Universal controller could push robotic prostheses, exoskeletons into real-world use

A team of researchers has developed a universal approach to controlling robotic exoskeletons that requires no training, no calibration, and no adjustments to complicated algorithms. Instead, users can don the 'exo' and go. Their system uses a kind of artificial intelligence called deep learning to autonomously adjust how the exoskeleton provides assistance, and they've shown it works seamlessly to support walking, standing, and climbing stairs or ramps.

A team of researchers in Aaron Young's lab have developed a universal approach to controlling robotic exoskeletons that requires no training, no calibration, and no adjustments to complicated algorithms. Instead, users can don the"exo" and go. "We stopped trying to bucket human movement into what we call discretized modes -- like level ground walking or climbing stairs -- because real movement is a lot messier," said Dean Molinaro, lead author on the study and a recently graduated Ph.D. student in Young's lab."Instead, we based our controller on the user's underlying physiology. What the body is doing at any point in time will tell us everything we need to know about the environment.

"What's so cool about this is that it adjusts to each person's internal dynamics without any tuning or heuristic adjustments, which is a huge difference from a lot of work in the field," Young said."There's no subject-specific tuning or changing parameters to make it work." And like the motion-capture studios used to make movies, every movement was recorded and cataloged to understand what joints were doing for each activity.study is"application agnostic," as Young put it. Yet their controller offers the first bridge to real-world viability for robotic exoskeleton devices.

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