About

Wael Suleiman is a robotics researcher whose work spans humanoid robot motion planning, human-robot interaction, and kinematics optimization. His research has fundamentally advanced how humanoid robots replicate, plan, and execute complex movements in dynamic real-world environments. Suleiman's early and most influential contribution — "On human motion imitation by humanoid robot" (2008, 126 citations) — established a landmark optimization framework enabling humanoid robots to faithfully reproduce captured human motions, a foundational challenge in embodied robotics. This work, alongside his recursive humanoid motion optimization method leveraging Lie group theory, demonstrated sophisticated integration of dynamics and analytical gradient computation into robot control. His research progressively expanded to address practical deployment challenges, including feasible gait pattern generation, reactive leg motion with geometric constraints, and SLAM-based autonomous navigation under heavy payloads. His investigations into manipulability index optimization have significantly improved industrial robot dexterity during inverse kinematics solving. More recently, Suleiman has pioneered multimodal human-robot interaction, combining tactile and vision-based sensing to enhance collaborative robot safety — work that has rapidly accumulated 32 citations since 2023. Across his career, his publications collectively reflect a coherent vision: enabling robots to move intelligently, safely, and cooperatively alongside humans.

Research Focus

Key Achievements

14
H-Index
38
Papers
589
Total Citations
16
Avg Citations/Paper
🏆 Most Cited Paper
On human motion imitation by humanoid robot
126 citations · 2008
📈 Most Prolific Year: 2017 (5 Papers)
🤝 Key Collaborators: 24
🏛 Institutions: Laboratoire d'Analyse et d'Architecture des Systèmes, Université de Sherbrooke, National Institute of Advanced Industrial Science and Technology, Centre National de la Recherche Scientifique, Université Fédérale de Toulouse Midi-Pyrénées

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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