Mahmoud Hamandi

Cleveland State University, Université Fédérale de Toulouse Midi-Pyrénées

Papers

Total Citations

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About

Mahmoud Hamandi’s research sits at the intersection of robotics, human-robot interaction, and motion planning, with a particular focus on enabling robots to operate safely and intuitively alongside people. His work addresses fundamental challenges in robot perception and navigation, from the low-level tracking infrastructure to high-level social awareness. One of his key contributions is in motion capture systems, where he developed methods for automatically generating distinctive marker configurations, simplifying the setup for precise robot localization—a critical enabler for algorithm testing and data collection. In human-robot collaboration, Hamandi has advanced the field by predicting human intentions during manipulation tasks, allowing robots to anticipate a partner’s next move. Perhaps his most notable work, DeepMoTIon, introduces a learning-based approach that mimics human navigation patterns in crowded spaces; by training on pedestrian surveillance data, the model enables robots to predict human motion from LiDAR scans and navigate with a natural, socially compliant flow. With each of his most cited papers garnering 5–6 citations, Hamandi’s contributions are establishing a foundation for more seamless, human-aware robotic systems.