最强AI当机器人手，成功率仍惨不忍睹
把最强的视觉语言模型装进机器人身体，让它自己看、自己想、自己动手——结果远没到能当通用机器人的程度。研究者造了个考场，1000个任务覆盖几何判断、空间推理、物体操作等，让10个顶尖模型裸考。闭源模型全面领先，开源模型在规划和操作上明显掉队。更扎心的是失败方式千奇百怪：有的模型几何判断对了但动作落点错，有的做到一半丢了物体，有的明明该停却不停。多算几轮也不一定救得回来——让模型自己检查代码，它能把执行修好但任务没完成；让它自我反思，反而把对的判断改错了。这不是明天能用的东西，但它划清了底线：现在的AI离“看懂了就能干活”还差得远。
📄 原文摘要(英文)
A robot generalist must interpret a scene, act on it, and satisfy every condition of a task. How far do frontier vision-language models (VLMs) meet these demands? We introduce Embodied Agent Arena, evaluating ten VLM agents across Geometry, Spatial Reasoning, Affordance, Task Planning, and Manipulation. Its 1,000 cases combine 32 established sources with GeoProbe, our 168-case benchmark for camera, motion, depth, and scale estimation. A minimal harness leaves task decisions to the model through source-native interfaces. Closed-weight models lead all five domains, whereas open-weight VLMs show useful spatial reasoning but substantially lower planning and manipulation success. Beneath these averages, similar geometric errors conceal different scale and shape distortions, and valid region estimates need not identify usable action points. During execution, agents can achieve early subgoals yet lose object control, bind an action to the wrong receiving region, or stop before the required physical state holds. Additional computation does not uniformly resolve these failures: code repair can restore execution without completing the task, while self-review can reverse correct judgments. Failure costs also differ: the strongest planners concentrate failed-token expenditure in prolonged body-control tasks, whereas other models fail broadly across procedures. Matched traces show how successful recovery uses feedback to restore a missing prerequisite and advance the remaining task.