  In this paper we propose an algorithm for the training of neural network
control policies for quadrotors. The learned control policy computes control
commands directly from sensor inputs and is hence computationally efficient. An
imitation learning algorithm produces a policy that reproduces the behavior of
a path following control algorithm with collision avoidance. Due to the
generalization ability of neural networks, the resulting policy performs local
collision avoidance of unseen obstacles while following a global reference
path. The algorithm uses a time-free model predictive path-following controller
as a supervisor. The controller generates demonstrations by following few
example paths. This enables an easy to implement learning algorithm that is
robust to errors of the model used in the model predictive controller. The
policy is trained on the real quadrotor, which requires collision-free
exploration around the example path. An adapted version of the supervisor is
used to enable exploration. Thus, the policy can be trained from a relatively
small number of examples on the real quadrotor, making the training sample
efficient.
