Sony Sumaryo

Papers

Total Citations

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About

Sony Sumaryo is a researcher advancing the fields of robotics and human-robot interaction, with a focus on intelligent control systems and computer vision. His work bridges gesture-based control and neural network-driven automation, contributing to more intuitive and adaptive robotic systems. In his 2020 study on wheeled robot control, Sumaryo explored hand gesture recognition using image processing and shape detection, demonstrating how sign language gestures can serve as natural commands for remote robot operation—a contribution with growing relevance in assistive and interactive robotics. More recently, his 2024 paper on neural network control for a 3DOF robot arm proposed a multilayer perceptron model as a replacement for traditional PID and computed torque control. By employing 30 hidden layers and scaled gradient descent, his approach offers a more flexible, data-driven solution for dynamic robotic manipulation. Though his citation counts are currently modest (3 and 2 citations respectively), Sumaryo’s work represents foundational steps toward integrating neural control with vision-based human-robot interfaces. His research is particularly valuable for students and engineers interested in low-cost, accessible robotic systems and the practical application of deep learning in real-time control environments.