Edisson Arias
Papers
2
Total Citations
13
H-Index
2
About
Edisson Arias is a researcher at the forefront of human-robot interaction, specializing in the application of convolutional neural networks (CNNs) to enhance social robotics. His work focuses on developing intuitive, non-verbal communication channels between humans and robots, particularly through gesticulation control. Arias’s most cited contributions—including his 2019 paper "A ConvNet-Based Approach Applied to the Gesticulation Control of a Social Robot" (7 citations) and its companion study on humanoid interactive robots (6 citations)—demonstrate how deep learning can enable robots to interpret and replicate human-like gestures, making them more accessible and engaging in social settings. By integrating computer vision with robotics, his research bridges the gap between technical control systems and natural human interaction, paving the way for more empathetic and responsive machines. Though his citation counts are modest, the foundational nature of his work in gesture-driven control systems positions him as a key contributor to the growing field of socially aware robotics. Arias’s efforts are particularly valuable for students and researchers exploring how AI can make robots not just functional, but truly interactive companions.
Research Focus
Key Achievements
Top Papers
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