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.