Data from: Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding

In 1994 Karl Sims showed that computational evolution can produce
interesting morphologies that resemble natural organisms. Despite nearly
two decades of work since, evolved morphologies are not obviously more
complex or natural, and the field seems to have hit a complexity ceiling.
One hypothesis for the lack of increased complexity is that most work,
including Sims', evolves morphologies composed of rigid elements,
such as solid cubes and cylinders, limiting the design space. A second
hypothesis is that the encodings of previous work have been overly
regular, not allowing complex regularities with variation. Here we test
both hypotheses by evolving soft robots with multiple materials and a
powerful generative encoding called a compositional pattern-producing
network (CPPN). Robots are selected for locomotion speed. We find that
CPPNs evolve faster robots than a direct encoding and that the CPPN
morphologies appear more natural. We also find that locomotion performance
increases as more materials are added, that diversity of form and behavior
can be increased with different cost functions without stifling
performance, and that organisms can be evolved at different levels of
resolution. These findings suggest the ability of generative soft-voxel
systems to scale towards evolving a large diversity of complex, natural,
multi-material creatures. Our results suggest that future work that
combines the evolution of CPPN-encoded soft, multi-material robots with
modern diversity-encouraging techniques could finally enable the creation
of creatures far more complex and interesting than those produced by Sims
nearly twenty years ago.