Ehsan Latif
Papers
14
Total Citations
88
H-Index
6
About
Ehsan Latif is a robotics and artificial intelligence researcher whose work spans multi-robot systems, autonomous navigation, wireless localization, and AI-driven education technology. His research addresses some of the most pressing challenges in cooperative robotics, including how swarm systems can explore unknown environments efficiently while minimizing communication overhead — a problem he tackles through innovative distributed reinforcement learning frameworks such as coverage-biased Q-learning and graph optimization-based relative localization. With contributions like SEAL, which simultaneously advances exploration and localization accuracy without relying on global positioning, Latif has meaningfully pushed the boundaries of GPS-denied multi-robot operation. More recently, Latif has extended his expertise into the rapidly evolving intersection of large language models and robotics. His PhysicsAssistant project — already garnering 17 citations since its 2024 publication — demonstrates how LLM-powered multimodal robots can transform science education, while his probabilistic path planning framework shows how semantic knowledge embedded in LLMs can guide real-world autonomous navigation. His systematic review of intelligent tutoring systems further reflects a growing commitment to educational technology. Collectively accumulating nearly 80 citations across a diverse and technically rigorous portfolio, Latif represents an exciting voice bridging autonomous systems, AI reasoning, and human-centered applications.
Research Focus
Key Achievements
Top Papers
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- 3SEAL: Simultaneous Exploration and Localization for Multi-Robot Systems10 citations · 2023
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- 9Energy-Aware Multi-Robot Task Allocation in Persistent Tasks4 citations · 2021
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