US engineers design AI bionic hand that grips with human-like precision

Engineers at the University of Utah have given a bionic hand a mind of its own. By equipping a commercial prosthetic with pressure and proximity sensors and training an AI neural network on natural grasping movements, the team created a hand that grips more intuitively and securely.

Study participants were able to perform everyday tasks—such as picking up small items or raising a cup—with greater precision and less mental effort, without extensive practice. According to researchers, the breakthrough points to a future where prosthetics feel and function more like natural limbs. In May 2025, Korean researchers presented an ultra-light robotic hand with shape-adaptive grips, precise fingertip control, and thumb flexibility, powered by a single actuator.

Dexterity meets AI Everyday tasks like reaching for a mug, picking up a pencil, or shaking someone’s hand rely on the brain’s ability to control finger movements instinctively. For people using prosthetic arms and hands, this natural dexterity is often lost. Even with advanced robotic prostheses, performing simple actions requires extra mental effort, as users must consciously control each finger to grasp objects.

According to the team, a major challenge is that most commercial bionic hands lack the sense of touch that allows humans to grip intuitively. Yet dexterity involves more than sensory feedback—our brains also subconsciously model and predict hand-object interactions, enabling reflexive, precise movements.

“As lifelike as bionic arms are becoming, controlling them is still not easy or intuitive. Nearly half of all users will abandon their prosthesis, often citing their poor controls and cognitive burden, said Marshall Trout, a postdoctoral researcher in the Utah NeuroRobotics Lab, in a statement.

To tackle these challenges, researchers at the University of Utah partnered with TASKA Prosthetics to enhance a commercial robotic hand. They equipped the fingers with custom fingertips that detect pressure and include optical proximity sensors, mimicking the subtle sense of touch. The sensors are sensitive enough to detect something as light as a cotton ball landing on the hand. The team then trained an artificial neural network on the proximity data, teaching the hand to automatically adjust each finger’s position for a stable, precise grip.

With each finger operating independently yet in coordination, the hand can form an optimal grasp on virtually any object. Researchers claim this combination of touch replication and AI-driven movement allows the prosthetic to function more naturally, reducing mental strain and improving everyday usability.

Intuitive hand control As the development progressed, one challenge remained: ensuring the prosthetic could adapt if the user didn’t intend to grasp an object in the AI-predicted manner, such as when they wanted to release it. To solve this, the researchers developed a bioinspired system that shares control between the user and the AI, carefully balancing human intent with machine precision.

The AI augments natural movements, enhancing grip accuracy while reducing the mental effort required to complete tasks. The team tested the system with four participants who had amputations between the wrist and elbow. In addition to performing better on standardized assessments, participants successfully completed everyday tasks that require fine motor control.

Activities as simple as drinking from a plastic cup, which demand precise pressure to avoid dropping or crushing it, became manageable. According to researchers, combining AI assistance with human intent enabled the prosthetic hand to offer a more intuitive, natural experience, allowing users to perform daily tasks with less cognitive strain and greater confidence.

“By adding some artificial intelligence, we were able to offload this aspect of grasping to the prosthesis itself. The end result is more intuitive and more dexterous control, which allows simple tasks to be simple again, said Jacob A. George, a postdoctoral researcher in the Utah NeuroRobotics Lab, in a statement. The study team is exploring implanted neural interfaces that would allow users to control prostheses with their minds while restoring a sense of touch.

Their next steps involve integrating these technologies so that the enhanced sensors improve tactile function and the intelligent prosthetic can operate seamlessly with thought-based control.