Current Physical AI (PAI) relies heavily on closed-loop visual-servoing pipelines, whose perception and planning stages may become computationally intensive onboard due to complex models embedded on robots. In practice, offloading the perception task to on-site edges statically is inappropriate for latency-sensitive, precise industrial settings over a standardized industrial network. This emphasizes the importance of Control-Communication-Computing (3C) co-design in industrial automation: monolithic local execution saturates AI-accelerated machine and robot hardware, while static edge offloading exposes the control loop to network jitter. Existing adaptive task placement (ATP) controllers can partially address the gap by relocating a single pipeline stage on binary threshold rules, without a multi-stage model and an explicit cost on placement switching. In this Work-in-Progress (WiP) paper, we propose a directed acyclic graph (DAG) based quality-of-service (QoS)-aware dynamic task placement (DTP) framework for sensing-perception-planning-control pipelines in networked robotics. This pipeline is formalized as a DAG with task-level and node-level attributes for compute cost, communication delay, and feasible placement sets; over a small interpretable candidate set (fully local, static offload, hybrid), a window-based cost function combines tail end-to-end latency, deadline violation rate, hardware utilization, and a Hamming-distance switching penalty, and a DTP algorithm with hysteresis and a minimum dwell-time bounds placement chatter. Our WiP paper presents the theoretical framework, a structured qualitative analysis, and a two-phase simulation plus hardware-in-the-loop validation roadmap.

翻译：当前物理人工智能（PAI）高度依赖闭环视觉伺服流水线，其感知与规划阶段因机器人搭载的复杂模型而可能成为机载计算瓶颈。在实际场景中，将感知任务静态卸载至现场边缘设备并不适用于标准化工业网络下对延迟敏感的精密工业环境。这凸显了工业自动化中控制-通信-计算（3C）协同设计的重要性：单一本地执行会饱和AI加速设备与机器人硬件，而静态边缘卸载则使控制环暴露于网络抖动中。现有自适应任务部署（ATP）控制器通过基于二元阈值规则迁移单流水线阶段可部分解决上述问题，但缺乏多阶段模型及显式的部署切换代价。本项工作进展（WiP）论文针对网络化机器人系统中的感知-规划-控制流水线，提出一种基于有向无环图（DAG）的服务质量（QoS）感知的动态任务部署（DTP）框架。该流水线以DAG形式建模，包含任务级与节点级的计算开销、通信延迟及可行部署集合属性；在小型可解释候选集（全本地、静态卸载、混合部署）上，基于时间窗口的成本函数整合了尾端到端延迟、截止时间违反率、硬件利用率及汉明距离切换惩罚，同时采用带滞回特性与最小停留时间的DTP算法约束部署抖动。本WiP论文提出了理论框架、结构化定性分析，以及两阶段仿真与硬件在环验证路线图。