Reinforcement learning has helped a four-legged bot move a bit like a real animal, without having to be taught how to make each step.

The news: Roboticists want their creations to mimic animals because animals invariably move in the most energy-efficient way. But the eerily lifelike movement of robots like Boston Dynamics’ Spotmini is usually coded by hand. Now researchers have combined simulation with a technique called reinforcement learning to teach a dog-like robot called “ANYmal” to run faster and recover from falls. Crucially, it did so without any manual intervention. 



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Train at super-speed: The simulation let the team run training sessions 1,000 faster than real time on more than 2,000 ANYmals simultaneously. After the simulated training was transferred to a real robot, it was able to exceed its previous top speed by 25% and flip over after falls, the EFH Zurich team explains in a new paper published in Science Robotics today. It’s still pretty limited (as you’ll see above), but it’s a step (ho ho) in the right direction.

Uses: Besides keeping Jeff Bezos company? To be honest, there aren’t that many compelling practical uses at the moment, but the researchers say these sorts of four-legged robots could one day be used to inspect underground tunnels or carry heavy loads on construction sites.

50 years after Apollo 11, space technologies have radically changed life on Earth–and we’re still just at the beginning.

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