Machine learning helped MIT's cheetah robot break its own speed record

Mini Cheetah, the fastest robot of its build, poses with its lead AI researcher Gabriel Margolis and a friend's dog, Olive.Horses gallop. Kangaroos hop. Ducks waddle. Elephants amble. The fleet-footed quadruped robot called Mini Cheetah… well, doesn’t move like anything in the animal kingdom. A cross between a scramble and a scamper, its gait is desperately chaotic and comically ungraceful. In fact, its particular style is dubbed “gait-free.

Previous top robot runners were only speedy in limited scenarios. They performed best on an indoor treadmill, but suffered when navigating uneven terrains in the real world. Conversely, robots that could cross any kind of topography were generally sluggish across the board, because they weren’t optimized for speed, their responses were challenging to program. Mini Cheetah has the best of both worlds. .

The MIT researchers’ workaround was to use reinforcement learning, a goal-driven form of machine learning, to help a robot like Mini Cheetah figure out how to reach its top speed on its own. First, the team simulated all the potential scenarios of the real-world in a computer. Then they trained Mini Cheetah’s software on these virtual simulations before its deployment.

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