Faezeh Tavakoli

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About

Faezeh Tavakoli is a researcher in robotics and intelligent control systems, with a primary focus on humanoid robot locomotion and reinforcement learning. Her most-cited work, “Control of humanoid robot walking by Fuzzy Sarsa Learning” (2015), introduces a novel application of the Fuzzy Sarsa Learning (FSL) algorithm to stabilize bipedal walking. By integrating fuzzy logic with online learning, Tavakoli’s approach enables a humanoid robot to adapt its gait in real time, using the Zero Moment Point (ZMP) criterion based on the inverted pendulum model to ensure dynamic stability. This contribution addresses a core challenge in humanoid robotics—achieving robust, energy-efficient walking under varying conditions—and has garnered 6 citations, reflecting its relevance in the field. Tavakoli’s work bridges reinforcement learning and classical control theory, offering a scalable framework for autonomous locomotion. Her research is particularly valuable for students and engineers exploring adaptive control in legged robots, as it demonstrates how machine learning can enhance traditional stability metrics. Through this paper, Tavakoli has established herself as a contributor to the growing intersection of fuzzy systems and robotic control, paving the way for more intelligent, self-correcting humanoid platforms.