Researchers at North Carolina State University demonstrated a new method that leverages artificial intelligence (AI) and computer simulations to train robotic exoskeletons to autonomously help users save energy while walking, running, and climbing stairs. They are exploring how the approach could improve the performance of robotic prosthetic devices.
Specifically, the researchers focused on improving autonomous control of embodied AI systems—which are systems where an AI program is integrated into a physical robot technology. This work focused on teaching robotic exoskeletons how to assist able-bodied people with various movements. Normally, users have 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 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,” said Hao Su, PhD, corresponding author of a paper on the work, and an associate professor of mechanical and aerospace engineering at the university.
“Exoskeletons have enormous potential to improve human locomotive performance,” Su said. “However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws. The key idea here is that the embodied AI in a portable exoskeleton is learning how to help people walk, run, or climb in a computer simulation, without requiring any experiments. This work is essentially making science fiction reality—allowing people to burn less energy while conducting a variety of tasks.”
For example, in testing with human subjects, the researchers found that study participants used 24.3 percent less metabolic energy when walking in the robotic exoskeleton than without it. Participants used 13.1 percent less energy when running in the exoskeleton, and 15.4 percent less energy when climbing stairs.
“It’s important to note that these energy reductions are comparing the performance of the robotic exoskeleton to that of a user who is not wearing an exoskeleton,” Su said. “That means it’s a true measure of how much energy the exoskeleton saves.”
While this study focused on the researchers’ work with able-bodied people, the method also applies to robotic exoskeleton applications aimed at helping people with mobility impairments.
“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. And we are also interested in exploring how the method could improve the performance of robotic prosthetic devices for amputee populations,” Su said.
Editor’s note: This story was adapted from materials provided by North Carolina State University.
The paper, “Experiment-free exoskeleton assistance via learning in simulation,” was published in the journal Nature.
