May 27, 2017 @ 15:47 |
New research in University of California at Berkeley by Professor Ken Goldberg, Jeff Mahler, and the Laboratory for Automation Science and Engineering (AUTOLAB) created the Nimble- Fingered Robot, that shows real progress on the problem of robots grasping everyday objects called DexNet 2.0.
Grabbing the awkwardly shaped items that people pick up in their day-to-day lives is a slippery task for robots. Irregularly shaped items such as shoes, spray bottles, open boxes, even rubber duckies are easy for people to grab and pick up, but robots struggle with knowing where to apply a grip. In a significant step toward overcoming this problem, roboticists at UC Berkeley have a built a robot that can pick up and move unfamiliar, real-world objects with a 99 percent success rate.
DexNet 2.0’s high grasping success rate means that this technology could soon be applied in industry, with the potential to revolutionize manufacturing and the supply chain. DexNet 2.0 used deep learning with a cloud database of thousands of 3D objects to collect 6.7 million data points in order to train a robot to pick up and move objects in the real-world with a 99% success rate — significantly higher than previous methods.
DexNet 2.0 gained its highly accurate dexterity through a process called deep learning. The researchers built a vast database of three-dimensional shapes — 6.7 million data points in total — that a neural network uses to learn grasps that will pick up and move objects with irregular shapes. The neural network was then connected to a 3D sensor and a robotic arm. When an object is placed in front of DexNet 2.0, it quickly studies the shape and selects a grasp that will successfully pick up and move the object 99 percent of the time. DexNet 2.0 is also three times faster than its previous version.
- Keywords: UCB, AUTOLAB, Nimble-Fingered Robot,research,DexNet 2.0,Grabbing, robot,robotic arm,3D objects.
- Source: University of California at Berkeley
- Image: University of California at Berkeley