Robot middleware faces a new role in the era of Physical AI. Learned policies, planners, and vision-language-action (VLA) models now enter deployed robots as causal participants on the control path, but the layer that integrates them with timing, scheduling, and network has not been named. Recent language-agent work names this layer the harness, the external system that mediates tools, manages state, bounds resources, and records execution. The robotics community has not yet adopted this framing, and we propose that robot middleware is that harness. A Physical AI harness differs from a software harness in where it intervenes. A software harness mediates at tool-call boundaries. A Physical AI harness must mediate at control, computing, and communication simultaneously, because a learned policy's output crosses all three: its commands shift the trajectory, its inference time shifts the schedule, and its payload shifts the bandwidth. Robot middleware is the lowest robot-stack layer with mediating abstractions over all three, so it is best positioned to compose their enforcement. It already provides most of what a harness needs but lacks the enforcement for an AI model. We name this missing enforcement as three functions: Projection gates each output at emission, Isolation bounds the model's execution and transmission slot, and Transfer falls back to a verified baseline when checks fail. Each appears today as hand-built application code in deployed robot systems, built on surfaces robot middleware already provides. Robot middleware should host them not as the best single-axis enforcer but as the layer that composes all three. We sketch this as a ROS 2 Harness Profile, a deployment artifact that carries an AI model's declared output region, inference budget, and operating regime while the middleware enforces them across ROS 2, DDS, and Zenoh.

翻译：机器人中间件在物理人工智能时代面临新角色。学习策略、规划器及视觉-语言-动作模型如今作为控制路径上的因果参与者进入部署机器人，但将其与时序、调度和网络集成起来的层次尚未被命名。近期语言代理相关研究将这一层次称为"编排层"——即中介工具、管理状态、约束资源并记录执行过程的外部系统。机器人领域尚未采纳这一框架，我们提出机器人中间件正是该编排层。物理人工智能编排层与软件编排层的差异在于干预节点：软件编排层在工具调用边界进行中介，而物理人工智能编排层需同时在控制、计算和通信三个层面实施中介——因为学习策略的输出横跨三者：其指令改变轨迹，推理时间改变调度，数据载荷改变带宽。作为机器人堆栈中具备抽象中介三类要素的最低层次，机器人中间件最适合整合执行机制。该层次已具备编排所需的大部分功能，但缺乏针对人工智能模型的强制管控。我们将此缺失功能定义为三项：投射门控在输出时校验每个结果，隔离机制约束模型执行与传输时隙，转移机制在检查失败时回退至已验证基线。这些功能当前以手工构建的应用代码形式存在于部署机器人系统中，依托机器人中间件已提供的表面接口。机器人中间件不应仅作为单一维度的执行器，而应成为整合这三项功能的层次。我们将其设计为ROS 2编排配置文件，该部署制品携带AI模型声明的输出域、推理预算及运行范式，由中间件跨ROS 2、DDS和Zenoh协议执行管控。