面向物理AI的驾驭工程：机器人中间件即驾驭层
Harness Engineering for Physical AI: Robot Middleware Is the Harness Layer
AI总结：
本文提出机器人中间件作为物理AI的驾驭层，需同时干预控制、计算和通信，并补充投影、隔离和转移三种缺失的强制功能，以ROS 2驾驭配置文件为例。
摘要：
在物理AI时代，机器人中间件面临新的角色。学习策略、规划器和视觉-语言-动作（VLA）模型现在作为控制路径上的因果参与者进入已部署的机器人，但将它们与定时、调度和网络集成的层尚未被命名。最近的语言智能体工作将此层命名为驾驭层，即中介工具、管理状态、约束资源和记录执行的外部系统。机器人社区尚未采用这一框架，我们提出机器人中间件就是那个驾驭层。物理AI驾驭层与软件驾驭层的区别在于其干预位置。软件驾驭层在工具调用边界进行中介。物理AI驾驭层必须同时干预控制、计算和通信，因为学习策略的输出跨越所有三者：其命令改变轨迹，其推理时间改变调度，其有效载荷改变带宽。机器人中间件是机器人栈中最低的层，具有对所有三者的中介抽象，因此最适合组合它们的强制实施。它已经提供了驾驭层所需的大部分功能，但缺乏针对AI模型的强制实施。我们将这种缺失的强制实施命名为三个功能：投影在输出时门控每个输出，隔离约束模型的执行和传输时隙，转移在检查失败时回退到经过验证的基线。每个功能目前以手工构建的应用程序代码形式出现在已部署的机器人系统中，构建在机器人中间件已提供的表面上。机器人中间件应该将它们作为组合所有三者的层，而不是作为最佳的单轴强制器。我们将其勾勒为ROS 2驾驭配置文件，这是一个部署工件，携带AI模型声明的输出区域、推理预算和运行机制，而中间件在ROS 2、DDS和Zenoh上强制实施它们。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
