Carolina Lucas-Dophe

Instituto Tecnológico de Querétaro, Tecnológico de Monterrey

Papers

Total Citations

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About

Carolina Lucas-Dophe is at the forefront of human-robot collaboration, pioneering the integration of deep learning and collaborative robotics to transform manufacturing and assembly tasks. Her research focuses on creating intuitive, adaptable interfaces that bridge the gap between human workers and cobots, making industrial automation more accessible and efficient. Her most-cited work, "Integration of Deep Learning and Collaborative Robot for Assembly Tasks" (2024, 24 citations), demonstrates how neural networks can enable robots to understand and respond to human actions in real time, significantly improving workflow flexibility. In her 2023 study, "Flexible Human-Robot Interaction: Collaborative Robot Integrated with Hand Tracking," she proposed a novel system that uses hand-tracking technology to allow operators to guide cobots with natural gestures, eliminating the need for complex programming. With a growing citation impact, Lucas-Dophe’s contributions are shaping the next generation of smart factories, where humans and machines work side by side seamlessly. Her work is particularly notable for its practical applications in small-to-medium enterprises, where adaptable automation is critical. As a rising voice in collaborative robotics, she is redefining how we think about human-machine synergy in industrial settings.