視覚分布シフト下でのOpenVLA失敗の早期警告信号
Early Warning Signals for OpenVLA Failure under Visual Distribution Shift
視覚変化によりVLAポリシーが失敗する前に、内部活性化に失敗予兆信号があるか調査。線形モニタで失敗を高精度に遡及識別できるが、クリーンなエピソードでも誤警報が多いことを示した。
著者: Dipesh Tharu Mahato, Rachel Ren
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Visual shifts can cause a vision-language-action policy to fail after initially plausible behavior. We ask whether OpenVLA's internal activations contain signals associated with the steps before failure. We freeze the policy, record one MLP activation per LIBERO-10 step, and fit two linear monitors. Occlusion reduces task success from $57\%$ to $17\%$. Within failed matched-reset trajectories, a layer-16 logistic probe attains AUROC $0.972$ and AUPRC $0.352$, whereas action disagreement attains AUROC $0.496$. Without refitting, the occlusion-trained probe reaches AUROC $0.689$ on failed camera-jitter episodes. In a calibration check, however, the same layer-16 monitor averages 3.32 warning onsets per clean episode. This contrast shows that strong retrospective discrimination does not imply operationally quiet warning behavior. Because fitting and evaluation share tasks, resets, and seed, these results establish retrospective separability rather than prediction on independent episodes.