Michael Chen, Hong Chul Nam, Xi Deng, Anima Anandkumar · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28623 · open-access PDF
O planejamento sob ruído de execução de controle incerto requer políticas de feedback confiáveis em ambientes diversos. Introduzimos o Planning Under Noise (PUN), um operador neural que aprende campos de valor conscientes do ruído para planejamento em geometrias invisíveis. Com base numa formulação estabelecida de Poisson de controlo óptimo estocástico, abordamos dois desafios: especificar condições de fronteira que suportam a navegação em geometrias diferentes e computar eficientemente campos com gradientes confiáveis.
Machine translation (Meta M2M100); the English original follows.
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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