Papers
8
Total Citations
58
H-Index
5
About
Hadi Beik-Mohammadi is a robotics researcher whose work sits at the intersection of human-robot interaction, reinforcement learning, and intelligent control systems. His research explores how robots can perceive, decide, and act in dynamic environments—from designing socially aware personalities for human-robot collaboration to enabling dexterous manipulation through mixed-reality and deep reinforcement learning. In his most cited work, he demonstrated that a robot’s social personality significantly influences user acceptance, a finding with broad implications for assistive and service robotics. He has also advanced practical reinforcement learning by proposing task simplification methods that dramatically reduce the sample complexity of training robotic motor policies. His contributions extend to control theory, where he developed a self-adaptive fuzzy PD controller for omnidirectional robots, and to mechanical design, including the Exodex Adam—a reconfigurable haptic interface for whole-hand teleoperation. With over 50 citations across his publications, Beik-Mohammadi’s work has shaped key areas in robot navigation, motion planning, and human-centered AI. His research continues to push the boundaries of how robots learn, move, and interact with people in real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Designing a Personality-Driven Robot for a Human-Robot Interaction Scenario15 citations · 2019
- 2
- 3Structure and dynamic modelling of a spherical robot7 citations · 2012
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- 5Mixed-Reality Deep Reinforcement Learning for a Reach-to-grasp Task7 citations · 2019
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