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DiffWAM is presented, a geometry-conditioned navigation world-action model that directly transforms multi-level predictive features into continuous camera trajectories, providing a direct alternative to generate-then-reconstruct navigation pipelines.
NavGen is introduced, a text-to-video data generation pipeline that produces diverse vision-language navigation episodes across indoor and outdoor scenes and a style-diversification method that scales up long-tail data that is difficult and costly to collect.
A framework that combines decentralized asynchronous reasoning, lightweight information sharing, capability aware collaboration, and a unified action interface is proposed, enabling general purpose VLMs to generate robot specific actions executed by learning free experts without task or robot specific training.
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