Evolutionary Robotics

If we can design robots that are smart and capable enough, could we kickstart the robotic evolution, where robots design the next generation of robots?



Evolutionary robotics may sound like science fiction, but it is not a novel idea. Alan Turing proposed in the 1950s that the building of intelligent machines would be too difficult for human designers and that incorporating "mutations" and selective reproduction into the process may be a superior technique. Of fact, while the concept of evolutionary robotics has been around for a long time, the means required to put it into reality have only just been available.


For the first time in modern history, we have all of the building elements required to support evolutionary robotics: quick prototyping and physical reproduction via 3D printing, neural networks for learning and training, enhanced battery life and lower-cost materials, and much more. However, a robot evolution process can not come from humans writing the evolutionary code. Evolution by design is an unpredictable, chaotic process of trial and error. so to achieve actual evolutionary robotics, we will have to think about the whole process in an entirely different way.


You see, The phrase "evolutionary robotics" is a little deceptive because it actually refers to duplicating biological evolution processes to non-organic systems. "Artificial evolution" or "embodied evolution" could be better descriptors. It is not so much the robots that are changing as it is the processes themselves that are evolving.


The same method might be used for any creature that can be outfitted with a neural network and evolutionary algorithms to develop "offspring" from two or more parents through mutation and subsequent recombination. In reality, evolution does not even require a physical form; these same processes may be used to solve important issues within supercomputers.


Only evolutionary robotics can produce robots capable of sophisticated, autonomous real-world interaction. The advantages of such robots are too many to name, but possible applications include robotic firemen and search-and-rescue robots, as well as nuclear waste cleanup robots, home care robots, and others.


We may also improve our understanding of organic evolution. It's difficult to see how a more sophisticated understanding of evolution might have such far-reaching implications. We might obtain tremendous insights into the best ways to cure diseases and build immunities, extend our life spans, reduce our environmental footprint, and acquire a better understanding of our future on this planet.


Using artificial intelligence to enhance a design by repeatedly replicating it and making a little adjustment each time (iterative design) is not a new idea, but it has previously only been used in computer simulations. By simulating a collection of reproducible lifeforms, you may imitate a process akin to natural selection in genuine biological evolution. The most successful creatures are more inclined to replicate and propagate their own unique design. So, in a few generations, you'll have an optimized version of the lifeform that a human designer would not have discovered on their own.


Natural selection and evolution computer simulations have a number of advantages. The computer's speed is the sole theoretical restriction to the number of generations and how quickly they are created. Models that show little promise may be simply dismissed, whereas potentially beneficial designs can be examined quickly. Furthermore, there is no need for a huge supply of raw materials because computer memory is plentiful, inexpensive, and takes up relatively little space.


The issue is that the simulated lifeforms may exhibit little similarity to those found in the actual world. Meanwhile, physical robots that can be manufactured have generally been locked in one shape for their entire existence. The idea is to combine the two approaches and allow robots to design and build their own next generation, which will then design and build its own next generation and so on.


The result will be something we humans can’t even imagine (and that is the entire idea). Robots could spend days and nights designing, building, and reiterating new types of robots and then running them through test courses, to determine the desired characteristics for the next generation. No humans involved.


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