发表机构
Berkeley Education Alliance for Research in Singapore (BEARS); School of Materials Science and Engineering, Nanyang Technological University (NTU)（新加坡伯克利教育研究联盟（BEARS）; 南洋理工大学材料科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出面向自动驾驶实验室的AI原生硬件控制框架PUDA，通过命令行环境实现智能体与硬件的确定性交互，分离科学编排与物理操作，为智能体自动驾驶实验室提供实用执行与数据环境。
AI 中文摘要
物理统一设备架构（PUDA）是一种面向自动驾驶实验室（SDLs）的AI原生硬件控制框架。与构建以人类为中心的图形用户界面（GUI）编排层不同，PUDA创建了一个命令行运行时环境，使智能体能够对实验进行观察、定向、决策和行动，同时硬件执行保持确定性、原子性和可审计性。PUDA采用无界面设计，设备通过可发现的命令行界面呈现，JSON协议通过分布式消息系统路由，命令响应、数据产品和报告以结构化记录形式保存。PUDA将协议、运行、样本、测量和命令日志组织为AI原生数据结构，通过运行标识符和时间戳关联，保留从提交的协议到硬件响应再到最终数据产品的溯源信息。PUDA将科学编排与物理操作和数据遥测分离：智能体选择实验，而PUDA执行经过验证的命令并捕获与溯源关联的状态、响应和数据。其贡献并非另一种优化器、编排器或配方语言，而是为智能体SDLs提供了实用的执行和数据环境；更广泛的物理AI意义在于，PUDA为AI系统与物理工具交互提供了AI原生硬件控制框架。
英文摘要
Physical Unified Device Architecture (PUDA) is an AI-native hardware harness for self-driving laboratories (SDLs). Rather than building a human-centered graphical user interface (GUI) orchestration layer, PUDA creates a command-line runtime environment that lets agents observe, orient, decide, and act over experiments while hardware execution remains deterministic, atomic, and auditable. Headless by design, devices appear through discoverable command-line interfaces, JSON protocols are routed through a distributed messaging system, and command responses, data products, and reports are preserved as structured records. PUDA organizes protocols, runs, samples, measurements, and command logs into an AI-native data structure linked by run identifiers and timestamps, preserving provenance from submitted protocol through hardware response to resulting data products. PUDA separates scientific orchestration from physical operation and data telemetry: agents choose experiments, while PUDA executes validated commands and captures provenance-linked state, responses, and data. The contribution is not another optimizer, orchestrator, or recipe language. It is a practical execution and data environment for agentic SDLs; the broader physical AI implication is that PUDA provides an AI-native hardware harness for AI systems to interact with physical tools.
