Kamran Maqsood
Papers
3
Total Citations
66
H-Index
3
About
Kamran Maqsood is a leading researcher in the field of physical human-robot interaction, with a specific focus on developing intelligent controllers for robot-assisted rehabilitation. His work masterfully bridges iterative learning control and dynamic motion primitives to solve the fundamental challenge of motion uncertainty in human users. Maqsood’s key contribution lies in designing robotic systems that can learn and adapt to an individual’s unique movement characteristics in real-time, enabling the delivery of prescribed assistance or resistance forces during therapy. His most influential paper, "Iterative Learning-Based Robotic Controller With Prescribed Human–Robot Interaction Force" (2021), has garnered 28 citations and establishes a framework for robots to learn upper-limb dynamics. This is complemented by his 2023 work on dynamic motion primitives (23 citations), which addresses trajectory learning for force control. By separating task space into distinct control objectives, Maqsood’s research ensures consistent therapeutic assistance despite patient variability, directly enhancing rehabilitation outcomes. His work is pivotal for advancing adaptive, patient-specific robotic therapy.
Research Focus
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Top Papers
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