Papers

6

Total Citations

125

H-Index

5

About

Hamed Razavi investigates the intersection of control theory and biomechanics to create more intuitive and resilient bipedal robots. His research centers on human intention detection for physical human-robot interaction, particularly in collaborative walking tasks, and on developing stable, periodic gaits through symmetry and self-synchronization principles. A key contribution is his multiclass classification framework for recognizing human partners' movement intentions during cooperative object carrying, enabling safer and more responsive human-robot teams. In legged locomotion, Razavi pioneered the use of symmetry in designing stable periodic gaits and introduced time-projection control for push recovery, allowing bipedal robots like COMAN to dynamically adjust stepping strategies when disturbed. His work on self-synchronization demonstrates how 3D bipedal walking can achieve natural stability without explicit balancing, while his unified control structure spans the continuum from standing balance to walking using minimal parameter adjustments. With over 125 combined citations across his most influential papers, Razavi's contributions advance both the theoretical foundations and practical implementation of humanoid locomotion and human-robot collaboration.

Research Focus

Key Achievements

5
H-Index
6
Papers
125
Total Citations
21
Avg Citations/Paper
🏆 Most Cited Paper
Human Intention Detection as a Multiclass Classification Problem: Application in Physical Human–Robot Interaction While Walking
41 citations · 2018
📈 Most Prolific Year: 2016 (2 Papers)
🤝 Key Collaborators: 10
🏛 Institutions: École Polytechnique Fédérale de Lausanne, University of Michigan–Ann Arbor

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago