Loreto Susperregui
Papers
2
Total Citations
8
H-Index
2
About
Loreto Susperregui is a researcher whose work lies at the intersection of embedded systems, computer vision, and robotics, with a particular focus on industrial automation. Her early contributions center on the development of real-time visual servoing systems for mobile robots, designed to assist human workers in manufacturing environments. In her most-cited work, she proposed a hardware implementation of a neural-network recognition module using Field Programmable Gate Arrays (FPGAs), enabling efficient object detection directly on a mobile platform. This approach addressed the critical need for low-latency, embedded vision in dynamic industrial scenarios. Her subsequent research refined these FPGA-based algorithms, demonstrating how custom hardware can accelerate visual feedback for robotic manipulation tasks. Though her citation counts are modest—with her top paper receiving 5 citations—her work represents foundational steps in bringing neural-network-based perception to resource-constrained robotic systems. Susperregui’s contributions are particularly notable for bridging the gap between high-level machine learning and low-level hardware design, a challenge that remains central to deploying intelligent robots in real-world factories. Her research continues to inspire engineers seeking to embed adaptive vision into autonomous industrial assistants.
Research Focus
Key Achievements
Top Papers
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