WorldMAP: Bootstrapping Vision-Language Navigation Trajectory Prediction with Generative World Models
Vision-language models (VLMs) and generative world models are opening new opportunities for embodied navigation. VLMs are increasingly used as direct planners or trajectory predictors, while world models support look-ahead reasoning by imagining future views. Yet predicting a reliable trajectory from a single egocentric observation remains challenging. Current VLMs often generate unstable trajectories, and world models, though able to synthesize plausible futures, do not directly provide the grounded signals needed for navigation learning. This raises a central question: how can generated futures be turned into supervision for grounded trajectory prediction? We present WorldMAP, a teacher...
| Authors | Hongjin Chen, Shangyun Jiang, Tonghua Su et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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