Indian-Origin Engineer’s Team Create E-Skin That Can Feel Pain, Might Help to Develop New Generation Robots

A team of researchers led by an Indian-origin engineer in the UK has created an electronic skin capable of feeling “pain” and according to them it could help create a new generation of smart robots with human-like sensitivity. Professor Ravinder Dahiya, from the University of Glasgow’s James Watt School of Engineering, said the discovery marks a real step forward in work towards creating large-scale neuromorphic printed e-skin capable of responding appropriately to stimuli.

His team at the university developed the artificial skin with a new type of processing system based on synaptic transistors, which mimics the brain’s neural pathways in order to learn. A robot hand which uses the smart skin is said to show a remarkable ability to learn to react to external stimuli.

“We all learn early on in our lives to respond appropriately to unexpected stimuli like pain in order to prevent us from hurting ourselves again. Of course, the development of this new form of electronic skin didn’t really involve inflicting pain as we know it – it’s simply a shorthand way to explain the process of learning from external stimulus,” explained Dahiya.

“What we’ve 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,” he said.

In a new paper ‘Printed Synaptic Transistors based Electronic Skin for Robots to Feel and Learn’, published on Wednesday in the journal ‘Science Robotics’, the Scottish university researchers describe how they built their prototype computational e-skin, and how it improves on the current state of the art in touch-sensitive robotics.

The development of the electronic skin is described as the latest breakthrough in flexible, stretchable printed surfaces from the University of Glasgow’s Bendable Electronics and Sensing Technologies (BEST) Group.

Fengyuan Liu, a member of the BEST group and a co-author of the paper, added: “In the future, this research could be the basis for a more advanced electronic skin which enables robots capable of exploring and interacting with the world in new ways, or building prosthetic limbs which are capable of near-human levels of touch sensitivity.” Scientists have been working for decades to build artificial skin with touch sensitivity. One widely-explored method is spreading an array of contact or pressure sensors across the electronic skin’s surface to allow it detect when it comes into contact with an object.

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

The Glasgow University team’s new form of electronic skin draws inspiration from how the human peripheral nervous system interprets signals from skin in order to eliminate latency and power consumption.

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. That reduction of sensory data allows 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. Then, they 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, and harder touch creates a larger change in resistance. This input is designed to mimic the way sensory neurons work in the human body.