sensors

researchers-make-“neuromorphic”-artificial-skin-for-robots

Researchers make “neuromorphic” artificial skin for robots

The nervous system does an astonishing job of tracking sensory information, and does so using signals that would drive many computer scientists insane: a noisy stream of activity spikes that may be transmitted to hundreds of additional neurons, where they are integrated with similar spike trains coming from still other neurons.

Now, researchers have used spiking circuitry to build an artificial robotic skin, adopting some of the principles of how signals from our sensory neurons are transmitted and integrated. While the system relies on a few decidedly not-neural features, it has the advantage that we have chips that can run neural networks using spiking signals, which would allow this system to integrate smoothly with some energy-efficient hardware to run AI-based control software.

Location via spikes

The nervous system in our skin is remarkably complex. It has specialized sensors for different sensations: heat, cold, pressure, pain, and more. In most areas of the body, these feed into the spinal column, where some preliminary processing takes place, allowing reflex reactions to be triggered without even involving the brain. But signals do make their way along specialized neurons into the brain, allowing further processing and (potentially) conscious awareness.

The researchers behind the recent work, based in China, decided to implement something similar for an artificial skin that could be used to cover a robotic hand. They limited sensing to pressure, but implemented other things the nervous system does, including figuring out the location of input and injuries, and using multiple layers of processing.

All of this started out by making a flexible polymer skin with embedded pressure sensors that were linked up to the rest of the system via conductive polymers. The next layer of the system converted the inputs from the pressure sensors to a series of activity spikes—short pulses of electrical current.

There are four ways that these trains of spikes can convey information: the shape of an individual pulse, through their magnitude, through the length of the spike, and through the frequency of the spikes. Spike frequency is the most commonly used means of conveying information in biological systems, and the researchers use that to convey the pressure experienced by a sensor. The remaining forms of information are used to create something akin to a bar code that helps identify which sensor the reading came from.

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Scientists built an AI co-pilot for prosthetic bionic hands

To test their AI-powered hand, the team asked intact and amputee participants to manipulate fragile objects: pick up a paper cup and drink from it, or take an egg from a plate and put it down somewhere else. Without the AI, they could succeed roughly one or two times in 10 attempts. With the AI assistant turned on, their success rate jumped to 80 or 90 percent. The AI also decreased the participants’ cognitive burden, meaning they had to focus less on making the hand work.

But we’re still a long way away from seamlessly integrating machines with the human body.

Into the wild

“The next step is to really take this system into the real world and have someone use it in their home setting,” Trout says. So far, the performance of the AI bionic hand was assessed under controlled laboratory conditions, working with settings and objects the team specifically chose or designed.

“I want to make a caveat here that this hand is not as dexterous or easy to control as a natural, intact limb,” George cautions. He thinks that every little increment that we make in prosthetics is allowing amputees to do more tasks in their daily life. Still, to get to the Star Wars or Cyberpunk technology level where bionic prostheses are just as good or better than natural limbs, we’re going to need more than just incremental changes.

Trout says we’re almost there as far as robotics go. “These prostheses are really dexterous, with high degrees of freedom,” Trout says, “but there’s no good way to control them.” This in part comes down to the challenge of getting the information in and out of users themselves. “Skin surface electromyography is very noisy, so improving this interface with things like internal electromyography or using neural implants can really improve the algorithms we already have,” Trout argued. This is why the team is currently working on neural interface technologies and looking for industry partners.

“The goal is to combine all these approaches in one device,” George says. “We want to build an AI-powered robotic hand with a neural interface working with a company that would take it to the market in larger clinical trials.”

Nature Communications, 2025.  DOI: 10.1038/s41467-025-65965-9

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