Elaheh Sabbaghi
Papers
1
Total Citations
11
H-Index
1
About
Elaheh Sabbaghi is a robotics researcher whose work lies at the intersection of computer vision, human-robot interaction, and imitation learning. Her most-cited paper, "Learning of gestures by imitation using a monocular vision system on a humanoid robot" (2014, 11 citations), introduces a pioneering vision-based system that enables humanoid robots to recognize and replicate upper-body gestures using only a single onboard camera. This approach eliminates the need for markers, special clothing, or multiple sensors, making imitation learning more natural and accessible for real-world human-robot collaboration. Sabbaghi’s contributions address a fundamental challenge in robotics: enabling machines to learn from human demonstration without intrusive hardware. Her work has been cited in studies on gesture recognition, robot learning, and assistive robotics, underscoring its relevance to both academic research and practical applications. By advancing monocular vision techniques for gesture imitation, Sabbaghi has helped pave the way for more intuitive, low-cost robotic systems that can learn from and interact with humans in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1