These Self-Learning Robots Can Figure Out How to Move Forward, and Do It Fast 16 May 2021, 7:05 UTC ·
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Some robots look complex or even threatening, but what most of them have in common is that they are programmed to perform specific actions. Well, what if a robot were able to learn as it goes and modify its actions accordingly? This is what researchers at AMOFL Institute, in the Netherlands, wanted to test.
The result is a self-learning system comprised of several small and simple robots, which manages to teach itself how to move similarly to a train, on a circular trajectory. In order to get there, the first step was to choose very simple individual robots. According to Bas Overvelde, Principal Investigator of the Soft Robotic Matter group at AMOLF, it was important to demonstrate that a robot can learn to adapt to the environment even without complex models.
Credit: Soft Robotic Matter Group, AMOLF
Researchers from AMOLF s Soft Robotic Matter group have shown that a group of small autonomous, self-learning robots can adapt easily to changing circumstances. They connected these simple robots in a line, after which each individual robot taught itself to move forward as quickly as possible. The results were published today in the scientific journal
PNAS.
Robots are ingenious devices that can do an awful lot. There are robots that can dance and walk up and down stairs, and swarms of drones that can independently fly in a formation, just to name a few. However, all of those robots are programmed to a considerable extent - different situations or patterns have been planted in their brain in advance, they are centrally controlled, or a complex computer network teaches them behavior through machine learning. Bas Overvelde, Principal Investigator of the Soft Robotic Matter group at AMOLF, wanted to go back to the basics: a self-learning robo