Roboticists have developed a “mother” robot that can build and evaluate her own “children,” and then decide which version performs best to inform the design of the next generation. Remarkably, the system doesn’t require any human intervention.

Unlike biological species that evolve autonomously over time, robots are dependent on humans for reproduction and refinement. To overcome these limitations, a research team led by roboticists from the University of Cambridge have leveraged Darwinian principles to create a robotic system capable of artificial evolution. Their paper now appears at the open access journal PLOS ONE.

Here’s how the system works: A so-called “mother” robot is programmed to build a “child” robot that’s capable of rudimentary locomotion. This child can consist of anywhere from one to five plastic cubes, each with a small motor inside. Then, without any human intervention or computer simulation, the mother robot evaluates the quality of her offspring according to a speed test, and then uses that information to inform the design of next generation of progeny. It’s survival of the fittest, but applied to robots.

“In all experiments,” write the researchers in their study, “the fitness increases relative to the initial generation.” (Credit: University of Cambridge)


“Natural selection is basically reproduction, assessment, reproduction, assessment and so on,” noted lead researcher Fumiya Iida in a statement. “That’s essentially what this robot is doing—we can actually watch the improvement and diversification of the species.”

In five separate experiments, the mother designed, constructed, and evaluated ten agents over ten generations (for a total of 100 candidates). Each experiment typically began with a randomly generated child-bot. As the experiment progressed, the mother robot mutated her offspring by manipulating the physical configurations of the five blocks, which in this case can be construed as the robotic equivalent of genes.

By the time the mother robot got to the last generation, her spawn performed a speed task twice as quickly as the best individuals in the first generation. What’s more, her ability to improve performance increased over time. The researchers say this was on account of the robot’s ability to fine-tune design parameters during later generations.

Results from one of the five experiments. (Credit: University of Cambridge)

Fascinatingly, the researchers say some designs weren’t likely to have been conceived by a human; it was truly doing it’s own thing.


“One of the big questions in biology is how intelligence came about—we’re using robotics to explore this mystery,” added Iida. “We think of robots as performing repetitive tasks, and they’re typically designed for mass production instead of mass customisation, but we want to see robots that are capable of innovation and creativity.”

Interestingly, locomotion studies like these have been conducted before, but only in computer simulations. One of the strengths of the new study is that it’s not prone to the so-called “reality gap”, a mismatch between simulated and real-world behavior.

Read the entire study at PLOS ONE: “Morphological Evolution of Physical Robots through Model-Free Phenotype Development”.

Contact the author at and @dvorsky. All images University of Cambridge/L. Brodbeck et al., 2015/PLOS ONE.