For many people living with upper limb amputations, the promise of bionic hands has always seemed tantalizing—yet even the most advanced prosthetic devices have struggled to deliver the seamless, intuitive control that natural hands provide. That may be about to change, thanks to a groundbreaking study out of the University of Utah’s NeuroRobotics Lab. On December 11, 2025, researchers announced a major leap forward: by outfitting a commercial prosthetic hand with custom fingertips and integrating artificial intelligence (AI), they have created a system that allows users to grasp objects in a way that feels astonishingly natural—and far less mentally taxing.
Everyday actions like picking up a mug or shaking someone’s hand typically happen without conscious thought. Our brains, muscles, and nerves work together in a symphony of motion and feedback, letting us grip, hold, and release objects with little effort. But for amputees using prosthetic hands, this automatic ease is lost. Even with state-of-the-art bionic arms, users must focus intently on opening and closing their fingers, often leading to frustration and abandonment of the device. "Nearly half of all users will abandon their prosthesis, often citing their poor controls and cognitive burden," postdoctoral researcher Marshall Trout explained to Design & Development Today.
So, what’s been missing? According to the research team led by engineering professor Jacob A. George and Trout, two key elements: a true sense of touch, and the ability for the device to anticipate and adapt to the user’s intent in real time. Traditional prosthetic hands lack the fine sensory feedback that we take for granted, making it tough to judge how hard to grip a delicate object—or when to let go. Even more, they require users to micromanage every movement, which is both exhausting and unnatural.
The Utah team tackled these challenges head-on. First, they enhanced a commercial bionic hand (manufactured by TASKA Prosthetics) with custom fingertips designed to detect pressure and equipped with optical proximity sensors. These sensors don’t just feel when something is touched—they can actually "see" objects before contact, much like how our own fingers sense and prepare to grasp.
But the real magic comes from the AI. The researchers trained an artificial neural network on a variety of grasping postures, allowing each finger to operate independently and in parallel. When a user reaches for an object, each digit moves to just the right spot, forming a stable and secure grip. The system is so sensitive that it can detect an almost weightless cotton ball falling onto the fingertips. "By adding some artificial intelligence, we were able to offload this aspect of grasping to the prosthesis itself," George told Design & Development Today. "The end result is more intuitive and more dexterous control, which allows simple tasks to be simple again."
Of course, there’s a tricky balance to strike: too much machine control, and the user feels like they’re fighting a robot for dominance; too little, and the old problems of awkward, clumsy movement return. The Utah researchers found a solution in what they call "shared human-machine control." Here’s how it works: the user provides overall direction—say, the intention to pick up a cup—while the AI fine-tunes each finger’s position for precision and security. A dynamically weighted sum merges the user’s intent (captured through surface electromyography, which reads muscle signals from the residual limb) with the AI’s assistance.
"What we don’t want is the user fighting the machine for control. In contrast, here the machine improved the precision of the user while also making the tasks easier," Trout said. "In essence, the machine augmented their natural control so that they could complete tasks without having to think about them." This approach, according to a Nature Communications study led by Trout and colleagues, is the first real-world demonstration of shared control among multiple amputee participants using a commercial prosthesis.
The results? Four individuals with amputations between the elbow and wrist—known as transradial amputees—tested the system and saw striking improvements. They could pick up small objects, raise a cup, and perform other daily activities with far greater grip security and precision, all while expending less mental effort. Even simple tasks, like drinking from a plastic cup, became dramatically easier. The system’s distributed sensors proved more robust and energy-efficient than camera-based alternatives, providing reliable multiangle perception and allowing each finger to "see" and respond to its environment independently.
According to the Nature Communications publication, this sensor-rich, AI-assisted approach could be a game-changer for clinical practice. By reducing the training required and making the device more intuitive, clinicians may soon be able to offer amputees prosthetic hands that truly feel like a natural extension of themselves. "These findings point to a new generation of prosthetic devices that rely on shared autonomy to support more natural control," the study authors wrote. The hope is that greater dexterity and user satisfaction will follow, and that more amputees will embrace—and stick with—their prosthetic devices.
Looking ahead, the Utah NeuroRobotics Lab has even more ambitious goals. The team is exploring implanted neural interfaces that would allow individuals to control prostheses with their minds and, remarkably, to receive a sense of touch from the device. "Next steps, the team plans to blend these technologies, so that their enhanced sensors can improve tactile function and the intelligent prosthesis can blend seamlessly with thought-based control," George explained.
There’s still a long road before such systems become widely available, but the implications are enormous. Future research will focus on making these enhancements accessible to more people and on developing adaptive algorithms that can further personalize bionic hand performance. As the technology matures, prosthetic hands may one day offer not just restored function, but a genuine sense of embodiment—where the line between machine and self all but disappears.
For now, though, this breakthrough from the University of Utah stands as a beacon of hope for amputees and clinicians alike. By harnessing the power of artificial intelligence, proximity sensing, and shared control, the dream of a truly natural, intuitive bionic hand is closer than ever before.
