Dimitrios Kapsos

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About

Dimitrios Kapsos is a researcher at the forefront of integrating deep learning with robotic perception and RFID-based localization. His work primarily focuses on solving complex localization problems in 2D space by leveraging advanced neural network architectures. In his highly cited 2025 paper, "Deep Learning for Robotic RFID-Localization," Kapsos demonstrates how phase measurements collected by a single antenna mounted on a moving robot can be effectively combined with antenna-position data to accurately pinpoint RFID tags. This contribution addresses a critical challenge in logistics, warehouse automation, and smart environments, where precise, low-cost localization is essential. By bridging the gap between robotics and deep learning, Kapsos’s research offers a scalable and efficient solution that reduces hardware complexity while maintaining high accuracy. His work has already garnered attention within the robotics and RFID communities, with his key paper accumulating citations that underscore its relevance and impact. Kapsos’s innovative approach not only advances the field of robotic sensing but also opens new avenues for real-time, autonomous tracking systems, making him a notable emerging voice in applied machine learning and intelligent robotics.