Unshackling Evolution: Evolving Soft Robots with Multiple Materials and a Powerful Generative Encoding

Abstract

In 1994 Karl Sims showed that computational evolution can produce interesting morphologies that resemble natural or- ganisms. 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 encod- ings of previous work have been overly regular, not allow- ing 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 per- formance increases as more materials are added, that di- versity of form and behavior can be increased with differ- ent 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 com- plex, 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 pro- duced by Sims nearly twenty years ago.

BibTeX

@inproceedings{cheney2013,
  year = {2013},
  publisher = {ACM SIGEVOlution, Volume 7, Issue 1},
  author = {Cheney, Nick and MacCurdy, Robert and Clune, Jeff and Lipson, Hod},
  title = {Unshackling Evolution: Evolving Soft Robots with Multiple Materials and a Powerful Generative Encoding},
  booktitle = {Proceedings of the15th annual conference on genetic and evolutionary computation (GECCO)},
}