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Lazy search algorithms can efficiently solve problems where edge evaluationis the bottleneck in computation, as is the case for robotic motion planning.The optimal algorithm in this class, LazySP, lazily restricts edge evaluationto 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 beinvalid. This becomes prohibitively expensive when dealing with large graphs orhighly cluttered environments. Our key insight is the need to balance both edgeevaluation and search effort to minimize the total planning time. Ourcontribution is two-fold. First, we propose a framework, Generalized LazySearch (GLS), that seamlessly toggles between search and evaluation to preventwasted efforts. We show that for a choice of toggle, GLS is provably moreefficient than LazySP. Second, we leverage prior experience of edgeprobabilities to derive GLS policies that minimize expected planning time. Weshow that GLS equipped with such priors significantly outperforms competitivebaselines for many simulated environments in R2, SE(2) and 7-DoF manipulation.

Generalized Lazy Search for Robot Motion Planning: Interleaving Search and Edge Evaluation via Event-based Toggles