Papers
2
Total Citations
61
H-Index
2
About
Bakur AlQaudi is a researcher at the forefront of physical human-robot interaction (pHRI), with a focus on developing intelligent, adaptive control systems that enable robots to safely and intuitively collaborate with humans. His most cited work, "Model reference adaptive impedance control for physical human-robot interaction" (2016, 52 citations), introduces a novel framework that allows robots to dynamically adjust their stiffness and damping in real time, ensuring stable and compliant interactions even in uncertain environments. This contribution has been foundational for applications in rehabilitation robotics and collaborative manufacturing. Expanding on this, AlQaudi’s later work, "Intelligent Human–Robot Interaction Systems Using Reinforcement Learning and Neural Networks" (2017, 9 citations), pioneers the integration of reinforcement learning with neural network architectures, enabling robots to autonomously learn optimal interaction policies from human feedback. His research bridges classical control theory with modern machine learning, offering a pathway toward more autonomous and adaptive robotic assistants. Through these contributions, AlQaudi has established himself as a key voice in the evolution of safe, intelligent, and human-aware robotic systems.
Research Focus
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Top Papers
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