Rodrigo Melo

Universidade Federal de Pernambuco, Universidade Estadual do Ceará

Papers

Total Citations

H-Index

About

Rodrigo Melo’s research sits at the vibrant intersection of social robotics, human-robot interaction, and music education, with a strong technical foundation in computer vision and deep learning. His most cited work, "Comparing Social Robot Embodiment for Child Musical Education" (13 citations), explores how different robot forms—from humanoid to non-humanoid—affect children’s engagement and learning outcomes in music, a pioneering study that has shaped the design of educational robots. In "Computer Vision System with Deep Learning for Robotic Arm Control" (5 citations), Melo developed a three-stage pattern recognition pipeline—combining feature matching, edge detection, and deep learning—to enable robotic arms to interpret hand gestures, offering a practical, low-cost solution for intuitive human-robot collaboration. His work "Guitar Tuner and Song Performance Evaluation Using a NAO robot" (4 citations) further demonstrates his commitment to accessible music technology, using the NAO humanoid robot to both tune guitars and evaluate song performances, thereby opening new avenues for music therapy and self-directed learning. Melo’s contributions are notable for their interdisciplinary reach, bridging engineering, education, and the arts, and for their potential to make music education more engaging and inclusive through socially embodied robots.