不確実性を臨界の瞬間へ：VLAモデルのための信頼できる不確実性定量化
Shifting Uncertainty to Critical Moments: Towards Reliable Uncertainty Quantification for VLA Model
VLAモデルのロールアウト成否予測において、最大値ベースのスライディングウィンドウと運動安定性重み付け、自由度適応キャリブレーションを組み合わせ、短時間の危険な不確実性スパイクを捉える手法を提案。
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著者: Yanchuan Tang, Taowen Wang, Yuefei Chen, Boxuan Zhang, Qiang Guan, Ruixiang Tang
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Vision-Language-Action (VLA) models enable general-purpose robotic policies by mapping visual observations and language instructions to low-level actions, but they often lack reliable introspection. A common practice is to compute a token-level uncertainty signal and take its mean over a rollout. However, mean aggregation can dilute short-lived but safety-critical uncertainty spikes in continuous control. In particular, successful rollouts may contain localized high-entropy segments due to benign noise or non-critical micro-adjustments, while failure rollouts can appear low-entropy for most timesteps and only exhibit brief spikes near the onset of failure. We propose a unified uncertainty quantification approach for predicting rollout success versus failure that (1) uses max-based sliding window pooling to preserve transient risk signals, (2) applies motion-aware stability weighting to emphasize high-frequency action oscillations associated with unstable behaviors, and (3) performs DoF-adaptive calibration via Bayesian Optimization to prioritize kinematically critical axes. Experiments on the LIBERO benchmark show that our method substantially improves failure prediction accuracy and yields more reliable signals for failure detection, which can support downstream human-in-the-loop interventions.
