  Derivative based optimization methods are efficient at solving optimal
control problems near local optima. However, their ability to converge halts
when derivative information vanishes. The inference approach to optimal control
does not have strict requirements on the objective landscape. However,
sampling, the primary tool for solving such problems, tends to be much slower
in computation time. We propose a new method that combines second order methods
with inference. We utilise the Kullback Leibler (KL) control framework to
formulate an inference problem that computes the optimal controls from an
adaptive distribution approximating the solution of the second order method.
Our method allows for combining simple convex and non convex cost functions.
This simplifies the process of cost function design and leverages the strengths
of both inference and second order optimization. We compare our method to Model
Predictive Path Integral (MPPI) and iterative Linear Quadratic Regulator
(iLQG), outperforming both in sample efficiency and quality on manipulation and
obstacle avoidance tasks.
