Our paper “The Provable Virtue of Laziness in Motion Planning” has been selected to receive the best paper award at the 28th International Conference on Automated Planning and Scheduling, out of 209 submissions. The paper is joint work with Nika Haghtalab (CMU), Simon Mackenzie (CMU), Oren Salzman (CMU), and Sidd Srinivasa (UW).
Abstract: The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along shortest paths between the source and target. These algorithms were designed to minimize the number of edge evaluations in settings where edge evaluation dominates the running time of the algorithm; but how close to optimal are LazySP algorithms in terms of this objective? Our main result is an analytical upper bound, in a probabilistic model, on the number of edge evaluations required by LazySP algorithms; a matching lower bound shows that these algorithms are asymptotically optimal in the worst case.