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We consider the problem of robotic planning under uncertainty in this paper.This problem may be posed as a stochastic optimal control problem, a solutionto which is fundamentally intractable owing to the infamous "curse ofdimensionality". Hence, we consider the extension of a "decoupling principle"that was recently proposed by some of the authors, wherein a nominal open-loopproblem is solved followed by a linear feedback design around the open-loop,and which was shown to be near-optimal to second order in terms of a "smallnoise" parameter, to a much wider range of noise levels. Our empirical evidencesuggests that this allows for tractable planning over a wide range ofuncertainty conditions without unduly sacrificing performance.

Decoupling stochastic optimal control problems for efficient solution: insights from experiments across a wide range of noise regimes