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About

Yulia Hicks is a leading researcher at the intersection of robotics, computer vision, and human-robot interaction. Her work addresses two critical challenges: enabling robots to understand human intentions and equipping them with robust perception for agricultural automation. In human-robot interaction, Hicks has developed novel frameworks for human intention recognition that move beyond activity-specific models, using context relationships and environmental triggers to achieve more versatile, proactive robot responses. Her papers on this topic, including "Human intention recognition using context relationships in complex scenes" and "Context change and triggers for human intention recognition," have each garnered 9 and 6 citations respectively, establishing foundational work in the field. Simultaneously, Hicks has made significant contributions to agricultural robotics, introducing self-supervised and lightweight segmentation models for in-field fruit ripeness determination. Her 2025 papers on occluded apple ripeness and query-based fruit segmentation (7 and 6 citations) address the real-world challenge of occluded views in orchards, directly supporting the development of autonomous harvesting robots. Earlier work includes a feature extraction method for the Clock Drawing Test (9 citations) for dementia evaluation, and on-line self-supervised learning for robot visual classification. Hicks’s research is characterized by its practical orientation—bridging perception, intention, and action to create more capable, context-aware robotic systems.