About

Guillaume Morel is a prominent robotics researcher whose work spans human-robot interaction, vision-based control, and medical robotics. His research has made significant contributions to how robots perceive, predict, and respond to human movement, with lasting implications for both assistive technologies and surgical robotics. Morel's most influential work, "How can human motion prediction increase transparency?" (2008, 74 citations), addressed a fundamental challenge in assistive robotics: enabling robots to follow human movements without generating resistive forces, a property known as transparency. This contribution helped shape modern thinking in collaborative human-robot manipulation. His earlier work on vision-based control, particularly his 2000 paper on incorporating 2D constraints into robot manipulator control (57 citations), established robust frameworks for guiding robotic arms using visual feedback — a cornerstone capability in modern robotics. His research extended powerfully into surgical applications, including visual servoing for minimally invasive surgery, ultrasound-guided instrument tracking, and automated laparoscopic positioning. More recently, he explored exoskeleton trajectory computation for upper limb rehabilitation, bridging biomechanics and assistive robotics. Through projects like PROSBOT, Morel also demonstrated a commitment to translating research into clinical contexts. Collectively, his body of work reflects a career dedicated to making robots safer, smarter, and more intuitive partners for human users.

Research Focus

Key Achievements

6
H-Index
7
Papers
181
Total Citations
26
Avg Citations/Paper
🏆 Most Cited Paper
How can human motion prediction increase transparency?
74 citations · 2008
📈 Most Prolific Year: 2002 (2 Papers)
🤝 Key Collaborators: 33
🏛 Institutions: Institut Systèmes Intelligents et de Robotique, Université de Strasbourg, Sorbonne Université, Centre National de la Recherche Scientifique

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago