Planning under Noise with Neural Operators

Michael Chen, Hong Chul Nam, Xi Deng, Anima Anandkumar · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28623 · open-access PDF

Abstract

Planning under uncertain control execution noise requires reliable feedback policies across diverse environments. We introduce Planning Under Noise (PUN), a neural operator that learns noise-aware value fields for planning in unseen geometries. Building on an established screened-Poisson formulation of stochastic optimal control, we address two challenges: specifying boundary conditions that support navigation across varying geometries and efficiently computing fields with reliable gradients. Zero-Dirichlet obstacle boundaries terminate sampled paths at walls, weakening the goal signal along l

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