Using your hand to grasp a pen that’s lying on your desk doesn’t exactly feel like a chore, but for robots, that’s still a really hard thing to do. So to teach robots how to better grasp random objects, Google’s research team dedicated 14 robots to the task . The standard way to solve this problem would be for the robot to survey the environment, create a plan for how to grasp the object, then execute on it. In the real world, though, lots of things can change between formulating that plan and executing on it. Google is now using these robots to train a deep convolutional neural network (a technique that’s all the rage in machine learning right now) to help its robots predict the outcome of their grasps based on the camera input and motor commands. It’s basically hand-eye coordination for robots. The team says that it took about 3,000 hours of practice (and 800,000 grasp attempts) before it saw “the beginnings of intelligent reacti...
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