Neural Navigation Functions for Zero-Shot Generalizable Motion Planning
We introduce Neural Navigation Functions (Neural-NF), a learned reactive navigation function capable of zero-shot transfer across unseen environment geometries. Neural-NF places data-driven adaptation within a structured elliptic planner, where the navigation objective is learned while planner structure is preserved by construction. Specifically, intrinsic Laplacian-derived features are mapped to local PDE coefficients, and solving the resulting boundary value problem produces a globally consistent value function on each target domain. For every admissible learned model, the resulting policy is collision-free, provides monotonic descent and a global minimum at the goal by construction. Th...
| Authors | Benjamin D. Shaffer, Pei-An Hsieh, Brooks Kinch et al. |
|---|---|
| Primary category | cs.RO |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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