  Fast and efficient path generation is critical for robots operating in
complex environments. This motion planning problem is often performed in a
robot's actuation or configuration space, where popular pathfinding methods
such as A*, RRT*, get exponentially more computationally expensive to execute
as the dimensionality increases or the spaces become more cluttered and
complex. On the other hand, if one were to save the entire set of paths
connecting all pair of locations in the configuration space a priori, one would
run out of memory very quickly. In this work, we introduce a novel way of
producing fast and optimal motion plans for static environments by using a
stepping neural network approach, called OracleNet. OracleNet uses Recurrent
Neural Networks to determine end-to-end trajectories in an iterative manner
that implicitly generates optimal motion plans with minimal loss in performance
in a compact form. The algorithm is straightforward in implementation while
consistently generating near-optimal paths in a single, iterative, end-to-end
roll-out. In practice, OracleNet generally has fixed-time execution regardless
of the configuration space complexity while outperforming popular pathfinding
algorithms in complex environments and higher dimensions
