AI RESEARCH
WorldVLN: Autoregressive World Action Model for Aerial Vision-Language Navigation
arXiv CS.CV
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ArXi:2605.15964v1 Announce Type: cross Aerial vision-language navigation (VLN) requires agents to follow natural-language instructions through closed-loop perception and action in 3D environments. We argue that aerial VLN can be formulated as a prediction-driven world-action problem: the agent should anticipate latent world evolution and act according to the predicted consequences. To this end, we propose WorldVLN, the first autoregressive world action model for aerial