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Pretrained VLMs bring rich world knowledge and semantic priors to object-goal navigation, and fine-tuning a VLM into a navigation policy has become a common recipe. Despite this progress, success rates remain low, and it is unclear which capability these fine-tuned policies lack. Our diagnosis identifies goal grounding as the dominant failure. These policies explore well, yet often fail to notice that the goal is already in view or to estimate how far away it is. To address this failure, we ground the goal explicitly. Explicit grounding substantially improves success and reaches…
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| Age | -12.3 |
| Primary source | 20 |
| Total | 7.7 |
These components add up to 7.699999999999999, not 7.7. The total is what ordered your brief; the difference means a component was not recorded, so treat this breakdown as incomplete rather than as the whole reason.