{"id":3620,"date":"2019-06-01T11:48:10","date_gmt":"2019-06-01T02:48:10","guid":{"rendered":"http:\/\/163.180.4.222\/lab\/?p=3620"},"modified":"2019-06-01T11:48:10","modified_gmt":"2019-06-01T02:48:10","slug":"bridging-the-gap-between-artificial-vision-and-touch","status":"publish","type":"post","link":"https:\/\/biochemistry.khu.ac.kr\/lab\/?p=3620","title":{"rendered":"Bridging the gap between artificial vision and touch"},"content":{"rendered":"

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Object manipulation using an innovative glove allows large databases of detailed pressure maps to be obtained. Such data could lead to advances in robotic sensing and in our understanding of the role of touch in manipulation.<\/h5>\n

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The study and replication of human sensory abilities, such as visual, auditory and tactile (touch-based) perception, depend on the availability of suitable data. Generally, the larger and richer the data set, the more closely models can mimic these functions. Advances in artificial visual and speech systems rely on powerful models, known as deep-learning models, and have been fuelled by the ubiquity of databases of digital images and spoken audio (see, for example,\u00a0go.nature.com\/2w7nc0q<\/a>). By contrast, progress in the development of tactile sensors \u2014 devices that convert a stimulus of physical contact into a measurable signal \u2014 has been limited, mainly because of the difficulty of integrating electronics into flexible materials1<\/a><\/sup>. In\u00a0a paper in\u00a0Nature<\/i><\/a>, Sundaram\u00a0et al.<\/i>2<\/a><\/sup>\u00a0report their use of a low-cost tactile glove that addresses this issue.<\/p>\n

The authors\u2019 glove consists of a hand-shaped sensing sleeve that is attached to the palm side of a knitted glove (Fig. 1). The sleeve contains a force-sensitive film on which is sewn a network of 64 electrically conducting threads: 32 along one direction of the glove and 32 along the perpendicular direction. Each of the 548 points at which these threads overlap is a pressure sensor, because the electrical resistance of the interleaved film decreases when these points are pressed. The output of the glove can be processed as a 32\u2009\u00d7\u200932 array of greyscale pixels, in which the colour of each pixel indicates the applied pressure from low (black) to high (white). These pressure maps are recorded at about seven frames per second.<\/p>\n

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Figure 1 | A low-cost glove for artificial touch.<\/b>\u00a0Sundaram\u00a0et al.<\/i>2<\/a><\/sup>\u00a0describe a glove that consists of a hand-shaped sensing sleeve (black) attached to a knitted glove (yellow). The sleeve contains a force-sensitive film on which a network of electrically conducting threads (silver) is sewn. The points at which these threads overlap form pressure sensors. The authors show that pressure maps collected by these sensors during object manipulation enable machine-learning models to learn to identify individual objects, estimate the weights of objects and distinguish between different hand poses.<\/span>Credit: Subramanian Sundaram<\/p>\n<\/figcaption><\/figure>\n

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In Sundaram and colleagues\u2019 study, the glove was worn to record several videos of pressure maps during 3\u20135-minute sessions of single-hand manipulation of 26 everyday objects. This procedure resulted in a database of detailed pressure maps that, to my knowledge, is one of the largest data sets of this kind. The authors found that the glove was flexible, robust and sensitive to small pressure changes, despite its fabrication cost of only about US$10.<\/p>\n

To demonstrate that the glove captures different interactions of the hand with each object, Sundaram\u00a0et al.<\/i>\u00a0used the recorded data to carry out automatic object identification. They showed how a state-of-the-art deep-learning model, which was originally designed for large-scale image classification, could learn from the gathered pressure maps to re-identify the 26 objects during blind manipulation. The large number of maps and their spatial resolution proved essential for successful object identification.<\/p>\n

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