“This work proposes and demonstrates a new machine-learning framework that bridges the gap between simulation and reality to autonomously control wearable robots to improve mobility and health of humans,” says Hao Su, an associate professor of mechanical and aerospace engineering at North Carolina State University.

“Exoskeletons have enormous potential to improve human locomotive performance,” Su, the corresponding author of a new study published in Nature, explains. “However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws.”

The groundbreaking idea behind this work is that the embodied AI in a portable exoskeleton learns how to help people walk, run, or climb in a computer simulation, without requiring any real-world experiments.

The researchers concentrated on enhancing the autonomous control of embodied AI systems, which integrate AI programs into physical robot technology. The focus was on teaching robotic exoskeletons to assist able-bodied people with various movements.

Typically, users need to spend hours training an exoskeleton so that the technology knows how much force is needed—and when to apply that force—to help users walk, run, or climb stairs. The new method allows users to utilize the exoskeletons immediately.

“This work is essentially making science fiction reality—allowing people to burn less energy while conducting a variety of tasks,” says Su.

“We have developed a way to train and control wearable robots to directly benefit humans,” says first author Shuzhen Luo, a former postdoctoral researcher at NC State who is now an assistant professor at Embry-Riddle Aeronautical University.

In testing with human subjects, researchers found that study participants used 24.3% less metabolic energy when walking in the robotic exoskeleton compared to walking without it. Participants used 13.1% less energy when running in the exoskeleton and 15.4% less energy when climbing stairs.

“These energy reductions are a true measure of how much energy the exoskeleton saves,” Su points out, as they compare the performance of the robotic exoskeleton to that of a user not wearing an exoskeleton.

While this study focused on able-bodied individuals, the new method is also applicable to robotic exoskeletons designed to assist people with mobility impairments. “Our framework may offer a generalizable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals,” says Su.

“We are in the early stages of testing the new method’s performance in robotic exoskeletons being used by older adults and people with neurological conditions, such as cerebral palsy. We are also exploring how the method could improve the performance of robotic prosthetic devices for amputee populations.”

Additional coauthors of the study hail from NC State, the University of North Carolina at Chapel Hill, the University of Michigan, the University of California, Los Angeles, the Korea Advanced Institute of Science and Technology, and the New Jersey Institute of Technology.

The work was funded by the National Science Foundation, the National Institute on Disability, Independent Living, and Rehabilitation Research, a Switzer Research Fellowship, and the National Institutes of Health.

Luo and Su are co-inventors on intellectual property related to the controller discussed in this work. Su is also a cofounder of, and has a financial interest in, Picasso Intelligence, LLC, which develops exoskeletons.

By Impact Lab