The ultra-reliable and low-latency communication (URLLC) service of the fifth-generation (5G) mobile communication network struggles to support safe robot operation. Nowadays, the sixth-generation (6G) mobile communication network is proposed to provide hyper-reliable and low-latency communication to enable safer control for robots. However, current 5G/ 6G research mainly focused on improving communication performance, while the robotics community mostly assumed communication to be ideal. To jointly consider communication and robotic control with a focus on the specific robotic task, we propose goal-oriented semantic communication in robotic control (GSRC) to exploit the context of data and its importance in achieving the task at both transmitter and receiver. At the transmitter, we propose a deep reinforcement learning algorithm to generate optimal control and command (C&C) data and a proactive repetition scheme (DeepPro) to increase the successful transmission probability. At the receiver, we design the value of information (VoI) and age of information (AoI) based queue ordering mechanism (VA-QOM) to rank the queue based on the semantic information extracted from AoI and VoI. The simulation results validate that our proposed GSRC framework achieves a 91.5% improvement in the mean square error compared to the traditional unmanned aerial vehicle control framework.

翻译：第五代（5G）移动通信网络的超可靠低时延通信（URLLC）服务难以保障机器人安全运行。当前提出的第六代（6G）移动通信网络旨在提供超可靠低时延通信，以实现对机器人更安全的控制。然而，现有5G/6G研究主要聚焦于提升通信性能，而机器人学界通常假设通信为理想状态。为在特定机器人任务框架下联合考虑通信与机器人控制，本文提出面向机器人控制的目标导向语义通信（GSRC），以在收发两端共同利用数据的上下文及其对实现任务的重要性。在发送端，我们提出一种深度强化学习算法以生成最优控制与指令数据，并设计一种主动重传方案（DeepPro）以提升传输成功概率。在接收端，我们设计了基于信息价值（VoI）与信息时效（AoI）的队列排序机制（VA-QOM），通过从AoI和VoI中提取语义信息对队列进行优先级排序。仿真结果表明，与传统无人机控制框架相比，我们提出的GSRC框架在均方误差指标上实现了91.5%的性能提升。