Autonomous robotic systems are widely deployed in smart factories and operate in dynamic, uncertain, and human-involved environments that require low-latency and robust fault detection and recovery (FDR). However, existing FDR frameworks exhibit various limitations, such as significant delays in communication and computation, and unreliability in robot motion/trajectory generation, mainly because the communication-computation-control (3C) loop is designed without considering the downstream FDR goal. To address this, we propose a novel Goal-oriented Communication (GoC) framework that jointly designs the 3C loop tailored for fast and robust robotic FDR, with the goal of minimising the FDR time while maximising the robotic task (e.g., workpiece sorting) success rate. For fault detection, our GoC framework innovatively defines and extracts the 3D scene graph (3D-SG) as the semantic representation via our designed representation extractor, and detects faults by monitoring spatial relationship changes in the 3D-SG. For fault recovery, we fine-tune a small language model (SLM) via Low-Rank Adaptation (LoRA) and enhance its reasoning and generalization capabilities via knowledge distillation to generate recovery motions for robots. We also design a lightweight goal-oriented digital twin reconstruction module to refine the recovery motions generated by the SLM when fine-grained robotic control is required, using only task-relevant object contours for digital twin reconstruction. Extensive simulations demonstrate that our GoC framework reduces the FDR time by up to 82.6% and improves the task success rate by up to 76%, compared to the state-of-the-art frameworks that rely on vision language models for fault detection and large language models for fault recovery.

翻译：自主机器人系统广泛部署于智慧工厂，并运行在动态、不确定且包含人类参与的复杂环境中，此类环境对低延迟的鲁棒故障检测与恢复（FDR）提出了要求。然而，现有FDR框架存在通信与计算延迟显著、机器人运动/轨迹生成不可靠等局限性，这主要归因于通信-计算-控制（3C）闭环的设计未考虑下游FDR目标。为此，我们提出了一种新颖的目标导向通信（GoC）框架，该框架针对快速且鲁棒的机器人FDR需求，协同设计3C闭环，旨在最小化FDR时间的同时最大化机器人任务（例如工件分拣）成功率。在故障检测方面，所提出的GoC框架创新性地通过设计的表示提取器，定义并提取3D场景图（3D-SG）作为语义表示，并通过监测3D-SG中空间关系的变化来检测故障。在故障恢复方面，我们利用低秩适配（LoRA）技术微调一个小型语言模型（SLM），并通过知识蒸馏增强其推理与泛化能力，以生成机器人的恢复运动。同时，我们设计了一个轻量级的目标导向数字孪生重建模块，当需要精细的机器人控制时，该模块仅利用与任务相关的物体轮廓进行数字孪生重建，从而优化SLM生成的恢复运动。大量仿真结果表明，与依赖视觉语言模型进行故障检测和大型语言模型进行故障恢复的现有最优框架相比，所提出的GoC框架将FDR时间最高降低了82.6%，并将任务成功率最高提升了76%。