Abstract:The convergence of robotics and next-generation communication is a critical driver of technological advancement. As the world transitions from 5G to 6G, the foundational capabilities of wireless networks are evolving to support increasingly complex and autonomous robotic systems. This paper examines the transformative impact of 6G on enhancing key robotics functionalities. It provides a systematic mapping of IMT-2030 key performance indicators to robotic functional blocks including sensing, perception, cognition, actuation and self-learning. Building upon this mapping, we propose a high-level architectural framework integrating robotic, intelligent, and network service planes, underscoring the need for a holistic approach. As an example use case, we present a real-time, dynamic safety framework enabled by IMT-2030 capabilities for safe and efficient human-robot collaboration in shared spaces.



Abstract:6G wireless networks are expected to provide seamless and data-based connections that cover space-air-ground and underwater networks. As a core partition of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) have been envisioned to provide countless real-time intelligent applications. To realize this, promoting AI techniques into SAGIN is an inevitable trend. Due to the distributed and heterogeneous architecture of SAGIN, federated learning (FL) and then quantum FL are emerging AI model training techniques for enabling future privacy-enhanced and computation-efficient SAGINs. In this work, we explore the vision of using FL/QFL in SAGINs. We present a few representative applications enabled by the integration of FL and QFL in SAGINs. A case study of QFL over UAV networks is also given, showing the merit of quantum-enabled training approach over the conventional FL benchmark. Research challenges along with standardization for QFL adoption in future SAGINs are also highlighted.