Decoupling stochastic optimal control problems for efficient solution: insights from experiments across a wide range of noise regimes
Systems and Control
2019-09-19 v1 Systems and Control
Abstract
We consider the problem of robotic planning under uncertainty in this paper. This problem may be posed as a stochastic optimal control problem, a solution to which is fundamentally intractable owing to the infamous "curse of dimensionality". Hence, we consider the extension of a "decoupling principle" that was recently proposed by some of the authors, wherein a nominal open-loop problem 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 "small noise" parameter, to a much wider range of noise levels. Our empirical evidence suggests that this allows for tractable planning over a wide range of uncertainty conditions without unduly sacrificing performance.
Cite
@article{arxiv.1909.08585,
title = {Decoupling stochastic optimal control problems for efficient solution: insights from experiments across a wide range of noise regimes},
author = {Mohamed Naveed Gul Mohamed and Suman Chakravorty and Dylan A. Shell},
journal= {arXiv preprint arXiv:1909.08585},
year = {2019}
}