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Can one navigation VLA serve robots with radically different movement capabilities? This episode explains how VAMOS separates image-space route generation from robot-specific feasibility scoring, and what that division contributes to real-world navigation. We follow three threads: how a common trajectory representation combines heterogeneous robot demonstrations; how simulation-trained affordance models influence route selection; and how the reported navigation results relate to the architecture's individual components. The discussion covers indoor and outdoor experiments, Boston Dynamics Spot, the wheeled UW Hound, and the distinction between navigation-only and language-steerable checkpoints. We also connect the method to SayCan's affordance grounding and HAMSTER's visual-path interface, then examine the released code and datasets from a practitioner's perspective. The central limitation is scene-specific planner fine-tuning in the cross-embodiment demonstration, which narrows what that experiment establishes about transfer. For researchers building hierarchical policies or integrating foundation models with existing controllers, the episode offers a concrete way to separate proposal quality, capability assessment, and execution. When a robot chooses the right route, which part of the system deserves the credit?
