This Factory Robot Learns a New Job Overnight

MIT Technology Review:  Fanuc’s robot uses a technique known as deep reinforcement learning to train itself, over time, how to learn a new task. It tries picking up objects while capturing video footage of the process. Each time it succeeds or fails, it remembers how the object looked, knowledge that is used to refine a deep learning model, or a large neural network, that controls its action. Deep learning has proved to be a powerful approach in pattern recognition over the past few years.

“After eight hours or so it gets to 90 percent accuracy or above, which is almost the same as if an expert were to program it,” explains Shohei Hido, chief research officer at Preferred Networks, a Tokyo-based company specializing in machine learning. “It works overnight; the next morning it is tuned.”... (full story)

Comments (0)

This post does not have any comments. Be the first to leave a comment below.


Post A Comment

You must be logged in before you can post a comment. Login now.

Featured Product

US Digital - E4T Miniature Optical Kit Encoder

US Digital - E4T Miniature Optical Kit Encoder

US Digital is pleased to announce the launch of the E4T, their latest series of miniature high performance optical encoders. The E4T series delivers a marked performance increase over similar encoder models and designed to be an enhanced replacement for the E4P encoder series. The E4T utilizes state of the art transmissive optical sensing technology, and incorporates US Digital's own proprietary OptoASIC. Assembly of the E4T is simple and efficient and retains the previous E4P's form factor. Key features of the new E4T include: • Mechanically and Electrically Interchangeable with E4P • Improved Quadrature Signal Strength • 100 kHz Frequency Response • Transmissive Optical Design • Collet Style Push on Optical Disk Design (Patent Pending) • Simple & Efficient Assembly Process As with all of our products the E4T is designed and manufactured in their Vancouver, Washington USA facility and is available for purchase as of December 2014.