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Tiny Robot from USA Breaks Speed Record with AI

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The United States’ National Science Foundation backs the researcher from the Massachusetts Institutes of Technology (MIT) who made the robotic mini-cheetah to run rapidly through AI and machine learning. The robot cheetah broke the record for the quickest run by adapting to terrain variations through simulation.

The scientists trained the robot cheetah using a “learn by experience” technique. Humans have created robots that can walk, lift, and jump, but quick and efficient running is not one of them. Until now, that is. Running necessitates robots to respond quickly to changes in the environment and terrain.

The team taught the robot cheetah how to adapt to changes in its environment while in motion using the learn by experience paradigm, artificial intelligence, and machine learning. Using simulated scenarios, the robot can quickly experience and learn from varied terrains.

According to the researchers, manually training robots to adapt is a time-consuming, labour-intensive, and tiresome process. The experts believe that teaching robots to teach themselves could solve the scalability problem and allow robots to develop a broader set of abilities and tasks. They have now begun to apply their method to a broader range of robotic systems.

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 along with the Institute of AI and Fundamental Interactions (IAIFI) have been working together. Meanwhile, MIT PhD student Gabriel Margolis and IAIFI postdoc Ge Yang demonstrated the cheetah’s speed.

Fast running necessitates pushing the hardware to its limitations, such as operating near the maximum torque output of motors. In such cases, the robot dynamics are difficult to represent analytically. The robot must react swiftly to changes in the environment, such as when it comes into contact with ice while running on grass.

When the robot walks, it moves slowly, and the presence of snow is usually not an issue. Consider how you could negotiate practically any terrain if you walked slowly but carefully. Today’s robots confront a similar dilemma. The issue is that travelling across all terrains as if walking on ice is wasteful, but it is widespread among today’s robots. Humans adapt by running swiftly on grass and slowing down on ice.

Giving robots similar adaptability necessitates rapid detection of terrain changes and rapid adaptation to prevent the robot from falling over. In general, high-speed running is more difficult than walking because it is hard to create analytical (human-designed) models of all potential terrains in advance, and the robot’s dynamics become more complex at high speeds.

Programming a robot’s actions is tough, according to researchers. Human engineers must manually alter the robot’s controller if it fails on a particular terrain. However, humans no longer need to programme robots’ every move if the robot can explore many terrains and improve with practice.

Researchers added that the modern simulation tools allow the robot to gain 100 days of experience in just three hours. They also developed a method by which a robot’s behaviour improves from simulated experience and is successfully deployed in the actual world. The robot’s running talents function in the actual world because some of the simulator’s environments teach it real-world skills. The controller finds and executes essential talents in real-time.

Artificial intelligence research balances what humans must develop with what machines can learn on their own. Humans tell robots what to do and how to accomplish it. Such a system isn’t scalable because it would take significant human engineering effort to manually design a robot to operate in numerous contexts.

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