让轻量级机器人在本地运行视觉语言动作模型，实现工业级自适应自动化。
ROS2SmolVLA: Enabling Small Vision-Language-Action Models for Integration into Industrial-Grade Lightweight Robots
- 将SmolVLA适配至Universal Robots轻量级机械臂，支持本地计算。
- 在UR10e上完成抓取放置任务，验证模型在真实工业场景的可行性。
- 开源ROS2接口库，便于实验室与工厂快速部署使用。
工业生产需求转向小批量、多品种，传统机器人自动化系统因静态设计难以应对动态变化。视觉-语言-动作（VLA）模型可通过感知环境状态生成机器人动作，成为解决该问题的潜力方案。然而，现有研究或依赖大型模型无法本地部署，带来合规与安全风险；或仅在实验级硬件上验证，难以映射到真实工业场景。本文将Hugging Face的SmolVLA模型适配至Universal Robots的轻量级机械臂，并发布开源项目ROS2SmolVLA，提供ROS 2与SmolVLA间的接口，使模型可在工业级硬件上运行。通过在UR10e机械臂上完成抓取放置任务，验证了其功能性。结果表明，SmolVLA适用于需本地计算的小型任务，为轻量化智能机器人部署提供了可行路径。
原文摘要 · Abstract (English)
Industrial demand changes the paradigms of production. Due to smaller batch sizes and more variations in products, companies face a growing challenge to adopt more adaptive production systems. In particular, robot-based automation is usually static and fails to respond to constantly changing processes. Vision-Language-Action (VLA) Models are a promising opportunity to mitigate this challenge by generating robot actions based on the observed system state. However, current research either focuses on large models that cannot be computed on premise, creating compliance and security challenges, or use lab-grade robot hardware that obscures exploitation in real industrial settings. In this work, we adapt Hugging Face's SmolVLA for Universal Robots lightweight robots. Further, we release the open-source repository ROS2SmolVLA that implements an interface for ROS 2 to SmolVLA, and makes it applicable for industrial-grade hardware. By this, we allow a lenient adoption into lab and industrial environments. We validate the functionality of SmolVLA for a Universal Robots UR10e using a pick-and-place task and give implementation guidelines. Our findings support that SmolVLA is a well-suited option for small-sized tasks that need to be computed on premise.
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