Without any human intervention or computer simulation beyond the initial command to build a robot capable of movement, the mother created children constructed of between one and 5 plastic cubes with a small motor inside.

In each of 5 separate experiments, the mother robot designed, built and tested generations of 10 children, using the information gathered from one generation to inform the design of the next.

The results found that preferential traits were passed down through generations, so that the ‘fittest’ individuals in the last generation performed a set task twice as quickly as the fittest individuals in the first generation.

“Natural selection is basically reproduction, assessment, reproduction, assessment and so on. That’s essentially what this robot is doing – we can actually watch the improvement and diversification of the species,” said Dr Fumiya Iida from the University of Cambridge, senior author on the study published in the journal PLoS ONE.

“One of the big questions in biology is how intelligence came about – we’re using robotics to explore this mystery,” Dr Iida added.

“We think of robots as performing repetitive tasks, and they’re typically designed for mass production instead of mass customization, but we want to see robots that are capable of innovation and creativity.”

In nature, organisms are able to adapt their physical characteristics to their environment over time. These adaptations allow biological organisms to survive in a wide variety of different environments – allowing animals to make the move from living in the water to living on land, for instance.

But machines are not adaptable in the same way. They are essentially stuck in one shape for their entire ‘lives,’ and it’s uncertain whether changing their shape would make them more adaptable to changing environments.

Evolutionary robotics is a growing field which allows for the creation of autonomous robots without human intervention.

Most work in this field is done using computer simulation. Although computer simulations allow researchers to test thousands or even millions of possible solutions, this often results in a ‘reality gap’ – a mismatch between simulated and real-world behavior.

While using a computer simulation to study artificial evolution generates thousands, or even millions, of possibilities in a short amount of time, Dr Iida and co-authors found that having the robot generate its own possibilities, without any computer simulation, resulted in more successful children. The disadvantage is that it takes time: each child took the robot about 10 minutes to design, build and test.

“In future we might use a computer simulation to pre-select the most promising candidates, and use real-world models for actual testing,” Dr Iida said.