  Accurately modeling robot dynamics is crucial to safe and efficient motion
control. In this paper, we develop and apply an iterative learning
semi-parametric model, with a neural network, to the task of autonomous racing
with a Model Predictive Controller (MPC). We present a novel non-linear
semi-parametric dynamics model where we represent the known dynamics with a
parametric model, and a neural network captures the unknown dynamics. We show
that our model can learn more accurately than a purely parametric model and
generalize better than a purely non-parametric model, making it ideal for
real-world applications where collecting data from the full state space is not
feasible. We present a system where the model is bootstrapped on pre-recorded
data and then updated iteratively at run time. Then we apply our iterative
learning approach to the simulated problem of autonomous racing and show that
it can safely adapt to modified dynamics online and even achieve better
performance than models trained on data from manual driving.
