This robot dog just taught itself to walk

The team's algorithm, called Dreamer, uses past experiences to build up a model of the surrounding world. Dreamer also allows the robot to conduct trial-and-error calculations in a computer program as opposed to the real world, by predicting potential future outcomes of its potential actions. This allows it to learn faster than it could purely by doing. Once the robot had learned to walk, it kept learning to adapt to unexpected situations, such as resisting being toppled by a stick. "Teaching robots through trial and error is a difficult problem, made even harder by the long training times such teaching requires," says Lerrel Pinto, an assistant professor of computer science at New York University, who specializes in robotics and machine learning.