  This work presents an approach to learn path planning for robot social
navigation by demonstration. We make use of Fully Convolutional Neural Networks
(FCNs) to learn from expert's path demonstrations a map that marks a feasible
path to the goal as a classification problem. The use of FCNs allows us to
overcome the problem of manually designing/identifying the cost-map and
relevant features for the task of robot navigation. The method makes use of
optimal Rapidly-exploring Random Tree planner (RRT*) to overcome eventual
errors in the path prediction; the FCNs prediction is used as cost-map and also
to partially bias the sampling of the configuration space, leading the planner
to behave similarly to the learned expert behavior. The approach is evaluated
in experiments with real trajectories and compared with Inverse Reinforcement
Learning algorithms that use RRT* as underlying planner.
