When designing a brand new robot, it’s usually a good idea to design and test it in simulation first, to get a sense of how well your design is going to work. But even a successful simulated robot will only provide you limited insight into how it’s going to do when you actually build it: as we’ve seen, even sophisticated simulations don’t necessarily reveal how robots will perform in the real world.
This fundamental disconnect between simulation and reality becomes especially problematic when you’re dealing with an area of robotics where it’s impractical to build physical versions of everything. Evolutionary robotics is a very good example of this, where robot designs are tested and iterated over hundreds (or thousands) of generations: it works great in simulation (if you have a fast computer), but is much harder to do in practice. However, with something like evolutionary robotics, we come back to the original issue, which is that a robot that has evolved to work well in simulation may not work well at all out of simulation, which throws into question the value of iterating on the fitness of a robot through simulation at all.
In a paper published last month in PLOS ONE, Luzius Brodbeck, Simon Hauser, and Fumiya Iida from the Institute of Robotics and Intelligent Systems at ETH Zurich took things one step further by teaching a “mother robot” to autonomously build children robots out of component parts to see how well they move, doing all of the hard work of robot evolution without any simulation compromises at all.