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New 'Deep Learning' Technique Lets Robots Learn Through Trial-and-Error
jan_jes writes: UC Berkeley researchers turned to a branch of artificial intelligence known as deep learning for developing algorithms that enable robots to learn motor tasks through trial and error. It's a process that more closely approximates the way humans learn, marking a major milestone in the field of artificial intelligence. Their demonstration robot completes tasks such as "putting a clothes hanger on a rack, assembling a toy plane, screwing a cap on a water bottle, and more" without pre-programmed details about its surroundings. The challenge of putting robots into real-life settings (e.g. homes or offices) is that those environments are constantly changing. The robot must be able to perceive and adapt to its surroundings, so this type of learning is an important step.
Hmm... sorry, this has been happening for many years already... back to the 80s... This is nothing new.
No, it was not happening in the 1980s. The fundamental algorithm behind deep learning networks was worked out by Geoffrey Hinton in 2006. Before that, training a NN more than 2 layers deep was intractable.