3D-Printed Prosthetic Hand with Hybrid Sensors for Precise Grip Control

Researchers at Johns Hopkins University have developed a prosthetic hand capable of gripping objects with human-like precision. The technology combines soft and rigid materials with a multi-layered sensor system to recognize different surface textures and automatically adjust to the characteristics of the object. The hand is controlled by muscle signals from the forearm, while machine learning processes the artificial touch sensor signals to generate realistic feedback.

The device features a flexible multi-finger system made of polymer-based structures and a stable, 3D-printed internal skeleton. The three layers of tactile sensors mimic human skin, allowing not only the detection of touch but also the distinction between various materials. Air chambers in the finger segments can be precisely controlled to achieve differentiated grip strength.

“The goal from the beginning has been to create a prosthetic hand that we model based on the human hand’s physical and sensing capabilities—a more natural prosthetic that functions and feels like a lost limb,” said Sriramana Sankar, a Johns Hopkins PhD student in biomedical engineering who led the work. “We want to give people with upper-limb loss the ability to safely and freely interact with their environment, to feel and hold their loved ones without concern of hurting them.”

In laboratory tests, the prosthesis demonstrated high accuracy in handling everyday objects, including delicate plush toys, cardboard boxes, and metal water bottles. One particularly impressive example was its ability to grip a thin plastic cup filled with water using only three fingers while applying just enough force to prevent deformation.

“We’re combining the strengths of both rigid and soft robotics to mimic the human hand,” Sankar said. “The human hand isn’t completely rigid or purely soft—it’s a hybrid system, with bones, soft joints, and tissue working together. That’s what we want our prosthetic hand to achieve. This is new territory for robotics and prosthetics, which haven’t fully embraced this hybrid technology before. It’s being able to give a firm handshake or pick up a soft object without fear of crushing it.”

The technology employs a neuron-inspired control system that translates artificial sensor signals into nerve-like impulses. Nitish Thakor, professor of biomedical engineering at Johns Hopkins University, explains that the system is designed to recognize temperature differences and detect slipping objects. Future improvements will include stronger grip force, additional sensors, and more durable materials. While current research has already produced promising results, further development is crucial to integrating this technology into everyday prosthetic use.

“If you’re holding a cup of coffee, how do you know you’re about to drop it? Your palm and fingertips send signals to your brain that the cup is slipping,” Thakor said. “Our system is neurally inspired—it models the hand’s touch receptors to produce nervelike messages so the prosthetics’ ‘brain,’ or its computer, understands if something is hot or cold, soft or hard, or slipping from the grip.”

“This hybrid dexterity isn’t just essential for next-generation prostheses,” Thakor said. “It’s what the robotic hands of the future need because they won’t just be handling large, heavy objects. They’ll need to work with delicate materials such as glass, fabric, or soft toys. That’s why a hybrid robot, designed like the human hand, is so valuable—it combines soft and rigid structures, just like our skin, tissue, and bones.”