  In existing wireless networks, the control programs have been designed
manually and for certain predefined scenarios. This process is complicated and
error-prone, and the resulting control programs are not resilient to disruptive
changes. Data-driven control based on Artificial Intelligence and Machine
Learning (AI/ML) has been envisioned as a key technique to automate the
modeling, optimization and control of complex wireless systems. However,
existing AI/ML techniques rely on sufficient well-labeled data and may suffer
from slow convergence and poor generalizability. In this article, focusing on
digital twin-assisted wireless unmanned aerial vehicle (UAV) systems, we
provide a survey of emerging techniques that can enable fast-converging
data-driven control of wireless systems with enhanced generalization capability
to new environments. These include SLAM-based sensing and network
softwarization for digital twin construction, robust reinforcement learning and
system identification for domain adaptation, and testing facility sharing and
federation. The corresponding research opportunities are also discussed.
