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.

But while a foal or a giraffe needs much longer to master walking, the robot learns to move forward fluently in just one hour. A computer program acts as the artificial presentation of the animal's spinal cord, and learns to optimize the robot's movement in a short time. The artificial neural network is not yet ideally adjusted at the beginning, but rapidly self-adjusts. On July 18, the research work which is at the intersection of robotics and biology was published in the renowned journal Nature Machine Intelligence.

Study hard enough, kids, and maybe one day you'll grow up to be a professional robot fighter. A few years ago, Boston Dynamics set the standard for the field by having people wielding hockey sticks try to keep Spot the quadrupedal robot from opening a door. Previously, in 2015, the far-out federal research agency Darpa hosted a challenge in which it forced clumsy humanoid robots to embarrass themselves on an obstacle course way outside the machines' league. And now, behold: The makers of the Jueying robot dog have taught it a fascinating way to fend off a human antagonizer who kicks it over or pushes it with a stick. A team of researchers from China's Zhejiang University--where the Jueying's hardware was also developed--and the University of Edinburgh didn't teach the Jueying how to recover after an assault, so much as they let the robot figure it out.