  We propose a two-phase risk-averse architecture for controlling stochastic
nonlinear robotic systems. We present Risk-Averse Nonlinear Steering RRT*
(RANS-RRT*) as an RRT* variant that incorporates nonlinear dynamics by solving
a nonlinear program (NLP) and accounts for risk by approximating the state
distribution and performing a distributionally robust (DR) collision check to
promote safe planning. The generated plan is used as a reference for a
low-level tracking controller. We demonstrate three controllers: finite horizon
linear quadratic regulator (LQR) with linearized dynamics around the reference
trajectory, LQR with robustness-promoting multiplicative noise terms, and a
nonlinear model predictive control law (NMPC). We demonstrate the effectiveness
of our algorithm using unicycle dynamics under heavy-tailed Laplace process
noise in a cluttered environment.
