  In recent years, learning-based approaches have revolutionized motion
planning. The data generation process for these methods involves caching a
large number of high quality paths for different queries (start, goal pairs) in
various environments. Conventionally, a uniform random strategy is used for
sampling these queries. However, this leads to inclusion of "trivial paths" in
the dataset (e.g.,, straight line paths in case of length-optimal planning),
which can be solved efficiently if the planner has access to a steering
function. This work proposes a "non-trivial" query sampling procedure to add
more complex paths in the dataset. Numerical experiments show that a higher
success rate can be attained for neural planners trained on such a non-trivial
dataset.
