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The ultra-reliable and low-latency communication (URLLC) service of thefifth-generation (5G) mobile communication network struggles to support saferobot operation. Nowadays, the sixth-generation (6G) mobile communicationnetwork is proposed to provide hyper-reliable and low-latency communication toenable safer control for robots. However, current 5G/ 6G research mainlyfocused on improving communication performance, while the robotics communitymostly assumed communication to be ideal. To jointly consider communication androbotic control with a focus on the specific robotic task, we proposetask-oriented and semantics-aware communication in robotic control (TSRC) toexploit the context of data and its importance in achieving the task at bothtransmitter and receiver. At the transmitter, we propose a deep reinforcementlearning algorithm to generate optimal control and command (C&C) data and aproactive repetition scheme (DeepPro) to increase the successful transmissionprobability. At the receiver, we design the value of information (VoI) and ageof information (AoI) based queue ordering mechanism (VA-QOM) to reorganize thequeue based on the semantic information extracted from the AoI and the VoI. Thesimulation results validate that our proposed TSRC framework achieves a 91.5%improvement in the mean square error compared to the traditional unmannedaerial vehicle control framework.

Task-oriented Semantics-aware Communications for Robotic Waypoint Transmission: the Value and Age of Information Approach