Credit-Guided Policy Improvement for Test-time Adaptive Vision-Language Navigation
Test-time adaptation for vision-language navigation (TTA-VLN) enables pretrained policies to adapt online to unseen environments using only test-time observations and interaction history. However, distribution shifts can distort local action preferences and lead to off-course decisions. Existing methods rely on predictive uncertainty, trajectory-level feedback, or accumulated adaptation experience to correct such deviations. These signals, however, do not directly reveal whether an executed action supports instruction-guided progress toward the goal. Moreover, a plausible corrective signal does not guarantee a reliable policy update. The key challenge is thus twofold: identifying interact...
| Authors | Yang Li, Sijia Zhang, Yihan Li et al. |
|---|---|
| Primary category | cs.RO |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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