DexNet

Dex-net is a robotic. It uses a Grasp Quality Convolutional Neural Network to learn how to grasp unusually shaped objects.

History

Dex-net was developed by University of California, Berkeley professor Ken Goldberg and graduate student Jeff Mahler.

Design

Dex-net includes a high-resolution 3-D sensor and two arms, each controlled by a different neural network. One arm is equipped with a conventional robot gripper and another with a suction system. The robot’s software scans an object and then asks both neural networks to decide, on the fly, whether to grab or suck a particular object. It runs on an off-the-shelf industrial machine made by Swiss robotics company ABB.

The software learns by attempting to pick up objects in a virtual environment. Dex-Net can generalize from an object it has seen before to a new one. The robot can “nudge” such virtual objects to examine if it is unsure how to grasp it. The trial data set was 6.7 million point clouds, grasps and analytic grasp metrics generated from thousands of 3D models. Grasps are defined as a gripper’s planar position, angle and depth

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Source: Wikipedia