  Path-planning algorithms are an important part of a wide variety of robotic
applications, such as mobile robot navigation and robot arm manipulation.
However, in large search spaces in which local traps may exist, it remains
challenging to reliably find a path while satisfying real-time constraints.
Efforts to speed up the path search have led to the development of many
practical path-planning algorithms. These algorithms often define a search
heuristic to guide the search towards the goal. The heuristics should be
carefully designed for each specific problem to ensure reliability in the
various situations encountered in the problem. However, it is often difficult
for humans to craft such robust heuristics, and the search performance often
degrades under conditions that violate the heuristic assumption. Rather than
manually designing the heuristics, in this work, we propose a learning approach
to acquire these search heuristics. Our method represents the environment
containing the obstacles as an image, and this image is fed into fully
convolutional neural networks to produce a search heuristic image where every
pixel represents a heuristic value (cost-to-go value to a goal) in the form of
a vertex of a search graph. Training the heuristic is performed using
previously collected planning results. Our preliminary experiments (2D grid
world navigation experiments) demonstrate significant reduction in the search
costs relative to a hand-designed heuristic.
