Map2Motion: Brain-Inspired Prospective Planning from Structural Cognitive Maps to Executable Motion

Mammalian navigation suggests a computational regime in which compact structural knowledge is continuously grounded by prospective computation to support flexible long-horizon behavior. In contrast, conventional robotic navigation typically relies on dense metric representations, accurate localization, and globally specified trajectories, whose storage, computation, and metric-consistency requirements grow with environment scale. Inspired by structural cognitive maps and prospective neural sequences, we propose Map2Motion, a cognitive-map-guided prospective motion planning converting structural cognitive maps to executable motion. Map2Motion conditions a flow-based distribution of prospective motions on structural guidance and current perception, and learns a forward dynamics model to predict the generated motions' embodied future states and traversability for closed-loop selection and execution. Our experimental result on MP3D benchmark achieves an 83% success rate (SR) with only 4.7% of the global map storage of dense-map baselines, while retaining 64% SR under 0.2 m localization error. In $3,029.4{\mathrm{m}}^2$ real-world building navigation tasks, it achieves 78% SR with 19.32 ms average planning latency and only 2.6% of dense-map storage, indicating its efficiency and capability in practical scenarios.