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We consider the problem of robotic planning under uncertainty. This problemmay be posed as a stochastic optimal control problem, complete solution towhich is fundamentally intractable owing to the infamous curse ofdimensionality. We report the results of an extensive simulation study in whichwe have compared two methods, both of which aim to salvage tractability byusing alternative, albeit inexact, means for treating feedback. The first is arecently proposed method based on a near-optimal "decoupling principle" fortractable feedback design, wherein a nominal open-loop problem is solved,followed by a linear feedback design around the open-loop. The second is ModelPredictive Control (MPC), a widely-employed method that uses repeatedre-computation of the nominal open-loop problem during execution to correct fornoise, though when interpreted as feedback, this can only said to be animplicit form. We examine a much wider range of noise levels than have beenpreviously reported and empirical evidence suggests that the decoupling methodallows for tractable planning over a wide range of uncertainty conditionswithout unduly sacrificing performance.

Experiments with Tractable Feedback in Robotic Planning under Uncertainty: Insights over a wide range of noise regimes (Extended Report)