Replicating Cheetah Speed to Robots Shorts #Shorts

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It’s been roughly 23 years since one of the first robotic animals trotted on the scene, defying classical notions of our cuddly four-legged friends. Since then, a barrage of the walking, dancing, and door-opening machines have commanded their presence, a sleek mixture of batteries, sensors, metal, and motors. Missing from the list of cardio activities was one both loved and loathed by humans (depending on whom you ask), and which proved slightly trickier for the bots: learning to run.

Researchers from MIT’s Improbable AI Lab, part of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and directed by MIT Assistant Professor Pulkit Agrawal, as well as the Institute of AI and Fundamental Interactions (IAIFI) have been working on fast-paced strides for a robotic mini cheetah — and their model-free reinforcement learning system broke the record for the fastest run recorded. Here, MIT PhD student Gabriel Margolis and IAIFI postdoc Ge Yang discuss just how fast the cheetah can run.

Q: We’ve seen videos of robots running before. Why is running harder than walking?

A: Achieving fast running requires pushing the hardware to its limits, for example by operating near the maximum torque output of motors. In such conditions, the robot dynamics are hard to analytically model. The robot needs to respond quickly to changes in the environment, such as the moment it encounters ice while running on grass. If the robot is walking, it is moving slowly and the presence of snow is not typically an issue. Imagine if you were walking slowly, but carefully: you can traverse almost any terrain. Today’s robots face an analogous problem. The problem is that moving on all terrains as if you were walking on ice is very inefficient, but is common among today’s robots. Humans run fast on grass and slow down on ice — we adapt. Giving robots a similar capability to adapt requires quick identification of terrain changes and quickly adapting to prevent the robot from falling over. In summary, because it’s impractical to build analytical (human-designed) models of all possible terrains in advance, and the robot's dynamics become more complex at high-velocities, high-speed running is more challenging than walking.

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The MIT mini cheetah learns to run faster than ever, using a learning pipeline that’s entirely trial and error in simulation.
Q: Previous agile running controllers for the MIT Cheetah 3 and mini cheetah, as well as for Boston Dynamics’ robots, are “analytically designed,” relying on human engineers to analyze the physics of locomotion, formulate efficient abstractions, and implement a specialized hierarchy of controllers to make the robot balance and run. You use a “learn-by-experience model” for running instead of programming it. Why?

A: Programming how a robot should act in every possible situation is simply very hard. The process is tedious, because if a robot were to fail on a particular terrain, a human engineer would need to identify the cause of failure and manually adapt the robot controller, and this process can require substantial human time. Learning by trial and error removes the need for a human to specify precisely how the robot should behave in every situation. This would work if: (1) the robot can experience an extremely wide range of terrains; and (2) the robot can automatically improve its behavior with experience.



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