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Engineering | School of Electrical, Computer and Energy Engineering

Reinforcement learning expedites ‘tuning’ of robotic prosthetics

Reinforcement learning expedites ‘tuning’ of robotic prosthetics

Researchers from Arizona State University, North Carolina State University and the University of North Carolina have developed an intelligent system for “tuning” powered prosthetic knees, allowing users to walk comfortably with the prosthetic device in minutes rather than hours. The system is the first to rely solely on reinforcement learning to tune a robotic prosthesis.

Professor Jennie Si

Jennie Si, a professor in the School of Electrical, Computer and Energy Engineering in ASU’s Ira A. Fulton Schools of Engineering, co-authored a paper released today in the journal IEEE Transactions on Cybernetics.

Using the reinforcement learning framework, Si developed the algorithm that essentially “teaches” a prosthetic device to adapt to a user’s normal walking gait using data collected from a suite of sensors in the device and the person’s natural walking pattern.

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Skills

Posted on

January 17, 2019