  Sampling-based motion planners have experienced much success due to their
ability to efficiently and evenly explore the state space. However, for many
tasks, it may be more efficient to not uniformly explore the state space,
especially when there is prior information about its structure. Previous
methods have attempted to modify the sampling distribution using hand selected
heuristics that can work well for specific environments but not universally. In
this paper, a policy- search based method is presented as an adaptive way to
learn implicit sampling distributions for different environments. It utilizes
information from past searches in similar environments to generate better
distributions in novel environments, thus reducing overall computational cost.
Our method can be incor- porated with a variety of sampling-based planners to
improve performance. Our approach is validated on a number of tasks, including
a 7DOF robot arm, showing marked improvement in number of collision checks as
well as number of nodes expanded compared with baseline methods.
