While large vision-language models (VLMs) are increasingly adopted as the perceptual backbone for embodied agents, their fundamental vision-language grounding (VLG) capability in embodied scenarios remains unexplored. We present \textbf{EPIC-Bench}, \textbf{E}mbodied \textbf{P}ercept\textbf{I}on Ben\textbf{C}hmark, a fine-grained grounding benchmark designed to systematically evaluate the VLG capabilities of VLMs in real-world embodied environments. EPIC-Bench comprises 6.6k meticulously human-annotated tuples (Image, Text, Mask) spanning three core stages of the embodied interaction pipeline: Target Localization, Navigation, and Manipulation. With 23 fine-grained tasks, multi-dimensional settings and evaluation over 80 leading VLMs, EPIC-Bench provides a diagnostic analysis.

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