Neural operators can efficiently learn noise-aware planning by amortizing expensive PDE solutions during training, enabling fast, reliable navigation in high-dimensional spaces with execution uncertainty.
This paper introduces PUN, a neural operator that learns to plan under control noise by predicting value fields for navigation in unseen environments.