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Aerial traffic monitoring can drastically improve the adaptability of autonomous vehicles to new cities with minimal data collection efforts.
A single representation can be easily manipulated, but a balanced ensemble of encoders dramatically enhances image generation quality and robustness.
Visuomotor control can now generalize to unseen environments and instructions by grounding world models in a vision-language latent space, outperforming standard vision-language approaches by a large margin.
