ROBOTS have come a long way in the century since Czech writer Karel used the word to describe artificial automata. Once largely confined to factories, they are now found everywhere from the military and medicine to education and underground rescue. People have created robots that can make art, plant trees, ride skateboards and explore the ocean’s depths. There seems no end to the variety of tasks we can design a machine to do.
but what if we don’t know exactly what our robot needs to be capable of? We might want it to clean up a nuclear accident where it is unsafe to send humans, explore an unmapped asteroid or terraform a distant planet, for example. We could simply design it to meet any challenges we think it might and then keep our fingers crossed. There is a better alternative, though: take a lesson from evolution and create robots that can adapt to their environment. It sounds far-fetched, but that is exactly what my colleagues and I are working on in a project called
We aren’t there yet, but we have already created robots that can “mate” and “reproduce” to generate new designs that are built autonomously. What’s more, using the evolutionary mechanisms of variation and survival of the fittest, over generations, these robots can optimise their design. If successful, this would be a way to produce robots that can adapt to difficult, dynamic environments without direct human oversight. It is a project with huge potential – but not without major challenges and ethical implications.
The notion of using evolutionary principles to design objects can be traced back to the early 1960s and the origins of evolutionary computation, when a group of German engineering students invented the first “evolution strategy”. Their novel algorithm generated a range of designs and then selected a set of them, biased towards high-performing ones, to build upon in subsequent iterations. When applied to a real-world engineering problem, this not only optimised the design of a nozzle but also generated a final product that was so unintuitive that the process could be described as creative – one of the most prized properties of biological evolution.
Since then, there has been a step change in our ability to apply artificial evolution to designing objects. The enormous increase in computational power allows computers to churn through generations of designs in short order and to generate high-fidelity simulations of real environments in which to test these. Meanwhile, advances in evolutionary computation theory have resulted in better ways to represent the information from which designs are built – their virtual DNA – and to manipulate this when generating “offspring” so that it mirrors processes found in nature. These include mutation and DNA recombination, which creates genetic diversity through breaking stretches of DNA and recombining them in novel ways. The Examples of evolutionary design in practice now range from tables to new molecules with desired functions. As far back as 2006, NASA sent a satellite into space with a communication antenna created via artificial evolution.
An obvious way around this second shortcoming is to skip the simulation stage and build and evaluate new evolved designs directly in hardware. This was first demonstrated by researchers at ETH Zurich, Switzerland, in 2015. They used a “mother robot” equipped with an evolutionary algorithm to autonomously design and fabricate offspring. These were then tested, with only those achieving the best results being selected as designs to feed into the next generation. In 2016, Guszti Eiben at Free University Amsterdam, the Netherlands, and his team described a different approach. They used physical robots programmed with rules allowing them to “meet and mate”, triggering a production process to create a new “robot baby”.
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