In this paper, we consider the problem of discovering dynamical system models
from noisy data. The presence of noise is known to be a significant problem for
symbolic regression algorithms. We combine Gaussian process regression, a
nonparametric learning method, with SINDy, a parametric learning approach, to
identify nonlinear dynamical systems from data. The key advantages of our
proposed approach are its simplicity coupled with the fact that it demonstrates
improved robustness properties with noisy data over SINDy. We demonstrate our
proposed approach on a Lotka-Volterra model and a unicycle dynamic model in
simulation and on an NVIDIA JetRacer system using hardware data. We demonstrate
improved performance over SINDy for discovering the system dynamics and
predicting future trajectories.Comment: Submitted to ICRA 202