A robot has run the fastest 100 metres for a bipedal machine, setting a Guinness World Record. Cassie the robot reached the finish line in just under 25 seconds. The robot has knees that bend like those of an ostrich and no external sensors or cameras. It was trained for the equivalent of a full year in a simulated environment.
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. “We don’t need to understand how the physics of an environment actually works, we just put the robot into an environment and turn it on,” says Levine.
Instead, the robot receives a certain reward for each action it performs, depending on how successful it was according to predefined goals. It repeats this process continuously while comparing its previous successes until it learns to walk.