  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
task-oriented and semantics-aware communication in robotic control (TSRC) 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 reorganize the
queue based on the semantic information extracted from the AoI and the VoI. The
simulation results validate that our proposed TSRC framework achieves a 91.5%
improvement in the mean square error compared to the traditional unmanned
aerial vehicle control framework.
