  Robot motion planning involves computing a sequence of valid robot
configurations that take the robot from its initial state to a goal state.
Solving a motion planning problem optimally using analytical methods is proven
to be PSPACE-Hard. Sampling-based approaches have tried to approximate the
optimal solution efficiently. Generally, sampling-based planners use uniform
samplers to cover the entire state space. In this paper, we propose a
deep-learning-based framework that identifies robot configurations in the
environment that are important to solve the given motion planning problem.
These states are used to bias the sampling distribution in order to reduce the
planning time. Our approach works with a unified network and generates
domain-dependent network parameters based on the environment and the robot. We
evaluate our approach with Learn and Link planner in three different settings.
Results show significant improvement in motion planning times when compared
with current sampling-based motion planners.
