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.” And this brandless bound is what makes it fast.
A team of researchers from the Massachusetts Institute of Technology (MIT) created a computer algorithm that spurs this artificially intelligent robot to maximize its speed, thereby breaking its own sprint records. In several demonstrations, Mini Cheetah can still go turbo as it spins in a circle or darts across ice, loose gravel, and inclines.
“What we are interested in is, given the robotic hardware, how fast can [a robot] go?” says Pulkit Agrawal, an AI researcher at MIT and the leader of the Improbable AI Lab that conceived the project. “We didn’t want to constrain the robot in arbitrary ways.”
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. (But perhaps, just not elegance.)
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. Schooled on this dataset and free of any programming constraints, Mini Cheetah is able to create its own signature sprinting style that humans wouldn’t have been able to conceive of. (This may explain why its movements look quite unnatural to us.) Furthermore, it could modify how it loped in real time to adapt to the conditions of its route.
Thanks to this machine learning software, Mini Cheetah bumped its peak indoor velocity from 12 feet per second before training to 13 feet per second after. It more or less maintained this speed outdoors when tearing up unfamiliar grounds. It could catch itself when it tripped.