Christopher M. Trombley

University of Louisville, Robotics Research (United States)

Papers

Total Citations

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About

Christopher M. Trombley is a robotics researcher whose work bridges perception, manipulation, and human-robot interaction. His primary research areas include visual servoing for precise robotic tool changing, adaptive control for nursing assistance, and deep learning for dynamic environment mapping. Trombley’s major contributions include developing a visual servoing strategy using RGB-D cameras that enables precise and effective robotic tool changes—a critical capability for flexible industrial manufacturing. His work on the Adaptive Robotic Nursing Assistant (ARNA) introduced a neural, model-free human intent estimator that enhances safety and predictability during human-robot collaboration. Additionally, Trombley proposed Dynamic-GAN, an attention-based deep learning framework that removes dynamic objects from camera frames to improve SLAM performance in feature-dense environments. With over 11 citations across his most-cited papers, his research has been recognized for addressing real-world challenges in both industrial and assistive robotics. His innovative approaches to tool change automation, intent estimation, and dynamic object removal demonstrate a commitment to making robots more adaptable, safe, and effective in complex, changing environments.