用静态场景统一评估具身智能，降低门槛提升效率
Static and Plugged: Make Embodied Evaluation Simple
- 用静态场景替代交互环境，实现简单插拔式评估
- 覆盖42个场景、8个维度，评估19个VLM和11个VLA模型
- 提供首个统一静态基准榜单，适合研究者快速对比模型
具身智能快速发展，亟需高效评估方法。现有基准多依赖交互式仿真环境或真实世界部署，成本高、碎片化且难扩展。为此，我们提出StaticEmbodiedBench，一个即插即用的基准，通过静态场景表示实现统一评估。涵盖42种多样化场景和8个核心维度，支持通过简易接口进行可扩展、全面的评估。我们评估了19个视觉-语言模型（VLMs）和11个视觉-语言-动作模型（VLAs），建立了首个具身智能的静态统一排行榜。此外，我们公开了200个样本子集，以加速具身智能发展。
原文摘要 · Abstract (English)
Embodied intelligence is advancing rapidly, driving the need for efficient evaluation. Current benchmarks typically rely on interactive simulated environments or real-world setups, which are costly, fragmented, and hard to scale. To address this, we introduce StaticEmbodiedBench, a plug-and-play benchmark that enables unified evaluation using static scene representations. Covering 42 diverse scenarios and 8 core dimensions, it supports scalable and comprehensive assessment through a simple interface. Furthermore, we evaluate 19 Vision-Language Models (VLMs) and 11 Vision-Language-Action models (VLAs), establishing the first unified static leaderboard for Embodied intelligence. Moreover, we release a subset of 200 samples from our benchmark to accelerate the development of embodied intelligence.
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