  Sampling-based model-predictive controllers have become a powerful
optimization tool for planning and control problems in various challenging
environments. In this paper, we show how the default choice of uncorrelated
Gaussian distributions can be improved upon with the use of a colored noise
distribution. Our choice of distribution allows for the emphasis on low
frequency control signals, which can result in smoother and more exploratory
samples. We use this frequency-based sampling distribution with Model
Predictive Path Integral (MPPI) in both hardware and simulation experiments to
show better or equal performance on systems with various speeds of input
response.
