EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models

Haozhe Shan; Xiancong Ren; Han Dong; Haoyuan Shi; Yingji Zhang; Jiayu Hu; Yi Zhang; Yong Dai; Bin Shen; Lizhen Qu; Zenglin Xu; Xiaozhu Ju

arXiv · 2605.17070

EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models

Haozhe Shan·Xiancong Ren·Han Dong·Haoyuan Shi·Yingji Zhang·Jiayu Hu·Yi Zhang·Yong Dai·Bin Shen·Lizhen Qu·Zenglin Xu·Xiaozhu Ju

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Abstract

While large vision-language models (VLMs) are increasingly adopted as the perceptual backbone for embodied agents, existing benchmarks often rely on question-answering or multiple-choice formats. These protocols allow models to exploit linguistic priors rather than demonstrating genuine visual grounding. To address this, we present EPIC-Bench, Embodied PerceptIon BenChmark, a fine-grained grounding benchmark designed to systematically evaluate the visual perceptual capabilities of VLMs in real-world embodied environments. Comprising 6.6k meticulously annotated tuples (Image, Text, Mask), EPIC-Bench spans 23 fine-grained tasks across three core stages of the embodied interaction pipeline: Target Localization, Navigation, and Manipulation. Extensive evaluations of over 89 leading VLMs reveal that while advanced reasoning models show promise, current VLMs universally struggle with complex visual-text alignment for physical interactions. Specifically, models exhibit critical bottlenecks in multi-target counting, part-whole relationship understanding, and affordance region detection. EPIC-Bench provides a robust foundation and actionable insights for advancing the next generation of vision-driven embodied models.

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BibTeX RIS

Haozhe Shan, Xiancong Ren, Han Dong, Haoyuan Shi, Yingji Zhang, Jiayu Hu, Yi Zhang, Yong Dai, Bin Shen, Lizhen Qu, Zenglin Xu, Xiaozhu Ju. 2026-05-16. EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models. https://arxiv.org/abs/2605.17070

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