Collision-Free Is Not Enough: Passage-Conditioned Navigation for Wheeled-Legged Robots

A collision-free detour can still leave a robot unable to negotiate the next stair or ledge. For wheeled-legged robots, negotiating terrain depends on approach geometry, speed, and gait as well as obstacle clearance. We introduce passage-conditioned navigation: a persistent reference combines demonstrated geometry and gait events with configured rules, while observations and execution feedback guide local adaptation with a fixed locomotion library. On a teaching-compiled route, event-conditioned handling reduces mean lateral entry error from 13.43 to 0.56 cm across 98 deterministic kinematic rollouts. In matched contact-simulation perturbation cases across six segments of a separate, frozen mapping-derived reference, rejoining reduces mean terminal route deviation from 1.14 m to 0.11 m and raises ordered completion from 0% to 59.4%. Across 2,800 contact-simulation trials, the live-sensing reference achieves 62.6% completion, exceeding two graph-based route alternatives by 18.6 and 19.0 percentage points with the same executor. An outdoor deployment traverses successive terrain transitions using factory gait controllers; the navigation stack also runs on an Apple M1 laptop. A geometry stress test exposes limits to transfer. Together, these results show how stored passage conditions and reference-based recovery support local adaptation, and where controller and observation interactions still constrain reliability.