LangGap：視覚-言語-行動モデルにおける言語ギャップの診断と解消
LangGap: Diagnosing and Closing the Language Gap in Vision-Language-Action Models
VLAモデルが言語指示を無視しがちな問題を診断するため、同一配置で指示文だけを変える4次元の意味摂動ベンチマークLangGapを構築し、データ拡張による改善と限界を示した。
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著者: Yuchen Hou, Lin Zhao
分類: cs.RO, cs.AI, cs.CL, cs.CV, cs.LG
原文アブストラクト
Vision-Language-Action (VLA) models achieve over 95% success on standard benchmarks. However, through systematic experiments, we find that current state-of-the-art VLA models largely ignore language instructions. Prior work lacks: (1) systematic semantic perturbation diagnostics, (2) a benchmark that forces language understanding by design, and (3) linguistically diverse training data. This paper constructs the LangGap benchmark, based on a four-dimensional semantic perturbation method -- varying instruction semantics while keeping the tabletop layout fixed -- revealing language understanding deficits in {\pi}0.5. Existing benchmarks like LIBERO assign only one task per layout, underutilizing available objects and target locations; LangGap fully diversifies pick-and-place tasks under identical layouts, forcing models to truly understand language. Experiments show that targeted data augmentation can partially close the language gap -- success rate improves from 0% to 90% with single-task training, and 0% to 28% with multi-task training. However, as semantic diversity of extended tasks increases, model learning capacity proves severely insufficient; even trained tasks perform poorly. This reveals a fundamental challenge for VLA models in understanding diverse language instructions -- precisely the long-term value of LangGap.
