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.
