  Heuristic search is often used for motion planning and pathfinding problems,
for finding the shortest path in a graph while also promising completeness and
optimal efficiency. The drawback is it's space complexity, specifically storing
all expanded child nodes in memory and sorting large lists of active nodes,
which can be a problem in real-time scenarios with limited on-board
computation. To combat this, we present the Search with Learned Optimal
Pruning-based Expansion (SLOPE), which, learns the distance of a node from a
possible optimal path, unlike other approaches that learn a cost-to-go value.
The unfavored nodes are then pruned according to the said distance, which in
turn reduces the size of the open list. This ensures that the search explores
only the region close to optimal paths while lowering memory and computational
costs. Unlike traditional learning methods, our approach is orthogonal to
estimating cost-to-go heuristics, offering a complementary strategy for
improving search efficiency. We demonstrate the effectiveness of our approach
evaluating it as a standalone search method and in conjunction with learned
heuristic functions, achieving comparable-or-better node expansion metrics,
while lowering the number of child nodes in the open list. Our code is
available at https://github.com/dbokan1/SLOPE.
