Evolving Soft Robots in Tight Spaces

ABSTRACT

Soft robots have become increasingly popular in recent years -- and justifiably so. Their compliant structures and (theoretically) infinite degrees of freedom allow them to undertake tasks which would be impossible for their rigid body counterparts, such as conforming to uneven surfaces, efficiently distributing stress, and passing through small apertures. Previous work in the automated deign of soft robots has shown examples of these squishy creatures performing traditional robotic task like locomoting over flat ground. However, designing soft robots for traditional robotic tasks fails to fully utilize their unique advantages. In this work, we present the first example of a soft robot evolutionarily designed for reaching or squeezing through a small aperture -- a task naturally suited to its type of morphology. We optimize these creatures with the CPPN-NEAT evolutionary algorithm, introducing a novel implementation of the algorithm which includes multi-objective optimization while retaining its speciation feature for diversity maintenance. We show that more compliant and deformable soft robots perform more effectively at this task than their less flexible counterparts. This work serves mainly as a proof of concept, but we hope that it helps to open the door for the better matching of tasks with appropriate morphologies in robotic design in the future.

References

1. J. E. Auerbach and J. C. Bongard. Evolving cppns to grow three-dimensional physical structures. In Proceedings of the 12th annual conference on Genetic and evolutionary computation, pages 627--634. ACM, 2010.
2. M. Calisti, M. Giorelli, G. Levy, B. Mazzolai, B. Hochner, C. Laschi, and P. Dario. An octopus-bioinspired solution to movement and manipulation for soft robots. Bioinspiration & biomimetics, 6(3):036002, 2011.
3. N. Cheney, J. Clune, and H. Lipson. Evolved electrophysiological soft robots. In ALIFE 14: The Fourteenth Conference on the Synthesis and Simulation of Living Systems, volume 14, pages 222--229, 2014.
4. N. Cheney, R. MacCurdy, J. Clune, and H. Lipson. Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding. In Proceedings of the 15th annual conference on Genetic and evolutionary computation, pages 167--174. ACM, 2013.
5. K.-J. Cho, J.-S. Koh, S. Kim, W.-S. Chu, Y. Hong, and S.-H. Ahn. Review of manufacturing processes for soft biomimetic robots. International Journal of Precision Engineering and Manufacturing, 10(3):171--181, 2009.
6. J. Hiller and H. Lipson. Automatic design and manufacture of soft robots. Robotics, IEEE Transactions on, 28(2):457--466, 2012.
7. J. Hiller and H. Lipson. Dynamic simulation of soft multimaterial 3d-printed objects. Soft Robotics, 1(1):88--101, 2014.
8. S. Kim, C. Laschi, and B. Trimmer. Soft robotics: a bioinspired evolution in robotics. Trends in biotechnology, 31(5):287--294, 2013.
9. J. Lehman, K. O. Stanley, and R. Miikkulainen. Effective diversity maintenance in deceptive domains. In Proceedings of the 15th annual conference on Genetic and evolutionary computation, pages 215--222. ACM, 2013.
10. C. Majidi. Soft robotics: a perspective--current trends and prospects for the future. Soft Robotics, 1(1):5--11, 2014.
11. G. Methenitis. Evolution of soft robots by novelty search. 2014.
12. J. Rieffel, D. Knox, S. Smith, and B. Trimmer. Growing and evolving soft robots. Artificial life, 20(1):143--162, 2014.
13. J. Schrum and R. Miikkulainen. Evolving multimodal behavior with modular neural networks in ms. pac-man. In Proceedings of the 2014 conference on Genetic and evolutionary computation, pages 325--332. ACM, 2014.
14. J. Secretan, N. Beato, D. B. D'Ambrosio, A. Rodriguez, A. Campbell, and K. O. Stanley. Picbreeder: evolving pictures collaboratively online. In Proc. of the 26th SIGCHI Conf. on Human Factors in Computing Systems, pages 1759--1768. ACM, 2008.
15. R. F. Shepherd, F. Ilievski, W. Choi, S. A. Morin, A. A. Stokes, A. D. Mazzeo, X. Chen, M. Wang, and G. M. Whitesides. Multigait soft robot. Proceedings of the National Academy of Sciences, 108(51):20400--20403, 2011.
16. K. O. Stanley. Compositional pattern producing networks: A novel abstraction of development. Genetic programming and evolvable machines, 8(2):131--162, 2007.
17. K. O. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary computation, 10(2):99--127, 2002.
18. A. Stilli, H. A. Wurdemann, and K. Althoefer. Shrinkable, stiffness-controllable soft manipulator based on a bio-inspired antagonistic actuation principle. In Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on, pages 2476--2481. IEEE, 2014.
19. Y. Sugiyama and S. Hirai. Crawling and jumping by a deformable robot. The International journal of robotics research, 25(5--6):603--620, 2006.
20. B. A. Trimmer, A. E. Takesian, B. M. Sweet, C. B. Rogers, D. C. Hake, and D. J. Rogers. Caterpillar locomotion: a new model for soft-bodied climbing and burrowing robots. In 7th International Symposium on Technology and the Mine Problem, volume 1, pages 1--10. Mine Warfare Association Monterey, CA, 2006.
21. W. van Willigen, E. Haasdijk, and L. Kester. Evolving intelligent vehicle control using multi-objective neat. In Computational Intelligence in Vehicles and Transportation Systems (CIVTS), 2013 IEEE Symposium on, pages 9--15. IEEE, 2013.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Control methods

    • Search methodologies

• Theory of computation

  • Design and analysis of algorithms

    • Algorithm design techniques

      • Dynamic programming