Zahraa Awad

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About

Zahraa Awad is a researcher advancing the field of bipedal robotics, with a specific focus on terrain classification and human-robot interaction. Her work addresses a critical challenge in legged locomotion: enabling humanoid robots to perceive and adapt to diverse ground surfaces in real time. In her highly cited 2020 comparative study, Awad evaluated four machine learning techniques—including Support Vector Machines—for classifying nine distinct terrains using sensor data from the NAO humanoid robot, leveraging force, current, position, and inertial sensors. This foundational work has garnered 5 citations and established a benchmark for sensor-based terrain identification. Building on this, her 2022 paper introduced an innovative online training system enhanced with augmented reality, allowing human users to actively assist robots in improving real-time terrain classification. This human-aided approach, cited 2 times, represents a novel fusion of robotics and AR, demonstrating Awad’s commitment to practical, interactive solutions for autonomous navigation. Her contributions are vital for the development of more adaptable and resilient bipedal robots, with potential applications in search-and-rescue, exploration, and assistive technologies. Awad’s research stands out for its integration of machine learning, sensor fusion, and human-in-the-loop systems, marking her as a promising voice in modern robotics.