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Evolutionary robotics

Published:01 August 2013Publication History
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Abstract

Taking a biologically inspired approach to the design of autonomous, adaptive machines.

References

  1. Auerbach, J.E. and Bongard, J.C. On the relationship between environmental and morphological complexity in evolved robots. In Proceedings of the 2012 Genetic and Evolutionary Computation Conference, 521--528. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Beer, R.D. The dynamics of brain-body-environment systems: A status report. Handbook of Cognitive Science: An Embodied Approach (2008), 99--120.Google ScholarGoogle Scholar
  3. Bongard, J. Morphological change in machines accelerates the evolution of robust behavior. In Proceedings of the National Academy of Sciences 108, 4 (2011), 1234.Google ScholarGoogle ScholarCross RefCross Ref
  4. Bongard, J. Zykov, V. and Lipson, H. Resilient machines through continuous self-modeling. Science 314 (2006), 1118--1121.Google ScholarGoogle ScholarCross RefCross Ref
  5. Cheney, N., MacCurdy, R., Clune, J. and Lipson, H. Unshackling evolution: Evolving soft robots with multiple materials and a powerful generative encoding. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM, NY, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Clune, J., Beckmann, B.E., Ofria, C. and R.T. Pennock, R.T. Evolving coordinated quadruped gaits with the hyperneat generative encoding. IEEE Congress on Evolutionary Computation (2009), 2764--2771. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Collins, S., Ruina, A., Tedrake, R. and Wisse, M. Efficient bipedal robots based on passive-dynamic walkers. Science 307, 5712 (2005), 1082--1085.Google ScholarGoogle ScholarCross RefCross Ref
  8. Edlund, J.A., Chaumont, N., Hintze, A., Koch, C., Tononi, G. and Adami, C. Integrated information increases with fitness in the evolution of animats. PLoS Computational Biology 7, 10 (2011).Google ScholarGoogle ScholarCross RefCross Ref
  9. Floreano, D. and Mattiussi, C. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press, Cambridge, MA, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Frutiger, D.R., Bongard, J.C. and Iida, F. Iterative product engineering: Evolutionary robot design. In Proceedings of the Fifth International Conference on Climbing and Walking Robots. P. Bidaud and F.B. Amar, eds. Professional Engineering Publishing, 2002, 619--629.Google ScholarGoogle Scholar
  11. Hauert, S., Zufferey, J.C. and Floreano, D. Evolved swarming without positioning information: An application in aerial communication relay. Autonomous Robotics 26 (2009), 21--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Hornby, G.S. and Pollack, J.B. Creating high-level components with a generative representation for body-brain evolution. Artificial Life 8, 3 (2002), 223--246. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Iida, F. and Laschi, C. Soft robotics: Challenges and perspectives. Procedia Computer Science 7 (2011), 99--102.Google ScholarGoogle ScholarCross RefCross Ref
  14. Izquierdo, E. and Buhrmann, T. Analysis of a dynamical recurrent neural network evolved for two qualitatively different tasks: Walking and chemotaxis. Artificial Life XI: Proceedings of the 11th International Conference on the Simulation and Synthesis of Living Systems. MIT Press, Cambridge, MA, 2008, 257--264.Google ScholarGoogle Scholar
  15. Jakobi, N., Husbands, P. and Harvey, I. Noise and the reality gap: The use of simulation in evolutionary robotics. Advances in Artificial Life (1995), 704--720. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Koos, S., Mouret, J.-M. and S. Doncieux, S. The transferability approach: Crossing the reality gap in evolutionary robotics. IEEE Transactions on Evolutionary Computation (2012); doi: 10.1109/TEVC.2012.2185849.Google ScholarGoogle Scholar
  17. Lehman, J. and Stanley, K.O. Abandoning objectives: Evolution through the search for novelty alone. Evolutionary Computation 19, 2 (2011), 189--223. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Lipson, H. and Pollack, J.B. Automatic design and manufacture of artificial lifeforms. Nature 406 (2000), 974--978.Google ScholarGoogle ScholarCross RefCross Ref
  19. Long, J. Darwin's Devices: What Evolving Robots Can Teach Us about the History of Life and the Future of Technology. Basic Books, 2012.Google ScholarGoogle Scholar
  20. Luke, S. and Spector, L. Evolving teamwork and coordination with genetic programming. In Proceedings of the First Annual Conference on Genetic Programming. MIT Press, Cambridge, MA, 150--156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Lungarella, M., Metta, G., Pfeifer, R. and Sandini, G. Developmental robotics: A survey. Connection Science 15, 4 (2003), 151--190.Google ScholarGoogle ScholarCross RefCross Ref
  22. Mataric, M. and Cliff, D. Challenges in evolving controllers for physical robots. Robotics and Autonomous Systems 19 (1996), 67--84.Google ScholarGoogle ScholarCross RefCross Ref
