Papers
5
Total Citations
36
H-Index
3
About
Behnam Yazdankhoo’s research lies at the intersection of teleoperation, haptics, and human-robot interaction, with a focus on making advanced robotic control systems more accessible and adaptive. His key contributions include developing adaptive online prediction methods for operator position in teleoperation systems with unknown time-varying delays—work that has garnered 23 citations and demonstrated practical viability through both simulation and experimental validation. Yazdankhoo also introduced a cost-effective haptic teleoperation framework that addresses the limitations of existing systems, which are often prohibitively expensive or lack sufficient rotational degrees of freedom; this work provides an economical alternative using two robots as master and slave. In humanoid robotics, he has advanced ball trajectory prediction through friction-based studies and combined k-NN regression with autoregression methods, achieving notable results. Notably, his research on the influence of sex factors in modeling human hand/arm dynamics during teleoperation interaction highlights a critical gap in the field, offering insights into how gender differences affect haptic system design. With a growing citation record and a focus on practical, inclusive solutions, Yazdankhoo’s work is shaping the future of accessible teleoperation and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 2Cost-effective Haptic Teleoperation Framework: Design and Implementation4 citations · 2023
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