Wireless localization determines the position of devices by analyzing characteristics of radio signals such as time of arrival, signal strength, and angle of arrival. It is technically important because accurate location information is essential for high-precision positioning in 6G wireless communication systems. Our lab has explored a wide range of localization methods, including model-based and AI-driven approaches, e.g. cooperative localization and fingerprinting [1, 2]. Recently, we have focused on graph neural networks (GNNs), which provide a powerful way to model the relationships between base stations and user devices using a graph structure [3]. This helps the model better understand the wireless network and improve localization accuracy, especially in complex environments. To validate our theories in practice, we perform real-world prototyping using Ultra-Wideband (UWB) hardware, including demonstrations with small autonomous vehicles for cooperative localization and multi-agent scenarios involving drone swarms and ground rovers.