  Teaching autonomous mobile robots to successfully navigate human crowds is a
challenging task. Not only does it require planning, but it requires
maintaining social norms which may differ from one context to another. Here we
focus on crowd navigation, using a neural network to learn specific strategies
in-situ with a robot. This allows us to take into account human behavior and
reactions toward a real robot as well as learn strategies that are specific to
various scenarios in that context. A CNN takes a top-down image of the scene as
input and outputs the next action for the robot to take in terms of speed and
angle. Here we present the method, experimental results, and quantitatively
evaluate our approach.
