For the grinding machine producer Strausak, the robot manufacturer Stäubli and the encoder supplier HEIDENHAIN, the insertion of cylindrical tools into tool holders with a robot arm was a real challenge-and therefore a task made to measure.
Improving the effectiveness of small and medium manufacturers could help stimulate the economy and drive job creation. Adding robotic employees to the manufacturing mix might just make manufacturers in the United States more competitive with their counterparts in Asia.
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 )
What do ants and Industry 4.0 have in common? What challenges faced the engineers when it came to developing these delicate technology platforms? Take a look behind the scenes and dive into the world of the Bionic Learning Network... ( cont'd )
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