# Goal-oriented Semantic Communications for Robotic Waypoint Transmission: The Value and Age of Information Approach

arXiv: https://arxiv.org/abs/2312.13182 · HTML: https://mlsanity.com/paper/2312.13182/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2023.

## Built on by (most cited first, 5 of 5)

- ["X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems](https://mlsanity.com/paper/2507.19657/) (2025)
- [Goal-Oriented Semantic Communication for Wireless Visual Question Answering](https://mlsanity.com/paper/2411.02452/) (2024)
- [Observation Compression in Rate-Limited Closed-Loop Distributed ISAC Systems: From Signal Reconstruction to Control](https://mlsanity.com/paper/2505.01780/) (2025)
- [Efficient Information Updates in Compute-First Networking via Reinforcement Learning with Joint AoI and VoI](https://mlsanity.com/paper/2505.06025/) (2025)
- [Task-Semantic Graph-Driven Distributed Agent Networking for Underwater Target Tracking](https://mlsanity.com/paper/2605.15528/) (2026)

## Abstract (arXiv)

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.