Evolving Soft Robots with CPPNs (New Paper & Video)

[Jeff Clune]

Hello all,

I am pleased to announce a new paper on evolving soft robots with CPPNs. In it we try to improve the complexity of evolved virtual organisms over what Karl Sims generated nearly 20 years ago. What do you think: are we on track to do that? Have we already? The video is the best way to judge.

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I just found out that the video for the paper is on the front page of Hacker News (I have no idea how they found it so fast without any announcements). 

If you want to help out, please go here and "up vote" us. You'll need to register, but that is painless. NOTE: you may have to scroll to the second or third page (hopefully not!).

https://news.ycombinator.com/news

Look for: "Bizarre Soft Robots Evolve to Run (ieee.org)"
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Cite: Cheney N, MacCurdy R, Clune J, Lipson H (2013) Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding. Proceedings of the Genetic and Evolutionary Computation Conference. 

Video: http://youtu.be/z9ptOeByLA4

PDF: http://jeffclune.com/publications/2013_Softbots_GECCO.pdf

Abstract: 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.

As always, please let me know if you have any comments or questions!

PS. Here is the IEEE Spectrum article that found the video: http://spectrum.ieee.org/automaton/robotics/robotics-software/bizarre-soft-robots-evolve-to-run

Best regards,
Jeff Clune

Assistant Professor
Computer Science

University of Wyoming
jeff...@uwyo.edu
jeffclune.com