazySP, lazily restricts edge evaluation
to only the shortest path. Doing so comes at the expense of search effort,
i.e., LazySP must recompute the search tree every time an edge is found to be
invalid. This becomes prohibitively expensive when dealing with large graphs or
highly cluttered environments. Our key insight is the need to balance both edge
evaluation and search effort to minimize the total planning time. Our
contribution is two-fold. First, we propose a framework, Generalized Lazy
Search (GLS), that seamlessly toggles between search and evaluation to prevent
wasted efforts. We show that for a choice of toggle, GLS is provably more
efficient than LazySP. Second, we leverage prior experience of edge
probabilities to derive GLS policies that minimize expected planning time. We
show that GLS equipped with such priors significantly outperforms competitive
baselines for many simulated environments in R2, SE(2) and 7-DoF manipulation.
