As one of the most promising technologies in industry, the Digital Twin (DT) facilitates real-time monitoring and predictive analysis for real-world systems by precisely reconstructing virtual replicas of physical entities. However, this reconstruction faces unprecedented challenges due to the everincreasing communication overhead, especially for digital robot arm reconstruction. To this end, we propose a novel goal-oriented semantic communication (GSC) framework to extract the GSC information for the robot arm reconstruction task in the DT, with the aim of minimising the communication load under the strict and relaxed reconstruction error constraints. Unlike the traditional reconstruction framework that periodically transmits a reconstruction message for real-time DT reconstruction, our framework implements a feature selection (FS) algorithm to extract the semantic information from the reconstruction message, and a deep reinforcement learning-based temporal selection algorithm to selectively transmit the semantic information over time. We validate our proposed GSC framework through both Pybullet simulations and lab experiments based on the Franka Research 3 robot arm. For a range of distinct robotic tasks, simulation results show that our framework can reduce the communication load by at least 59.5% under strict reconstruction error constraints and 80% under relaxed reconstruction error constraints, compared with traditional communication framework. Also, experimental results confirm the effectiveness of our framework, where the communication load is reduced by 53% in strict constraint case and 74% in relaxed constraint case. The demo is available at: https://youtu.be/2OdeHKxcgnk.

翻译：作为工业领域最具前景的技术之一，数字孪生（DT）通过精确重构物理实体的虚拟副本，为现实世界系统提供实时监控与预测分析。然而，由于通信开销的持续增长，这种重构面临着前所未有的挑战，特别是在数字机械臂重构场景中。为此，我们提出了一种新颖的目标导向语义通信（GSC）框架，旨在为数字孪生中的机械臂重构任务提取GSC信息，以在严格与宽松的重构误差约束下最小化通信负载。不同于传统重构框架需周期性传输重构消息以实现实时DT重构，本框架实现了特征选择（FS）算法以从重构消息中提取语义信息，并采用基于深度强化学习的时间选择算法在时间维度上选择性传输语义信息。我们通过基于Franka Research 3机械臂的Pybullet仿真与实验室实验验证了所提出的GSC框架。针对一系列不同的机器人任务，仿真结果表明：与传统通信框架相比，本框架可在严格重构误差约束下降低至少59.5%的通信负载，在宽松重构误差约束下降低80%的通信负载。实验结果同样证实了框架的有效性，在严格约束情况下通信负载降低53%，在宽松约束情况下降低74%。演示视频详见：https://youtu.be/2OdeHKxcgnk。