Semantic-based Internet of Embodied Intelligence: Visions and Frontiers

Yaheng Wang, Rui Meng, Xiaodong Xu, Yiming Liu, Feiliang Song, Linyuan Hu, Huishi Song, Lexi Xu, Tony Q. S. Quek, Ping Zhang

Recent advances in generative artificial intelligence (AI) and embodied intelligence (EI) enable autonomous agents to interact with the physical world. However, scaling these systems into networks of multiple agents, namely the Internet of EI (IoEI), faces critical bottlenecks. These include the overhead of massive multimodal data transmission and the decoupling of logical reasoning from physical constraints. To address these challenges, we envision the Semantic-based IoEI (SIoEI), which leverages semantic information as a unified metric throughout the agent lifecycle. We systematically define four key dimensions of EI: perception, intelligence, control, and communication. We further elaborate how semantic empowerment revolutionizes environmental perception, cognition and task planning, action generation and robust control, and communication and networking. We also present a case study to verify that, the semantic-empowered end-to-end process significantly improves channel robustness and reduces end-to-end latency for EI. Finally, we outline critical open research directions for the SIoEI paradigm.