  With the advent of machine learning, there have been several recent attempts
to learn effective and generalizable heuristics. Local Heuristic A* (LoHA*) is
one recent method that instead of learning the entire heuristic estimate,
learns a "local" residual heuristic that estimates the cost to escape a region
(Veerapaneni et al 2023). LoHA*, like other supervised learning methods,
collects a dataset of target values by querying an oracle on many planning
problems (in this case, local planning problems). This data collection process
can become slow as the size of the local region increases or if the domain
requires expensive collision checks. Our main insight is that when an A* search
solves a start-goal planning problem it inherently ends up solving multiple
local planning problems. We exploit this observation to propose an efficient
data collection framework that does <1/10th the amount of work (measured by
expansions) to collect the same amount of data in comparison to baselines. This
idea also enables us to run LoHA* in an online manner where we can iteratively
collect data and improve our model while solving relevant start-goal tasks. We
demonstrate the performance of our data collection and online framework on a 4D
$(x, y, \theta, v)$ navigation domain.
