Vision-Language-Action (VLA) models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic variation remains poorly understood. In this work, we present the first systematic multilingual evaluation of VLA models by translating the LIBERO benchmark into ten languages, revealing severe performance degradation under non-English instructions, with success rates dropping by 30-50%. Through fine-grained analysis of task executions, we find that language influence is highly non-uniform across steps: certain steps exhibit strong language dependence and dominate overall task failure, while others are largely language-agnostic. Based on this insight, we propose a step-wise inference-time intervention that aligns representations according to step language sensitivity, substantially improving performance under linguistic variation. Our results indicate that language robustness in VLA models is fundamentally a step-wise control problem, highlighting the importance of temporally structured analysis for reliable embodied agents.

翻译：视觉-语言-动作（VLA）模型在语言条件机器人操作任务中展现出强大性能，但其对语言变化的鲁棒性仍知之甚少。本研究通过将LIBERO基准翻译为十种语言，首次对VLA模型进行系统性多语言评估，揭示了非英语指令下的严重性能退化，成功率下降30-50%。通过对任务执行的细粒度分析，我们发现语言影响在不同步骤间高度非均匀：某些步骤表现出强烈语言依赖性并主导整体任务失败，而其他步骤则基本与语言无关。基于此发现，我们提出一种分步推理时干预方法，根据步骤语言敏感性对齐表征，从而显著提升语言变化下的性能。结果表明，VLA模型的语言鲁棒性本质上是一个分步控制问题，凸显了时间结构化分析对可靠具身智能体的重要性。