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应用于智能工厂，并在动态、不确定且有人参与的复杂环境中运行，这要求系统具备低延迟且鲁棒的故障检测与恢复能力。然而，现有故障检测与恢复框架存在诸多局限，例如通信与计算存在显著延迟，以及机器人运动/轨迹生成不可靠，这主要因为通信-计算-控制闭环的设计未考虑下游故障检测与恢复的具体目标。为解决此问题，我们提出了一种新颖的目标驱动通信框架，该框架联合设计面向快速鲁棒机器人故障检测与恢复的通信-计算-控制闭环，其目标是在最大化机器人任务（如工件分拣）成功率的同时，最小化故障检测与恢复时间。在故障检测方面，我们的目标驱动通信框架创新性地定义并提取三维场景图作为语义表示，通过我们设计的表示提取器，通过监测三维场景图中空间关系的变化来检测故障。在故障恢复方面，我们通过低秩自适应微调一个小型语言模型，并借助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。我们还设计了一个轻量级的目标驱动数字孪生重建模块，在需要精细机器人控制时，仅利用任务相关物体的轮廓进行数字孪生重建，以优化由小型语言模型生成的恢复运动。大量仿真实验表明，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先进框架相比，我们的目标驱动通信框架将故障检测与恢复时间降低了高达82.6%，并将任务成功率提升了高达76%。