In a new paper — Printed Synaptic Transistors based Electronic Skin for Robots to Feel and Learn — published in the journal Science Robotics, a team of engineers from the University of Glasgow reveal how they have developed an artificial skin with a new type of processing system based on ‘synaptic transistors’. This skin ‘mimics the brain’s neural pathways in order to learn’, and a robot hand that uses the smart skin shows a remarkable ability to learn to react to external stimuli.

Scientists have been working for decades to build artificial skin with touch sensitivity, with one widely explored method being spreading an array of contact or pressure sensors across the electronic skin’s surface to allow it to detect when it meets an object.

Data from the sensors is then sent to a computer to be processed and interpreted. However, the sensors typically produce a large volume of data, which can take time to be properly processed and responded to, thereby introducing delays that could reduce the skin’s potential effectiveness in real-world tasks.

To eliminate latency and power consumption, the Glasgow team’s new form of electronic skin draws inspiration from how the human peripheral nervous system interprets signals from skin. As soon as human skin receives an input, the peripheral nervous system begins processing it at the point of contact, reducing it to only the vital information before it is sent to the brain and thereby ensuring efficient use of communication channels needed to send the data to the brain, which then responds almost immediately for the body to react appropriately.

To build an electronic skin capable of a computationally efficient, synapse-like response, the researchers printed a grid of 168 synaptic transistors made from zinc-oxide nanowires directly onto the surface of a flexible plastic surface.

They then connected the synaptic transistor with the skin sensor present over the palm of a fully articulated, human-shaped robot hand. When the sensor is touched, it registers a change in its electrical resistance; a small change corresponds to a light touch, while a harder touch creates a larger change in resistance, with this input designed to mimic the way sensory neurons work in the human body.

Ravinder Dahiya (pictured), a professor at the university’s James Watt School of Engineering, said: “What we have been able to create through this process is an electronic skin capable of distributed learning at the hardware level, which doesn’t need to send messages back and forth to a central processor before taking action.

“Instead, it greatly accelerates the process of responding to touch by cutting down the amount of computation required. We believe that this is a real step forward in our work towards creating large-scale neuromorphic printed electronic skin capable of responding appropriately to stimuli.”