外观
论文arXiv 2609.105221 个月前
Show-Harness：仅用一个 VLM Agent 就能玩转机器人
原文链接：Show-Harness: Just a VLM Agent Can Play Robots（arXiv 2609.10522 · 发布日期：2026-09-09）
Show-Harness: Just a VLM Agent Can Play Robots
Show-Harness用紧凑语义接口连接意图与动作，并在跨任务、具身和环境实验中验证VLM机器人控制。
核心要点
- 动机是基础VLM具备广泛世界智能，但将其转化为机器人控制仍具挑战。
- 方法暴露离散语义动作单元，并由具身特定解释器确定性落地为局部动作。
- 同一接口可零样本调用闭源前沿VLM，也可用少量GPU小时微调小型开源VLM。
- 实验显示装备Show-Harness的VLM智能体跨任务、具身和环境鲁棒泛化，并优于代表性范式。
验证范围与局限：验证范围为摘要所述的多任务、多具身和多环境实验，未说明具体机器人平台或任务。
英文摘要 (Abstract)
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.
作者：Yanzhe Chen, Zechen Bai, Cao, Zhijun, Wenzheng Zeng, Kevin Qinghong Lin, Yiqi Lin, Guoqiang Liang, Kevin Ma, Qiming Huang, Mike Zheng Shou
来源
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