让大模型“玩”机器人，不写一行控制代码
机器人控制通常需要专门训练、专用硬件，但这篇论文把门槛拆掉了：他们给大模型一个“语义动作”面板——比如“向前”“抓取”“放下”——模型只需要在这些离散动作里做选择，具体怎么执行由底层解释器负责。结果，闭源前沿模型开箱就能零样本控制机器人，小规模开源模型也只需几小时微调就能部署。更妙的是，同一套动作面板还能用在 GUI 上，让人和 AI 用鼠标“玩”机器人，不需要昂贵的遥操作设备。它不是你明天就能用上的东西，但它指向一个方向：大模型对世界的理解，可能只差一个合适的接口就能落地成物理行动。
📄 原文摘要(英文)
Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to "play" robots through a compact semantic interface linking intent to action. Show-Harness exposes discrete semantic action units that VLMs can naturally reason over, while embodiment-specific interpreters deterministically ground them into local robot actions, keeping the VLM directly responsible for fine-grained physical decisions. Through the same interface, Show-Harness demonstrates the feasibility of (1) directly unlocking closed-source frontier VLMs for zero-shot robot control, and (2) adapting small-scale open-source VLMs for low-cost deployment with just a few GPU-hours of fine-tuning. We further develop GUMI (GUI Manipulation Interface), which extends the same semantic action space to GUI-based demonstration collection, allowing humans and agents to "play" robots across embodiments without specialized teleoperation hardware. Extensive experiments show that Show-Harness-equipped VLM agents generalize robustly across tasks, embodiments, and environments, outperforming representative agentic and VLA paradigms. These results suggest that the right interface can unlock substantial embodied capability from foundation VLMs, without requiring additional model capacity or costly embodiment-specific pretraining.