Robot trained to read braille at twice the speed of humans

训练机器人以两倍于人类的速度阅读盲文

Researchers have developed a robotic sensor that incorporates artificial intelligence techniques to read braille at speeds roughly double that of most human readers.

 研究人员开发了一种机器人传感器，利用人工智能技术以大约两倍于大多数人类读者的速度阅读盲文。

The research team, from the University of Cambridge, used machine learning algorithms to teach a robotic sensor to quickly slide over lines of braille text.

 剑桥大学的研究团队使用机器学习算法教授一个机器人传感器快速滑过盲文文本的行。

The robot was able to read the braille at 315 words per minute at close to 90% accuracy.

 这个机器人能够以每分钟315个单词的速度读取盲文，准确率接近90%。

Although the robot braille reader was not developed as an assistive technology, the researchers say the high sensitivity required to read braille makes it an ideal test in the development of robot hands or prosthetics with comparable sensitivity to human fingertips.

 尽管机器人盲文阅读器并非作为辅助技术开发的，但研究人员表示，阅读盲文所需的高灵敏度使其成为开发具有与人类指尖相当灵敏度的机器人手或假肢的理想测试。

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

 这些结果发表在《IEEE机器人与自动化信函》期刊上。

Human fingertips are remarkably sensitive and help us gather information about the world around us. Our fingertips can detect tiny changes in the texture of a material or help us know how much force to use when grasping an object: for example, picking up an egg without breaking it or a bowling ball without dropping it.

 人类指尖具有非常高的敏感性，帮助我们收集周围世界的信息。我们的指尖可以探测材料纹理的微小变化，或者在抓取物体时帮助我们知道要使用多大的力量：例如，不破坏鸡蛋地捡起它，或者不掉落保龄球。

Reproducing that level of sensitivity in a robotic hand, in an energy-efficient way, is a big engineering challenge.

 以一种节能的方式在机器人手中复制这种敏感度是一个巨大的工程挑战。

In Professor Fumiya Iida's lab in Cambridge's Department of Engineering, researchers are developing solutions to this and other skills that humans find easy, but robots find difficult.

 在剑桥大学工程系的飯田史矢教授的实验室中，研究人员正在开发解决这个问题以及其他人类认为容易的技能，但机器人认为困难的解决方案。

"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.

剑桥大学工程系和彭布罗克学院本科生帕思·波特达尔是本文的第一作者，他说：“人类指尖的柔软性是我们能够以正确的压力抓取物体的原因之一。”

"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."

 “对于机器人而言，柔软性是一个有用的特性，但你还需要大量的传感器信息，同时将两者结合起来是有难度的，特别是在处理柔软或可变形的表面时。”

Braille is an ideal test for a robot 'fingertip' as reading it requires high sensitivity, since the dots in each representative letter pattern are so close together.

 盲文是机器人“指尖”的理想测试对象，因为阅读盲文需要高灵敏度，由于每个代表性字母图案中的点非常接近。

The researchers used an off-the-shelf sensor to develop a robotic braille reader that more accurately replicates human reading behaviour.

 研究人员使用现成的传感器开发了一种更准确地复制人类阅读行为的机器人盲文阅读器。

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

 工程系的合著者大卫·哈德曼说：“现有的机器人盲文阅读器只能一次读取一个字母，这与人类的阅读方式不同。”

"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."

“现有的机器人盲文阅读器以静态方式工作：它们接触一个字母图案，读取它，从表面抬起，移动过去，降低到下一个字母图案上，以此类推。我们希望有一种更真实和更高效的方式。”

The robotic sensor the researchers used has a camera in its 'fingertip', and reads by using a combination of the information from the camera and the sensors.

 研究人员使用的机器人传感器在其“指尖”上配有摄像头，并通过利用摄像头和传感器的信息相结合来进行阅读。

"This is a hard problem for roboticists as there's a lot of image processing that needs to be done to remove motion blur, which is time and energy-consuming," said Potdar.

 波特达尔说：“对于机器人学家来说，这是一个难题，因为需要进行大量的图像处理来消除运动模糊，这需要时间和能量。”

The team developed machine learning algorithms so the robotic reader would be able to 'deblur' the images before the sensor attempted to recognise the letters.

 团队开发了机器学习算法，使机器人阅读器能够在传感器尝试识别字母之前对图像进行“去模糊”处理。

They trained the algorithm on a set of sharp images of braille with fake blur applied.

 他们训练了算法，使用带有人为模糊的清晰盲文图像集。

After the algorithm had learned to deblur the letters, they used a computer vision model to detect and classify each character.

 在算法学会去模糊字母之后，他们使用计算机视觉模型来检测和分类每个字符。

Once the algorithms were incorporated, the researchers tested their reader by sliding it quickly along rows of braille characters.

 一旦算法被整合进去，研究人员通过快速滑动机器人阅读器沿盲文字符的行进行了测试。

The robotic braille reader could read at 315 words per minute at 87% accuracy, which is twice as fast and about as accurate as a human Braille reader.

 机器人盲文阅读器的阅读速度为每分钟315个单词，准确率达到了87%，这是人类盲文读者的两倍速度，准确率大致相当。

"Considering that we used fake blur the train the algorithm, it was surprising how accurate it was at reading braille," said Hardman.

 “考虑到我们使用虚拟模糊来训练算法，它在阅读盲文时的准确性令人惊讶。”哈德曼说道。

"We found a nice trade-off between speed and accuracy, which is also the case with human readers."

 “我们发现了速度和准确性之间的良好平衡，这也适用于人类读者。”

"Braille reading speed is a great way to measure the dynamic performance of tactile sensing systems, so our findings could be applicable beyond braille, for applications like detecting surface textures or slippage in robotic manipulation," said Potdar.

“盲文阅读速度是衡量触觉感知系统动态性能的一个很好的方式，因此我们的研究结果可能适用于盲文之外的应用，如检测表面纹理或机器人操纵中的滑动。”波特达尔说道。

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.

 未来，研究人员希望将这项技术扩展到类似人类手或皮肤的尺寸。该研究部分得到了三星全球研究联合计划的支持。