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About

Rahaf Rahal is a robotics researcher specializing in teleoperation, haptic shared control, and human-robot interaction, with a particular focus on making robotic manipulation safer, more intuitive, and more responsive to human needs. Her most influential work introduces adaptive impedance control architectures for bilateral teleoperation, leveraging Learning from Demonstration to enable robots to learn variable stiffness policies during contact tasks — a contribution that has garnered 85 citations and represents a significant advance in intelligent robotic control. Rahal has also made notable strides in haptic shared control, developing frameworks that balance autonomy and human input to enhance operator comfort and reduce physical strain during telemanipulation, reflected in her widely cited 2020 paper with 59 citations. Her research extends to specialized domains including robot-assisted cutting under nonholonomic constraints, with applications in surgery, nuclear decommissioning, and manufacturing. More recently, she has explored human-inspired learning from demonstration for manipulation of fragile objects such as food items, addressing real-world industrial automation challenges. With a cumulative citation profile reflecting growing community recognition, Rahal's work sits at a compelling intersection of control theory, machine learning, and human-centered robotics.