jan_jes writes: UC Berkeley researchers turned to a new branch of artificial intelligence known as deep learning, which they have developed algorithms that enable robots to learn motor tasks through trial and error using a process that more closely approximates the way humans learn, marking a major milestone in the field of artificial intelligence. 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, like homes or offices, is that those environments are constantly changing. The robot must be able to perceive and adapt to its surroundings. This latest developments will be presented on Thursday, May 28, in Seattle at the International Conference on Robotics and Automation (ICRA).