The world’s largest industrial robot maker, Fanuc, is developing robots that use reinforcement learning to figure out how to do things.
Give the robot a task, like picking widgets out of one box and putting them into another container, and it will spend the night figuring out how to do it. Come morning, the machine should have mastered the job as well as if it had been programmed by an expert.
Fanuc worked with Preferred Networks, a Tokyo-based company specializing in machine learning. 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.
Read more by Will Knight, MIT Technology Review, March 2016