# PanoVLN

PanoVLN performs vision-and-language navigation in continuous environments with a 4B vision-language model and RGB-only panoramic input, combining longer action-sequence supervision, confidence-guided execution, decision-centric training data, and fused panoramic geometric features to reach 77.3% and 78.0% success rate on R2R-CE and RxR-CE Val-Unseen, exceeding the previous best by 11.9 and 8.7 percentage points, and enabling real-world quadruped navigation with fewer pauses.

PanoVLN performs vision-and-language navigation in continuous environments with a 4B vision-language model and RGB-only panoramic input, combining longer action-sequence supervision, confidence-guided execution, decision-centric training data, and fused panoramic geometric features to reach 77.3% and 78.0% success rate on R2R-CE and RxR-CE Val-Unseen, exceeding the previous best by 11.9 and 8.7 percentage points, and enabling real-world quadruped navigation with fewer pauses.