  In this paper we propose a novel decision making architecture for Robust
Model Predictive Path Integral control (RMPPI) and investigate its performance
guarantees and applicability to off-road navigation. Key building blocks of the
proposed architecture are an augmented state space representation of the system
consisting of nominal and actual dynamics, a placeholder for different types of
tracking controllers, a safety logic for nominal state propagation, and an
importance sampling scheme that takes into account the capabilities of the
underlying tracking control. Using these ingredients, we derive a bound on the
free energy growth of the dynamical system which is a function of task
constraint satisfaction level, the performance of the underlying tracking
controller, and the sampling error of the stochastic optimization used within
RMPPI. To validate the bound on free energy growth, we perform experiments in
simulation using two types of tracking controllers, namely the iterative Linear
Quadratic Gaussian and Contraction-Metric based control. We further demonstrate
the applicability of RMPPI in real hardware using the GT AutoRally vehicle. Our
experiments demonstrate that RMPPI outperforms MPPI and Tube-MPPI by
alleviating issues of the aforementioned model predictive controllers related
to either lack of robustness or excessive conservatism. RMPPI provides the best
of the two worlds in terms of agility and robustness to disturbances.
