Dex-Net’s Robotic Arm Uses Deep Learning to Pick up New and Different Objects

Published by , May 29, 2017 12:30 pm

(TechCrunch) Dex-Net uses deep learning to let a robotic arm improvise an effective grip for objects it’s never seen before.
Dex-Net has more than six million artificial 3D representations of objects and how to work out the best way, theoretically, to pick up each. In real life, the system looks at an object, compares its point cloud to those in its memory and picks what it thinks is the closest fit. The researchers presented Dex-Net with dozens of objects it hadn’t seen before, and its chosen grip only failed one time. That suggests the system is fairly robust despite being trained on synthetic data — plus, it comes up with its candidate grip in an average of less than a second.

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