Masood Dehghan
Papers
14
Total Citations
125
H-Index
6
About
Masood Dehghan is a robotics researcher whose work sits at the intersection of assistive robotics, human-robot interaction, and visual learning. His research focuses on enabling robots to understand and replicate human manipulation tasks, particularly in the context of Activities of Daily Living (ADLs). Dehghan’s major contributions include developing flexible virtual fixture interfaces for tele-manipulation and novel methods for robot eye-hand coordination learning through human demonstrations, using inverse reinforcement learning to infer task specifications. His work on real-time human-robot interaction using Kinect sensors and salient closed boundary tracking via line segments perceptual grouping has advanced the field of intuitive robot control. With his most cited paper on quantitative analysis of ADLs for assistive robotics (24 citations), Dehghan has demonstrated the potential of wheelchair-mounted robotic manipulators to improve functional independence for the elderly and disabled. Notably, his system for online tool and task learning was a finalist in the prestigious KUKA Innovation Award, highlighting the practical impact of his research. Dehghan’s work continues to push boundaries in making robots more capable assistants in daily life.
Research Focus
Key Achievements
Top Papers
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- 9A Geometric Perspective on Visual Imitation Learning4 citations · 2020
- 10Online Tool and Task learning via Human Robot Interaction.3 citations · 2018