ReViP: 視覚-固有感覚の再バランスによる視覚-言語-行動モデルの偽完了の緩和
ReViP: Mitigating False Completion in Vision-Language-Action Models with Vision-Proprioception Rebalance
VLAモデルが固有感覚に過度に依存して生じる「偽完了」問題を分析し、進捗を考慮した視覚的手がかりで視覚と言語の接地を強化する新フレームワークReViPを提案した。
詳しい要約
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著者: Zhuohao Li, Yinghao Li, Jian-Jian Jiang, Lang Zhou, Tianyu Zhang, Jiadong Yin, Mu Lin, Yi-Lin Wei, Wei-Shi Zheng
分類: cs.RO, cs.CV
原文アブストラクト
Vision-Language-Action (VLA) models have advanced robotic manipulation by combining vision, language, and proprioception to predict actions. However, previous methods fuse proprioceptive signals directly with vision-language features, resulting in state-dominant bias and \textbf{false completions} despite visible execution failures. We systematically analyze this failure mode, attributing it to modality imbalance, where policies overly rely on internal state progression and underuse visual evidence. To address this, we introduce the first \textbf{False-Completion Benchmark Suite}, featuring eight tasks with three controlled perturbations (\emph{Object Drop}, \emph{Distractor Swap}, \emph{Relayout}) to comprehensively evaluate false completion. Moreover, we propose \textbf{ReViP}, a novel VLA framework with \textbf{Vi}sion-\textbf{P}roprioception \textbf{Re}balance to enhance visual grounding and robustness under perturbations. The key insight is to introduce auxiliary \emph{progress-aware visual cues} to adaptively modulate the coupling between semantic perception and proprioceptive dynamics. Specifically, progress-aware visual cues are extracted by an external Task-Stage Observer, which performs task-relevant reasoning on real-time observations to drive task-stage feature-wise linear modulation, enhancing environmental awareness and mitigating state-driven errors. Extensive experiments show that ReViP effectively mitigates false completion and improves success rates over strong VLA baselines, achieving a \textbf{26\%} gain over $\pi_0$ model on our suite, with gains extending to LIBERO, RoboTwin 2.0, and real-world evaluations.
