Atip Juwardi

Sunan Gunung Djati State Islamic University Bandung

Papers

Total Citations

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About

Atip Juwardi is a robotics researcher whose work sits at the intersection of computer vision, machine learning, and human-robot interaction. His key contributions center on developing socially aware robotic systems capable of perceiving and responding to human cues. Juwardi is best known for his foundational work on the social robot SyPEHUL (System of Physics, Electronics, Humanoid Robot and Machine Learning), a platform designed to bridge the gap between mechanical actuation and intelligent perception. His most cited paper (2017, 26 citations) established a robust framework for real-time face recognition and tracking using Cascade Classification and Local Binary Pattern Histogram (LBPH) algorithms, enabling SyPEHUL to identify and follow human faces during interaction. Building on this, his 2018 study (7 citations) expanded the robot’s capabilities to include facial expression recognition, integrating a 12 Degree-of-Freedom (DoF) robotic head controlled by an Arduino microcontroller. These contributions are significant for advancing intuitive human-robot communication, particularly in assistive and social robotics contexts. Juwardi’s work demonstrates a practical, systems-level approach to embedding perceptual intelligence in physical robots, making him a notable figure in the development of socially interactive machines.