AI有脑子没身体：最强模型任务成功率不到17%
我们总说AI越来越聪明，但让它操控一个身体在真实房间里找东西、走过去、拿起来，结果惨不忍睹。研究者造了一个测试框架HumanCLAW：让AI只负责下指令（比如“向前走”“抓取”），身体动作由系统代劳，排除平衡和电机误差的干扰。这样失败就只怪AI的决策。测试了9个最先进的视觉语言模型，在41个室内场景的1218个任务中，最好的模型成功率仅16.8%。问题不是AI认不出目标，而是它没有“身体意识”——不知道自己在哪里、有没有走到、有没有撞墙。它不是你明天能用上的，但告诉你：AI离真正自主行动还差一个“我”字。
📄 原文摘要(英文)
Evaluating whether a vision-language model (VLM) can act through a physical body is challenging. The outcome of an action couples the VLM's decision with motor control. When a task fails, it is hard to tell whether the VLM made a bad choice or the motor controller simply failed to execute it, e.g., losing balance and falling. In this work, we introduce HumanCLAW, an evaluation framework that decouples action decision-making from low-level execution. At every step, a harnessed, off-the-shelf VLM issues an atomic skill command, and the command is translated into a sub-second chunk of continuous full-body motion with real physical consequences, including gravity and collisions. The body can therefore act freely in the physical world, while execution-side disturbances, balance and motor errors, are factored out. What remains measurable is the model's action intelligence: its moment-to-moment choice of what the body should execute next. Based on this framework, we build HumanCLAW-Bench: 1,218 long-horizon, egocentric find-navigate-interact episodes across 41 indoor scenes. We test nine state-of-the-art VLMs and find that none solves the benchmark; the best model reaches only a 16.8% success rate. Recognizing the target is not the bottleneck. What current VLMs lack is embodied self-awareness: they lose track of their own body, failing to tell where it is, whether it has reached the goal, or whether it has hit an obstacle.