In testing with human subjects, the researchers found that study participants used 24.3% less metabolic energy when walking in the robotic exoskeleton than without the exoskeleton. Participants used 13.1% less energy when running in the exoskeleton, and 15.4% less energy when climbing stairs. Credit: Hao Su, NC State University
“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, corresponding author of a paper on the work which will be published today in the journal“Exoskeletons have enormous potential to improve human locomotive performance,” says Su, who is an associate professor of mechanical and aerospace engineering at North Carolina State University.
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.
For example, in testing with human subjects, the researchers found that study participants used 24.3% less metabolic energy when walking in the robotic exoskeleton than without the exoskeleton. Participants used 13.1% less energy when running in the exoskeleton, and 15.4% less energy when climbing stairs.