Show-Harness: Just a VLM Agent Can Play Robots

2026-09-09

AI Summary

本文通过Show-Harness系统，利用基础视觉-语言模型(VLMs)解决机器人控制难题，采用紧凑的语义接口连接意图与行动，实现跨任务、实体和环境的鲁棒性能。

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.

Problem

• vision-language models
• robot control
• embodiment

Innovation

• Show-Harness
• Vision-Language Models (VLMs)
• Embodied Control
• Semantic Interface
• GUI Manipulation Interface (GUMI)