Engineers at Johns Hopkins have developed a pioneering prosthetic hand that can grip plush toys, water bottles and other everyday objects like a human, carefully conforming and adjusting its grasp to avoid damaging or mishandling whatever it holds.

The system’s hybrid design is a first for robotic hands, which have typically been too rigid or too soft to replicate a human’s touch when handling objects of varying textures and materials. The innovation, reported in Science Advances, offers a promising solution for people with hand loss and could improve how robotic arms interact with their environment.

“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,” says Sriramana Sankar, a Johns Hopkins biomedical engineer 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.”

The device, developed by the same Neuroengineering and Biomedical Instrumentation Lab that in 2018 created the world’s first electronic “skin” with a humanlike sense of pain, features a multifinger system with rubberlike polymers and a rigid 3D-printed internal skeleton.

Its three layers of tactile sensors, inspired by the layers of human skin, allow it to grasp and distinguish objects of various shapes and surface textures, rather than just detect touch. Each of its soft air-filled finger joints can be controlled with the forearm’s muscles, and machine learning algorithms focus the signals from the artificial touch receptors to create a realistic sense of touch, Sankar says.

In the lab, the hand identified and manipulated 15 everyday objects, including delicate stuffed toys, dish sponges and cardboard boxes, as well as pineapples, metal water bottles and other sturdier items. In the experiments, the device achieved the best performance compared with the alternatives, successfully handling objects with 99.69% accuracy and adjusting its grip as needed to prevent mishaps. The best example was when it nimbly picked up a thin, fragile plastic cup filled with water, using only three fingers without denting it.

“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,” says Johns Hopkins biomedical engineer Nitish Thakor, who directed the work. “Our system is neurally inspired — it models the hand’s touch receptors to produce nervelike messages so the prosthetic’s ‘brain,’ or its computer, understands if something is hot or cold, soft or hard, or slipping from the grip.”