Scientists at Georgia Tech have created a robotic exoskeleton device equipped with a universal control framework. Unlike previous iterations, this framework eliminates the need for training, calibration, or complex adjustments to algorithms.
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Wear-and-Go: AI Robotic Exoskeleton Devised
The revolutionary exoskeleton aims to protect workers from injuries and assist stroke patients in regaining mobility. Researchers focused on developing a unified control framework that seamlessly supports users in walking, standing, climbing stairs, or ramps without the need to switch between modes or rely on predictive algorithms.
Powered by AI-backed deep learning algorithms, the exoskeleton autonomously manages assistance based on the user’s underlying physiological conditions. Instead of categorizing movements into discrete modes, the control system adapts to the user’s physiology in real-time, reducing metabolic and biomechanical effort.
The researchers observed significant improvements in metabolic efficiency and joint effort compared to conventional exoskeletons. Importantly, the control system is tailored for partial-assist devices, enhancing movement without entirely replacing the user’s effort.
By utilizing extensive force and motion-capture data from diverse subjects, the researchers trained the exoskeleton to perform various activities, including walking, ascending/descending stairs, and navigating ramps. The resulting controller represents a significant step towards real-world viability for robotic exoskeleton devices.
Published in the journal Science Robotics, this groundbreaking research paves the way for widespread adoption of AI-driven exoskeleton technology in homes, workplaces, and other applications, promising to revolutionize mobility assistance and injury prevention.
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