  Traditional learning approaches proposed for controlling quadrotors or
helicopters have focused on improving performance for specific trajectories by
iteratively improving upon a nominal controller, for example learning from
demonstrations, iterative learning, and reinforcement learning. In these
schemes, however, it is not clear how the information gathered from the
training trajectories can be used to synthesize controllers for more general
trajectories. Recently, the efficacy of deep learning in inferring helicopter
dynamics has been shown. Motivated by the generalization capability of deep
learning, this paper investigates whether a neural network based dynamics model
can be employed to synthesize control for trajectories different than those
used for training. To test this, we learn a quadrotor dynamics model using only
translational and only rotational training trajectories, each of which can be
controlled independently, and then use it to simultaneously control the yaw and
position of a quadrotor, which is non-trivial because of nonlinear couplings
between the two motions. We validate our approach in experiments on a quadrotor
testbed.
