  Achieving highly accurate dynamic or simulator models that are close to the
real robot can facilitate model-based controls (e.g., model predictive control
or linear-quadradic regulators), model-based trajectory planning (e.g.,
trajectory optimization), and decrease the amount of learning time necessary
for reinforcement learning methods. Thus, the objective of this work is to
learn the residual errors between a dynamic and/or simulator model and the real
robot. This is achieved using a neural network, where the parameters of a
neural network are updated through an Unscented Kalman Filter (UKF)
formulation. Using this method, we model these residual errors with only small
amounts of data -- a necessity as we improve the simulator/dynamic model by
learning directly from real-world operation. We demonstrate our method on
robotic hardware (e.g., manipulator arm, and a wheeled robot), and show that
with the learned residual errors, we can further close the reality gap between
dynamic models, simulations, and actual hardware.
