Mojtaba Esfandyari

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About

Mojtaba Esfandyari is a researcher in robotics and artificial intelligence, with a focus on motion planning, reinforcement learning, and autonomous systems. His major contributions lie in the development of learning-based control strategies for non-conventional robotic platforms, particularly spherical mobile robots—a class of robots known for their superior mobility, stability, and ability to operate in hazardous environments. Esfandyari’s most cited work, "XCS-based reinforcement learning algorithm for motion planning of a spherical mobile robot" (2016, 29 citations), introduces a novel approach that leverages eXtended Classifier Systems (XCS) to enable adaptive, real-time motion planning without requiring explicit environmental models. His earlier foundational paper (2013, 5 citations) established the concept of using learning agents for direct motion control, demonstrating how spherical robots can autonomously navigate complex terrains. Together, these works have influenced subsequent research in reinforcement learning for robotics, particularly in domains where traditional wheeled robots are impractical. Esfandyari’s research bridges the gap between machine learning and mechanical design, offering scalable solutions for autonomous exploration, surveillance, and disaster response. His work continues to inspire students and researchers interested in intelligent, adaptive robotic systems.