机器人认得出危险，却不会躲
机器人能认出「这是易碎品、那是台阶」，但真正走路时，它并不会因为面前是更危险的东西而放慢速度、拉开距离。研究者造了一个基准：场景只换障碍物类型、其他完全一样，再测当前最强的视觉语言机器人——结果它们嘴上分得清危险等级，脚下却一视同仁。给它们明确的安全提示也没用。唯一有效的改动，是在「认出障碍」和「生成动作」之间硬插一步，先把安全参数算出来，再让动作照着走。这不是你明天能用的功能，但它指出了一个真问题：AI 的「知道」和「做到」之间，还隔着一条没填上的沟。
📄 原文摘要(英文)
Safe household navigation requires robots to regulate clearance and speed according to what an obstacle is and how close it is, without unnecessary conservatism. Yet existing evaluations do not test whether semantic safety distinctions are reflected in continuous motion. To that end, we introduce OSMA-Bench, an Obstacle-Conditioned Safe Motion Adaptation Benchmark built on spatially matched scenario sets that differ only in obstacle type. We also propose the Semantic Sensitivity Index (SSI), which measures whether clearance and distance-dependent speed and smoothness follow the caution ordering of obstacles. Evaluating VLM-based and vision-language-action (VLA) policies on the benchmark, we find that neither consistently adapts its motion to obstacle safety, even with explicit safety prompts. Our analysis shows that the policies can correctly distinguish obstacles by safety relevance but fail to reflect this distinction in the motion parameters or actions they produce. For VLM-based policies, which recognize the obstacle and then generate a navigation program, adding an explicit parameter-inference step between these two stages improves adaptation. These findings expose a gap in semantic-to-motion grounding, highlighting the need to translate semantic caution into appropriately safe motion.