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Planning Under Noise (PUN): A Neural Operator Planning Method Under Noise

PUN (Planning Under Noise) is a neural operator that learns noise-aware value fields to enable motion planning with control execution noise in unseen geometric environments. It changes obstacle boundary conditions to reflecting Neumann conditions and sets a Dirichlet condition at the goal, and extends the Monte Carlo solver Walk on Stars (WoSt) to higher dimensions, learning the mapping from geometry to value field from noisy, partially converged labels.

Part of the event: PUN neural operator enables faster planning under noise

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