Researchers are using AI to finetune a robotic prosthesis to improve manual dexterity by finding the right balance between human and machine control.
Whether your reaching for a mug,a pencil,or someone’s hand,you don’t need to consciously instruct each of your fingers on where they need to go to get a proper grip.
The loss of that intrinsic ability is one of the many challenges people with prosthetic arms and hands face. Even with the most advanced robotic prostheses, these everyday activities come with an added cognitive burden as users purposefully open and close their fingers around a target.
Researchers at the University of Utah are now using artificial intelligence to solve this problem.By integrating proximity and pressure sensors into a commercial bionic hand and then training an artificial neural network on grasping postures,the researchers developed an autonomous approach that is more like the natural,intuitive way we grip objects. When working in tandem with the artificial intelligence, study participants demonstrated greater grip security, greater grip precision, and less mental effort.
Critically,the participants were able to perform numerous everyday tasks,such as picking up small objects and raising a cup,using different gripping styles,all without extensive training or practice.
The study was led by engineering professor Jacob A. George and Marshall Trout, a postdoctoral researcher in the Utah NeuroRobotics Lab, and appears in the journal nature Communications.
“As lifelike as bionic arms are becoming, controlling them is still not easy or intuitive,” Trout says. “Nearly half of all users will abandon their prosthesis, often citing their poor controls and cognitive burden.”
One problem is that most commercial bionic arms and hands have no way of replicating the sense of touch that normally gives us intuitive, reflexive ways of grasping objects. Dexterity is not simply a matter of sensory feedback, however. We also have subconscious models in our brains that simulate and anticipate hand-object interactions; a “smart” hand would also need to learn these automatic responses over time.
The Utah researchers addressed the first problem by outfitting a