  Traditional stochastic optimal control methods that attempt to obtain an
optimal feedback policy for nonlinear systems are computationally intractable.
In this paper, we derive a decoupling principle between the open loop plan, and
the closed loop feedback gains, that leads to a perturbation feedback control
based solution to optimal control problems under action uncertainty, that is
near-optimal to the third order. Extensive numerical simulations validate the
theory, revealing a wide range of applicability, coping with medium levels of
noise. The performance is compared with Nonlinear Model Predictive Control in
several difficult robotic planning and control examples that show near
identical performance to NMPC while requiring much lesser computational effort.
It also leads us to raise the bigger question as to why NMPC should be used in
robotic control as opposed to perturbation feedback approaches.
