Sensor-packed glove learns signatures of the human grasp

ScienceDaily | 5/29/2019 | Staff
urbanagirl3 (Posted by) Level 3
The researchers developed a low-cost knitted glove, called "scalable tactile glove" (STAG), equipped with about 550 tiny sensors across nearly the entire hand. Each sensor captures pressure signals as humans interact with objects in various ways. A neural network processes the signals to "learn" a dataset of pressure-signal patterns related to specific objects. Then, the system uses that dataset to classify the objects and predict their weights by feel alone, with no visual input needed.

In a paper published in Nature, the researchers describe a dataset they compiled using STAG for 26 common objects -- including a soda can, scissors, tennis ball, spoon, pen, and mug. Using the dataset, the system predicted the objects' identities with up to 76 percent accuracy. The system can also predict the correct weights of most objects within about 60 grams.

Gloves - Today - Thousands - Dollars - Sensors

Similar sensor-based gloves used today run thousands of dollars and often contain only around 50 sensors that capture less information. Even though STAG produces very high-resolution data, it's made from commercially available materials totaling around $10.

The tactile sensing system could be used in combination with traditional computer vision and image-based datasets to give robots a more human-like understanding of interacting with objects.

Humans - Objects - Feedback - Objects - Robots

"Humans can identify and handle objects well because we have tactile feedback. As we touch objects, we feel around and realize what they are. Robots don't have that rich feedback," says Subramanian Sundaram PhD '18, a former graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). "We've always wanted robots to do what humans can do, like doing the dishes or other chores. If you want robots to do these things, they must be able to manipulate objects really well."

The researchers also used the dataset to measure the cooperation between regions of the hand during object interactions. For example, when someone uses the...
(Excerpt) Read more at: ScienceDaily
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