  We present a control strategy that applies inverse dynamics to a learned
acceleration error model for accurate multirotor control input generation. This
allows us to retain accurate trajectory and control input generation despite
the presence of exogenous disturbances and modeling errors. Although accurate
control input generation is traditionally possible when combined with parameter
learning-based techniques, we propose a method that can do so while solving the
relatively easier non-parametric model learning problem. We show that our
technique is able to compensate for a larger class of model disturbances than
traditional techniques can and we show reduced tracking error while following
trajectories demanding accelerations of more than 7 m/s^2 in multirotor
simulation and hardware experiments.
