A virtual creature swings four tentacle-like arms, pushing itself forward. It creeps up a hill, then rushes down the other side. It looks like “an octopus walking on land,” says A grim Gupta. This strange critter evolved its own body. It also learned its own method of moving. This mix of evolution and learning could help engineers build new kinds of robots, Gupta says.
A PhD student studying computer vision at Stanford University in California, Gupta is sort of like a grandfather to this octopus-like creature and hundreds of other odd-looking virtual critters. He created the ancestors that gave rise to these creatures. He calls them animals, which stands for “universal animals.” That term reflects the fact that they can evolve into so many body shapes. Some resemble real animals. Others are quite bizarre.
The team discovered that an animal’s body type affects its ability to learn new things. We tend to think of learning as something that happens in the brain. But, Gupta notes, “your body plays a huge role in what things you can learn.” The type of world you live in matters, too.
If robots could evolve in a simulation, they might develop their own forms that work even better, Gupta and his colleagues thought. Then engineers could build bodies they never would have dreamed up on their own.
So they tried it out. Animals that learned to move in more complicated simulated worlds ended up with bodies better suited for learning. Gupta and his group described this in Nature Communications last October.
“I was excited about this work,” says Sam Krugman. He was not involved in this research, but knows a lot about the topic. He works on evolutionary robotics at the Wuss Institute. It’s part of Harvard University in Boston, Mass. He also works at the Allen Discovery Center of Tufts University in Medford, Mass. Robot engineers have tended to copy bodies they see in nature. That’s why many robots resemble real animals, such as dogs or people. Just over 500 randomly generated animals get tossed into a virtual world, which is a lot like a video game. In the simplest game, each animal has to cross a flat landscape. It figures out how to move using a computer model of machine learning. Machine learning is a type of artificial intelligence (AI) that allows computers to practice a skill until they have mastered it.
In this case, the machine-learning model controls the animal’s body. At first, when the model knows nothing about moving, the body flails around as it tries out random motions. If one motion brings the animal closer to its goal of crossing the landscape, the model learns to repeat that motion. The farther the animal gets across the landscape, the higher its score in the game.
A bouncing starfish
Later, the animals get split up into groups of four. Whichever member of the group has the highest score gets to evolve. Let’s imagine that the winner looks a bit like a starfish. When it evolves, its body changes randomly. For example, it might lose some of its legs. Or, all of its legs might grow a new segment. Or one might get longer and another shorter. In this last case, the limbs get lighter. Then “the starfish can bounce around more easily,” Gupta explains.
Later, all animals from the original group of four go back into the flat virtual world together with the new starfish. They remember nothing from their first trip through the world. They all have to start from scratch, flailing around until something works. Again, they all get a score and face off in groups of four to see who gets to evolve next.
This process repeats, over and over. Whenever a new animal gets created, the oldest one dies. If it was doing a good job, then it will have evolved a few times before it died. That means it left behind a bunch of children and grandchildren that might do even better. Over many generations, animals get better and better at crossing the landscape. They remember nothing from experiences. That’s because the point isn’t to cross the landscape. It’s to evolve bodies that are better at learning to move.
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