Abstract
Manually developing walking patterns for kinematically complex robots can be a challenging and time-consuming task. In order to automate this design process, a learning system that generates, tests, and optimizes different walking patterns is needed, as well as the ability to accurately simulate a robot and its environment. In this work, we describe a learning system that uses the CMA-ES method from evolutionary computation to learn walking patterns for a complex legged robot. The robot’s limbs are controlled using parametrized distorted sine waves, and the evolutionary algorithm optimizes the parameters of these waveforms, testing the walking patterns in a physical simulation. The best solutions evolved by this system has been transferred to and tested on a real robot, and has resulted in a gait that is superior to those previously designed by a human designer.
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References
Beer, R.D., Gallagher, J.C.: Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior 1(1), 91–122 (1992)
Beer, R.D., Quinn, R.D., Chiel, H.J., Ritzmann, R.E.: Biologically inspired approaches to robotics: what can we learn from insects? Communications of the ACM 40(3), 30–38 (1997)
Gomi, T., Ide, K.: Emergence of gait of a legged robot by collaboration through evolution. In: IEEE World Congress on Computational Intelligence (WCCI 1998), New York. IEEE Press, Los Alamitos (1998)
Grillner, S.: Neural networks for vertebrate locomotion. Scientific American 274(1), 64–69 (1996)
Gruau, F., Quatramaran, K.: Cellular encoding for interactive evolutionary robotics. Technical report, Amsterdam, The Netherlands (1996)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)
Hornby, G.S., Fujita, M., Takamura, S., Yamamoto, T., Hanagata, O.: Autonomous evolution of gaits with the sony quadruped robot. In: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, 13-17, 1999, vol. 2, pp. 1297–1304. Morgan Kaufmann, San Francisco (1999)
Ito, K., Matsuno, F.: A study of reinforcement learning for the robot with many degrees of freedom - acquisition of locomotion patterns for multi-legged robot. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, pp. 3392–3397 (2002)
Kassahun, Y., Edgington, M., de Gea, J., Kirchner, F.: Exploiting sensorimotor coordination for learning to recognize objects. In: Twentieth International Joint Conference On Artificial Intelligence (IJCAI 2007), Hyderabad, India, pp. 883–888 (January 2007)
Lewis, M.A., Fagg, A.H., Solidum, A.: Genetic programming approach to the construction of a neural network for control of a walking robot. In: Proceedings of the International Conference on Robotics and Automation, Nice, France, 12-14 1992, vol. 3, pp. 2618–2623. IEEE Computer Society Press, Los Alamitos (1992)
Reil, T., Husbands, P.: Evolution of central pattern generators for bipedal walking in a real-time physics environment. IEEE Trans. Evolutionary Computation 6(2), 159–168 (2002)
Röfer, T.: Evolutionary gait-optimization using a fitness function based on proprioception. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 310–322. Springer, Heidelberg (2005)
Schwefel, H.-P.P.: Evolution and Optimum Seeking: The Sixth Generation. John Wiley & Sons, Inc., New York (1993)
Smith, R.: Open dynamics engine (2005), http://www.ode.org
Spenneberg, D.: Bioinspirierte Kontrolle von Laufrobotern. PhD thesis, Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany (July 2006)
Spenneberg, D., Albrecht, M., Backhaus, T., Hilljegerdes, J., Kirchner, F., Strack, A., Zschenker, H.: Aramies: A four-legged climbing and walking robot. In: Proceedings of 8th International Symposium iSAIRAS, Munich (2005)
Zhang, J., Chen, Q.: Learning based gaits evolution for an AIBO dog. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1523–1526 (September 2007)
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Römmermann, M., Edgington, M., Metzen, J.H., de Gea, J., Kassahun, Y., Kirchner, F. (2008). Learning Walking Patterns for Kinematically Complex Robots Using Evolution Strategies. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_108
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DOI: https://doi.org/10.1007/978-3-540-87700-4_108
Publisher Name: Springer, Berlin, Heidelberg
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