Today, the robots of the most bizarre forms are very difficult to surprise. However, a group of scientists from Japan seems to have succeeded. They built strange robots from sticks and … other scrap materials. Moreover, thanks to a very advanced system of artificial intelligence, built on the basis of neural networks, the robot learned to walk. And strange as it may sound, this approach has a very promising future.
How are robots created
Usually, when designing robots, scientists need to have a very clear idea of what the robot should do, how it will move, and what actions it will need to perform. In this case, you first need to build a prototype of the future device in order to ensure that all their calculations were correct and to identify design flaws. But the development process does not end there. After forming an understanding of how the model will function, it will be necessary to “teach” it to those actions that it must perform, be it as a manipulator, walking or moving in other ways.
Branch robots
But, as scientists from the University of Tokyo and Preferred Networks say, the process of creating robotics doesn’t have to be so complicated. Back in December last year, they began experiments on the creation of robots based on servo drives and available materials (for example, tree branches).
At the same time, even before creating a model, robots learn how to move in a special computer simulation, where an advanced neural network teaches them to walk based on deep machine learning. In order to upload model data to a neural network, scientists simply take several branches of a suitable size and the body of a robot. All this is subject to 3D scanning. The resulting models appear in a special computer program that “collects” them together and begins the learning process.
During these “lessons”, a model of future robot behavior is set. The neural network is “rewarded” by the future mechanism for the correct movements, and it “punishes” for the incorrect ones. In addition, there is the possibility of manual adjustment of movements, as well as a system that provokes the occurrence of random events during training so that the robot is fully prepared for what awaits it in the real world. When training ends, it remains only to assemble the model and load the behavior algorithm into it.
Why create such robots?
In fact, the potential of such a system is enormous. In fact, all you need is a “computer and a pair of motors.” The rest of the details can be collected right under your feet. Such robots can be used for reconnaissance in regions where a person cannot reach, and sending full-fledged mechanisms is too expensive. In addition, these robots can be collected, for example, during the colonization of other planets. You can, for example, send an assembly module with a stock of microprocessors and servos, and other details can be assembled upon arrival at the mission site.
