  Sampling based methods are widely used for robotic motion planning.
Traditionally, these samples are drawn from probabilistic ( or deterministic )
distributions to cover the state space uniformly. Despite being
probabilistically complete, they fail to find a feasible path in a reasonable
amount of time in constrained environments where it is essential to go through
narrow passages (bottleneck regions). Current state of the art techniques train
a learning model (learner) to predict samples selectively on these bottleneck
regions. However, these algorithms depend completely on samples generated by
this learner to navigate through the bottleneck regions. As the complexity of
the planning problem increases, the amount of data and time required to make
this learner robust to fine variations in the structure of the workspace
becomes computationally intractable. In this work, we present (1) an efficient
and robust method to use a learner to locate the bottleneck regions and (2) two
algorithms that use local sampling methods to leverage the location of these
bottleneck regions for efficient motion planning while maintaining
probabilistic completeness.
  We test our algorithms on 2 dimensional planning problems and 7 dimensional
robotic arm planning, and report significant gains over heuristics as well as
learned baselines.
