Cambridge scientists develop lightning fast braille reading robot

The robot can read braille at 315 words per minute with 90% accuracy. Credit: University of Cambridge

Scientists from the University of Cambridge have developed a robot that can read braille twice as fast as humans.

The robot, which was trained using machine learning algorithms, features a sensor which moves over lines of braille text, much in the same way that human fingers do. Able to read braille at 315 words a minute with 90% accuracy, the robot can read at double the average speed of human braille readers.

However, the primary goal behind the robot’s creation was not to help visually impaired individuals read, rather to pioneer advancements in robotic sensitivity. Indeed, the high sensitivity required to read braille makes it an excellent test of just how sensitive a robot’s ‘touch’ can be. This makes the research potentially important in the future development of robotic hands and prosthetics.

“The softness of human fingertips is one of the reasons we’re able to grip things with the right amount of pressure,” said Parth Potdar from Cambridge’s Department of Engineering and an undergraduate at Pembroke College, the paper’s first author. “For robotics, softness is a useful characteristic, but you also need lots of sensor information, and it’s tricky to have both at once, especially when dealing with flexible or deformable surfaces.”

Co-author, David Hardman, also from the Department of Engineering, added: “There are existing robotic braille readers, but they only read one letter at a time, which is not how humans read.

“Existing robotic braille readers work in a static way: they touch one letter pattern, read it, pull up from the surface, move over, lower onto the next letter pattern, and so on. We want something that’s more realistic and far more efficient.”

In future, the researchers are hoping to scale the technology to the size of a humanoid hand or skin. The research was supported in part by the Samsung Global Research Outreach Program.

The results are reported in the journal IEEE Robotics and Automation Letters.

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