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Graph3R is introduced, which unifies view graph initialization with feed-forward reconstruction with feed-forward reconstruction through a shared geometric representation, and takes a step toward end-to-end reconstruction from uncurated image collections.
This work proposes an alternative approach: fine-tuning VLMs to learn navigation interactions directly in 2D pixel space through autoregressive trajectory generation, and demonstrates that this flagship model achieves state-of-the-art level performance with relatively limited computational resources and training data.
HAM-VLN is presented, a decision-coupled, agent-authored memory that equips the robot with a persistent, depth-grounded world graph and reduces the context length by more than 65% compared to previous methods.
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