  In this paper, we present a novel Model Predictive Control method for
autonomous robots subject to arbitrary forms of uncertainty. The proposed
Risk-Aware Model Predictive Path Integral (RA-MPPI) control utilizes the
Conditional Value-at-Risk (CVaR) measure to generate optimal control actions
for safety-critical robotic applications. Different from most existing
Stochastic MPCs and CVaR optimization methods that linearize the original
dynamics and formulate control tasks as convex programs, the proposed method
directly uses the original dynamics without restricting the form of the cost
functions or the noise. We apply the novel RA-MPPI controller to an autonomous
vehicle to perform aggressive driving maneuvers in cluttered environments. Our
simulations and experiments show that the proposed RA-MPPI controller can
achieve about the same lap time with significantly fewer collisions compared to
the baseline MPPI controller. The proposed controller performs on-line
computation at an update frequency of up to 80Hz, utilizing modern Graphics
Processing Units (GPUs) to multi-thread the generation of trajectories as well
as the CVaR values.
