Empowering Embodied AI in 6G Networks: Architecture, Enablers, and Open Challenges
Authors: Junaid Sajid, Sheikh Salman Hassan, Wenshuai Liu, Yan Kyaw Tun, Yaru Fu, Nguyen H. Tran, Zhu Han, Cedomir Stefanovic, +2 more
Organizations: Thomas Johann Seebeck Department of Electronics, Tallinn University of Technology (TalTech), 19086 Tallinn, Estonia · Institute for Imaging, Data and Communication (IDCoM), The University of Edinburgh, Edinburgh EH9 3BF, United Kingdom · School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, 214122, China · School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, 999077, China · Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark · School of Computer Science, The University of Sydney, Australia · Department of Electrical and Computer Engineering at the University of Houston, Houston, TX 77004 USA · Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
Embodied artificial intelligence (AI) is emerging as a key driver of the sixth-generation (6G) wireless networks by enabling agents that continuously perceive, communicate, and act in dynamic physical environments. Unlike conventional AI systems that process disembodied data, embodied agents such as robots, autonomous vehicles, and extended reality (XR) devices operate through closed-loop perception-communication-action (PCA) interactions, where communication performance directly affects physical behavior, control stability, and task success. However, existing AI-native wireless architectures remain largely connectivity-centric and are not designed to support task-driven embodied intelligence at large scale. Therefore, we present a holistic framework for embodied AI-native 6G systems, in which communication, sensing, computation, and control are jointly designed as a unified closed-loop infrastructure. We introduce a system-level PCA architecture, discuss key enabling technologies and representative applications, and highlight major open challenges in multimodal intelligence, edge-aware deployment, evaluation, trustworthiness, and practical implementation. Our central argument is that future 6G systems must evolve from intelligent communication platforms into active enablers of embodied physical intelligence.