A team from MIT taught their Mini Cheetah to run. For this, it took a good dose of artificial intelligence and machine learning. In three hours, the robot has learned one hundred days of virtual routes to then adapt its speed of movement according to the ground and obstacles. Result, a speed record.
Themovement is one of the big criticisms made of robots. Whether it’s a humanoid, or even recently the mechanized structures and its weight prevent the robot from moving quickly. Moreover, as the machine does not know the possible obstacles that it will encounter, the robot is always in apprehension and analysis before taking a step. Another major obstacle: the cost of materials. Given the price of a robot, researchers avoid pushing it to its limits.
Except that the robotics researchers of the (MIT) came up with the idea of using simulations powered by Artificial Intelligence (AI) to quickly teach the robot to adapt its walking as needed. In this case, it was their robot-panther to whom they wanted to learn to adapt its approach according to the terrain. Until now, they had taught him to run fast on a or perform somersaults. There, it is a question of teaching him to run in a natural environment.
In just three hours, the robot, known as, has stored 100 days of virtual adventures on a wide variety of terrains. Only the “animal” has learned techniques to modify its gait according to the terrain and the always with the same objective: to get from point A to point B. As we can see in the video below, it’s bound to be fun.
So that thelearns on its own, the researchers opted for reinforcement learning ( ). This type of machine learning works with a system of rewards or penalties, or rather trial and error. The robot is unable to recognize the type of ground, whether dry or wet, smooth or irregular, but depending on the feeling of a step, it is able to adapt its stride and its . This is all the more effective when he makes several passes in the same place, and he can then move even faster.
A new method allows MIT’s Mini Cheetah to learn to run fast and adapt its gait over difficult terrain. © YouTube, MIT
A very good look
As a result, the panther reached a speed of 3.9 m/s, or 14 km/h. It’s faster than a normal human since, for a runner, it represents 4 min 15 sec per kilometer. You already have to be a very good runner to hold such a pace. This is a record for a robot in an autonomous way, and the next step will now be to go further in behavioral autonomy, whether it is grasping objects or learning to move on unfamiliar ground. , without training with an AI.
” At the heart of AI research is the trade-off between what humans need to integrate (nature) and what the machine can learn on its own (build), . The traditional robotics paradigm is that humans tell the robot both what task to do and how to do it. The problem is that such a framework is not scalable, as it would take an immense human engineering effort to manually program a robot with the skills to operate in many diverse environments. A more practical way to build a robot with many diverse skills is to tell the robot what to do and let it figure out how. Our system is an example.
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This robot taught itself to run fast
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