AI RESEARCH

Planner-Admissible Graph-PDE Value Extensions for Sparse Goal-Conditioned Planning

arXiv CS.AI

ArXi:2605.19185v1 Announce Type: cross Sparse goal-conditioned planning with few cost-to-go labels can be viewed as a graph-PDE Dirichlet extension problem: extend sparse labels on a goal-dependent boundary to unlabelled graph vertices so that greedy rollouts reach the goal. We study which graph value extensions are planner-admissible under the operational argmin-Q planner. Our main result is a local action-gap certificate: if the surrogate value error along the rollout stays below half the true action gap, then the greedy rollout reaches the goal.