FlexPath: Adapting Learned Connectivity Guidance to Path Preferences

Recent learning-based path planners use neural networks to process occupancy representations and approximate heuristics for classical search algorithms, yielding near-optimal paths with reduced search effort. However, these methods are tied to a fixed objective, usually the shortest-path objective, implicit in their supervision. This limits their flexibility to accommodate alternative criteria. We introduce FlexPath, a two-stage learned search-guidance framework that first learns a recall-oriented connectivity prior initialized from shortest-path planner demonstrations and then refines this prior using differentiable path-shape objectives, thereby separating demonstration-based learning of connectivity-biased guidance from subsequent objective specific refinement. Beyond enabling adaptation to new routing preferences, the two-stage procedure improves standard shortest-path planning itself: on TMP, FlexPath improves optimal-path recovery from 75.0% to 88.6% over TransPath while reducing search expansions by 13.8%. Ablations show that neither prior learning nor objective fine-tuning alone matches the full pipeline; their combination yields the strongest path cost and search efficiency. We further demonstrate the preference adaptation by adapting guidance to non-shortest-path objectives such as obstacle clearance, class-conditioned obstacle clearance and waypoint following. For clearance with dmin=2, FlexPath achieves 96.2% full clearance satisfaction on feasible instances while maintaining low search effort, and it reaches 98.4% waypoint-following success.