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Georgia Tech's 'Universal' Exoskeleton Control Brings Robotic Assist to the Real World

For decades, the dream of donning a robotic exoskeleton to enhance human strength and endurance has been confined to science fiction. But a breakthrough from engineers at Georgia Tech could finally make those wearable robot suits a practical reality.



Researchers in the lab of professor Aaron Young have developed a novel "universal" control system that allows robotic exoskeletons to seamlessly adapt their assistance to any activity - walking, stair climbing, ramp ascent and more - without any manual adjustments or training required.

Their AI-powered system, described in the journal Science Robotics, relies on deep learning algorithms to automatically tailor how much force and torque an exoskeleton provides, simply by interpreting data from onboard sensors that monitor the user's muscles and joint movements.

"We stopped trying to classify human movement into discrete modes like walking or stair climbing," said Dean Molinaro, lead author of the study. "Real movement is too messy for that. Instead, we use the underlying human physiology as the guide for how much assistance to provide at any given moment."

Powered by force and motion capture data from test subjects, the deep learning controller essentially acts as a real-time "translator" between sensor signals and the robotic torque output needed for smooth, responsive assistance across any terrain or transition.

The breakthrough allows exoskeleton users to freely move between different activities like walking, stair climbing, and even transitional movements, without having to manually switch operating modes or recalibrate the device. It simply senses what the person is doing and adjusts automatically.

"There's no subject-specific tuning or adjusting parameters to make it work - it adapts to each individual's dynamics on its own," said Young. "That's a huge leap compared to much of the prior work which required extensive calibration."

In their lab tests using a hip exoskeleton system they developed, the Georgia Tech team found their universal controller could provide meaningful assistance that reduced wearers' metabolic rates and musculoskeletal loading compared to moving unaided and unassisted.

The study focused on "partial assist" exoskeletons designed to support and enhance human movements, rather than fully automate them. But Young believes the same adaptive AI control approach could work for full automation as well.

While robotic exoskeletons have been researched for rehabilitative purposes like helping stroke victims regain mobility, their impacts could extend well beyond medical applications into the workplace.

"Imagine how this assistive technology could benefit soldiers, baggage handlers, construction crews - really any physically demanding job where injury risk is high," said Young. "Exoskeletons like this could prevent a lot of musculoskeletal pain and lost productivity across many industries."

Previous exoskeleton control systems relied on manually tuning and calibrating parameters for specific operating scenarios or environments - a process that limited their real-world deployment and hindered widespread adoption beyond research labs.

By introducing a universal, self-adapting AI controller, the Georgia Tech breakthrough finally brings exoskeleton technologies closer to being viable in unstructured settings like homes, hospitals, and job sites.

"This is the first time a unified control framework like this has been demonstrated to actually assist humans across multiple activities seamlessly and without any prior tuning," said Molinaro. "It bridges a longstanding gap toward getting these systems out into the real world."

Several research groups and startups are pursuing commercial exoskeleton systems aimed at augmenting individuals' mobility and strength. But the Georgia Tech team believes their approach could leapfrog many current offerings in adaptability and ease of use.

While the researchers have only implemented their controller on a hip exoskeleton so far, the same deep learning techniques could theoretically work across full-body powered suits - bringing us one step closer to realizing the futuristic, robotic-augmented visions once confined to film and science fiction.

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