PathPainter: Transferring the Generalization Ability of Image Generation Models to Embodied Navigation

Abstract:Bird's-eye-view (BEV) images have been widely demonstrated to provide valuable prior information for navigation. Given the global information provided by such views, two key challenges remain: how to fully exploit this information and how to reliably use it during execution. In this paper, we propose a navigation system that uses BEV images as global priors and is designed for ground and near-ground robotic platforms. The system employs an image generation model to interpret human intent from natural language, identify the target destination, and generate traversability masks. During execution, we introduce cross-view localization to align the robot's odometry with the BEV map and mitigate long-term drift in conventional odometry. We conduct extensive benchmark experiments to evaluate the proposed method and further validate it on a UAV platform. Using only a conventional local motion planner, the UAV successfully completes a 160-meter outdoor long-range navigation task. This work demonstrates how the world-understanding capabilities of foundation models can be transferred to embodied navigation, enabling robots to benefit from the strong generalization ability of existing image generation models.

Work in the progress. 16 pages, 13 figures

Robotics (cs.RO)

arXiv:2605.07496 [cs.RO]

(or arXiv:2605.07496v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.07496 Focus to learn more arXiv-issued DOI via DataCite

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