ABSTRACT
The biological process of ribosomal assembly is one of the most versatile systems in nature. With only a few small building blocks, this natural process is capable of synthesizing the multitude of complex chemicals that form the basis of all organic life. This paper presents a robotics design and manufacturing scheme which seeks to capture some of the versatility of the ribosomal process. In this scheme, a custom "printer" folds a long ribbon of material in which control elements such as motors have been embedded into a morphology that is capable of accomplishing a pre-defined task. The evolved folding patterns are encoded with a special kind of compositional pattern producing network (CPPN), which can compactly describe patterns with regularities such as symmetry, repetition, and repetition with variation. This paper tests the efficacy of this design scheme and the effects of different ribbon lengths on the ability to produce walking robot morphologies. We show that a single strip of material can be folded into a variety of different morphologies displaying different forms of locomotion. Thus, the results presented here suggest a promising new method for the automated design and manufacturing of robotic systems.
- J. Bachrach, V. Zykov, and S. Griffith. Folding arbitrary 3d shapes with space-filling chain robots: Folded configuration design as 3d hamiltonian path through target solid, 2009.Google Scholar
- J. Clune, B. E. Beckmann, C. Ofria, and R. T. Pennock. Evolving coordinated quadruped gaits with the HyperNEAT generative encoding. In phProceedings of the IEEE Congress on Evolutionary Computation (CEC-2009) Special Session on Evolutionary Robotics, Piscataway, NJ, USA, 2009. IEEE Press. Google ScholarDigital Library
- J. Gauci and K. O. Stanley. A case study on the critical role of geometric regularity in machine learning. In phProceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-2008), Menlo Park, CA, 2008. AAAI Press. Google ScholarDigital Library
- J. Gauci and K. O. Stanley. Autonomous evolution of topographic regularities in artificial neural networks. phNeural Comput., 22: 1860--1898, 2010. Google ScholarDigital Library
- S. Griffith. phGrowing machines. PhD thesis, Massachusetts Institute of Technology, 2004.Google Scholar
- A. Knaian, K. Cheung, M. Lobovsky, A. Oines, P. Schmidt-Neilsen, and N. Gershenfeld. The milli-motein: A self-folding chain of programmable matter with a one centimeter module pitch. In phIntelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pages 1447--1453. IEEE, 2012.Google Scholar
- D. Landau and K. Binder. phA guide to Monte Carlo simulations in statistical physics. Cambridge university press, 2005. Google ScholarDigital Library
- J. Lehman and K. Stanley. Evolving a diversity of virtual creatures through novelty search and local competition. In phProceedings of the 13th annual conference on Genetic and evolutionary computation, pages 211--218. ACM, 2011. Google ScholarDigital Library
- H. Lipson and J. B. Pollack. Automatic design and manufacture of robotic lifeforms. phNature, 406 (6799): 974--978, 2000.Google Scholar
- H. Lodish, D. Baltimore, A. Berk, and J. Darnell. phMolecular cell biology. WH Freeman New York, NY:, 1995.Google Scholar
- S. Lohmann, J. Yosinski, E. Gold, J. Clune, J. Blum, and H. Lipson. Aracna: An open-source quadruped platform for evolutionary robotics. In phArtificial Life, volume 13, pages 387--392, 2012.Google Scholar
- C. Onal, R. Wood, and D. Rus. Towards printable robotics: Origami-inspired planar fabrication of three-dimensional mechanisms. In phRobotics and Automation (ICRA), 2011 IEEE International Conference on, pages 4608--4613. IEEE, 2011.Google Scholar
- J. Secretan, N. Beato, D. B. D'Ambrosio, A. Rodriguez, A. Campbell, and K. O. Stanley. Picbreeder: Evolving pictures collaboratively online. In phCHI '08: Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, pages 1759--1768, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- J. Secretan, N. Beato, D. B. D'Ambrosio, A. Rodriguez, A. Campbell, J. T. Folsom-Kovarik, and K. O. Stanley. Picbreeder: A case study in collaborative evolutionary exploration of design space. phEvolutionary Computation, 19 (3): 373--403, 2011. Google ScholarDigital Library
- K. O. Stanley. Compositional pattern producing networks: A novel abstraction of development. phGenetic Programming and Evolvable Machines Special Issue on Developmental Systems, 8 (2): 131--162, 2007. Google ScholarDigital Library
- K. O. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. phEvolutionary Computation, 10: 99--127, 2002. Google ScholarDigital Library
- K. O. Stanley and R. Miikkulainen. Competitive coevolution through evolutionary complexification. phJournal of Artificial Intelligence Research, 21: 63--100, 2004. Google ScholarDigital Library
- K. O. Stanley, D. B. D'Ambrosio, and J. Gauci. A hypercube-based indirect encoding for evolving large-scale neural networks. phArtificial Life, 15 (2): 185--212, 2009. Google ScholarDigital Library
Index Terms
- Ribosomal robots: evolved designs inspired by protein folding
Recommendations
Scaffolding for interactively evolving novel drum tracks for existing songs
Evo'08: Proceedings of the 2008 conference on Applications of evolutionary computingA major challenge in computer-generated music is to produce music that sounds natural. This paper introduces NEAT Drummer, which takes steps toward natural creativity. NEAT Drummer evolves a kind of artificial neural network called a Compositional ...
Evolving the placement and density of neurons in the hyperneat substrate
GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computationThe Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach demonstrated that the pattern of weights across the connectivity of an artificial neural network (ANN) can be generated as a function of its geometry, thereby allowing ...
Investigating whether hyperNEAT produces modular neural networks
GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computationHyperNEAT represents a class of neuroevolutionary algorithms that captures some of the power of natural development with a computationally efficient high-level abstraction of development. This class of algorithms is intended to provide many of the ...
Comments