  23. Meng, Y., Zhang, Y. and Jin, Y. Autonomous self-reconfiguration of modular robots by evolving a hierarchical mechanochemical model. Computational Intelligence Magazine 6, 1 (2011). IEEE, 43--54. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Miglino, O., Lund, H.H. and S. Nolfi, S. Evolving mobile robots in simulated and real environments. Artificial Life 2, 4 (1995), 417--434. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Paul, C. Morphological computation: A basis for the analysis of morphology and control requirements. Robotics and Autonomous Systems 54, 8 (2006), 619--630.Google ScholarGoogle Scholar
  26. Paul, C., Valero-Cuevas, F.J. and Lipson, H. Design and control of tensegrity robots for locomotion. IEEE Transactions on Robotics 22, 5 (2006), 944--957. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Pfeifer, R. and Bongard, J. How the Body Shapes the Way We Think: A New View of Intelligence. MIT Press, Cambridge, MA, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Polani, D., Sporns, O. and Lungarella, M. How information and embodiment shape intelligent information processing. In 50 Years of Artificial Intelligence, Springer, 2007, 99--111. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Quinn, M. Smith, L., Mayley, G. and Husbands, P. Evolving controllers for a homogeneous system of physical robots: Structured cooperation with minimal sensors. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 361, 1811 (2003), 2321--2343.Google ScholarGoogle ScholarCross RefCross Ref
  30. Reil, Y. and Husbands, P. Evolution of central pattern generators for bipedal walking in a real-time physics environment. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 159--168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Reynolds, C.W. Flocks, herds and schools: A distributed behavioral model. In ACM SIGGRAPH Computer Graphics 21 (1987), 25--34. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Rieffel, J., Saunders, F., Nadimpalli, S., Zhou, H., Hassoun, S., Rife, J. and Trimmer, B. Evolving soft robotic locomotion in PhysX. In Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers. ACM, NY, 2009, 2499--2504. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Rubenstein, M., Ahler, C. and Nagpal, R. Kilobot: A low-cost scalable robot system for collective behanviors. In Proceedings of 2012 IEEE International Conference on Robotics and Automation. IEEE, 3293--3298.Google ScholarGoogle Scholar
  34. Sims, K. Evolving 3D morphology and behaviour by competition. Artificial Life. Rodney A. Brooks and Pattie Maes, eds, (2009), 28--39. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Tuci, E., Massera, G., and Nolfi, S. Active categorical perception of object shapes in a simulated anthropomorphic robotic arm. IEEE Transactions on Evolutionary Computation 14, 6 (2010), 885--899. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Werner, G.M. and Dyer, M.G. Evolution of communication in artificial organisms. In Proceedings of the Second International Conference of Artificial Life. D. Farmer, C. Langton, S. Rasmussen, and C. Taylor, eds, (1991), 659--687.Google ScholarGoogle Scholar
  37. Williams, P. and Beer, R. Information dynamics of evolved agents. In Proceedings of the 11th International Conference on Simulation of Adaptive Behavior. S. Doncieux, B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, and J-B. Mouret, eds. Springer, 2010, 38--49. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Wischmann, S., Floreano, D. and Keller, L. Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots. In Proceedings of the National Academy of Sciences 109, 3 (2012), 864--868.Google ScholarGoogle ScholarCross RefCross Ref
  39. Yim, M., Shen, W.M., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E. and Chirikjian, G.S. Modular self-reconfigurable robot systems (grand challenges of robotics). Robotics & Automation Magazine 14, 1 (2007). IEEE, 43--52.Google ScholarGoogle ScholarCross RefCross Ref
  40. Zahadat, P., Christensen, D., Schultz, U., Katebi, S. and Stoy, K. Fractal gene regulatory networks for robust locomotion control of modular robots. In Proceedings of the 11th International Conference on Simulation of Adaptive Behavior. S. Doncieux, B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, and J-B. Mouret, Eds. Springer, 2010, 544--554. Google ScholarGoogle ScholarDigital LibraryDigital Library

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                cover image Communications of the ACM
                Communications of the ACM  Volume 56, Issue 8
                August 2013
                85 pages
                ISSN:0001-0782
                EISSN:1557-7317
                DOI:10.1145/2492007
                Issue’s Table of Contents

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                • Published: 1 August 2013

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