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About

Eyberth Rojas is a researcher specializing in robotics, human-robot interaction (HRI), and intelligent control systems. His work spans autonomous robot navigation, multimodal signal processing, and machine learning applications in robotic contexts. Among his most notable contributions is a fast path planning algorithm developed for the RoboCup Small Size League (2015, 10 citations), which addressed real-time navigation challenges in competitive robotic soccer environments. Rojas has also made significant strides in emotion recognition and multimodal communication within HRI, publishing work on identifying emotional signals and developing machine learning methodologies for interpreting multimodal instructions — research that collectively reflects his commitment to making robots more responsive to human behavior. Earlier in his career, he explored control theory through a comparative analysis of fuzzy and classical PI+D controllers applied to humanoid robot stabilization, demonstrating both theoretical rigor and practical engineering insight. His educational contributions are equally notable, having designed a RoboCup-based learning methodology that integrates classical conditioning and reinforcement principles to engage students in robotics. With a citation record spanning robotics competitions, HRI, and intelligent control, Rojas represents a versatile researcher whose work bridges foundational engineering and emerging human-centered robotics.