Autonomous systems are rapidly moving from lab prototypes to real deployments across farms, cities, and community spaces. This talk surveys practical AI-robotics pipelines—perception, planning, control, and human-in-the-loop design—that my group has translated into working field systems. In AgTech, we’ll discuss modular platforms for fruit harvesting, precision spraying, and pollination, combining edge vision (lightweight detectors on Jetson), robust actuation (CoreXY mechanisms, 6-DoF arms), and data-driven operations. In civil infrastructure and transportation, we will focus on AI-powered bridge and roadway inspection—vision/LiDAR-based defect detection, surface-distress mapping, and structural-condition scoring—as well as road-user safety through vehicle, cyclist, and pedestrian detection with near-miss analytics for proactive hazard mitigation. For assistive technologies, we’ll cover CrossBot, a smart crossing-assist robot, and ongoing work integrating low-visibility sensing (IR/radar/audio fusion) and brain–computer interface (BCI) concepts to expand accessibility. This includes results stemming from my U.S. patent on low-visibility human/animal detection using multimodal sensing, and lessons learned when hardening research prototypes for real environments. Across these domains, I’ll share insights on system architecture, model selection (from classical computer vision to compact deep nets), domain shift and on-device adaptation, and workforce development through student-centered field deployments—closing with an outlook on scalable, standards-aligned deployments and academia–industry–community collaboration.