Amid trial and error Google robots are trying to pick up things
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Amid trial and error Google robots are trying to pick up things



Amid trial and error Google robots are trying to pick up things
Google is teaching its robots a simple task amid trial and error, picking up objects in one bin and placing them in another. They’re not the first robot to pick something up, but these robots are actually learning new ways to pick up objects of different shapes, sizes, and characteristics based on constant feedback. For instance, they’ve robot learned to pick up a soft object differently than a hard object.
Other projects, like Cornell’s Deep Grasping paper, analyse an object once for the best place to grasp, attempt to pick it up, and then try again if it failed. Google’s approach continuously analyses the object and the robot hand’s relation to it, making it more adaptable, like a human.
Google robots are really just arms with brains, hooked up to a camera. They have two grasping fingers attached to a triple-joined arm, which are controlled by two deep neural networks. Deep neural networks are a popular flavour of artificial intelligence, because of their aptitude in being able to make predictions based on large amounts of data. In this case, one neural network is simply looking at photos of the bin and predicts whether the robot’s hand can correctly grasp the object. The other interprets how well the hand is grabbing, so it can inform the first network to make adjustments.
Researchers noted that the robots didn’t need to be calibrated based on different camera placement. As long as the camera had a clear view of the bin and arm, the neural network would be able to adjust and continue learning to pick up objects.
Over the course of two months, Google robots pick up objects 800,000 times. Six to 14 robots were working on picking up objects at any given time, and the only human role was to reload the robot’s bin of objects. The objects were ordinary household objects: office supplies, children’s toys, and sponges.

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