Robot dog learns to walk on tough terrain in just 20 minutes

A robot dog can learn to walk on unfamiliar and hard-to-master terrain, such as grass, bark and hiking trails, in just 20 minutes, thanks to a machine learning algorithm. Most autonomous robots have to be carefully programmed by humans or extensively tested in simulated scenarios before they can perform real-world tasks, such as walking up a rocky hill or a slippery slope – and when they encounter unfamiliar environments, they tend to struggle. Now, Sergey Levine at the University of California, Berkeley, and his colleagues have demonstrated that a robot using a kind of machine learning called deep reinforcement learning can work out how to walk in about 20 minutes in several different environments, such as a grass lawn, a layer of bark, a memory foam mattress and a hiking trail. The robot uses an algorithm called Q-learning, which doesn't require a working model of the target terrain. Such machine learning algorithms are usually used in simulations.