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Complex Morphology Neural Network Simulation in Evolutionary Robotics

Published online by Cambridge University Press:  22 July 2019

Grant W. Woodford Open the ORCID record for Grant W. Woodford [Opens in a new window]  and
Mathys C. du Plessis
Grant W. Woodford*
Affiliation:
Department of Computing Sciences, Nelson Mandela University South Campus, Port Elizabeth, South Africa E-mail: [email protected]
Mathys C. du Plessis
Affiliation:
Department of Computing Sciences, Nelson Mandela University South Campus, Port Elizabeth, South Africa E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dimensional problem specified in this work.

Keywords

AutomationControl of robotic systemsMotion planningRobot dynamicsSerial manipulator design and kinematics
Type
Articles
Information
Robotica , Volume 38 , Issue 5 , May 2020 , pp. 886 - 902
Copyright
Copyright © Cambridge University Press 2019

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References

Bongard, J., “Evolutionary robotics”, Commun. ACM 56(8), 74–83 (2013).CrossRefGoogle Scholar
Nolfi, S. and Floreano, D., Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines (MIT Press, Cambridge, MA, 2000).Google Scholar
Woodford, G., du Plessis, M. and Pretorius, C., “Evolving Snake Robot Controllers Using Artificial Neural Networks as an Alternative to a Physics-Based Simulator”, 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa (2015) pp. 267–274.Google Scholar
Pretorius, C., du Plessis, M. and Cilliers, C., Simulating robots without conventional physics: A neural network approach, J. Intell. Robotic Syst., Springer 71(3–4), 319–348(2013).CrossRefGoogle Scholar
Pretorius, C., du Plessis, M. and Gonslaves, J., A Comparison of Neural Networks and Physics Models as Motion Simulators for Simple Robotic Evolution, 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China (2014) pp. 2793–2800.Google Scholar
Prabhu, S., Seals, R., Kyberd, P., and Wetherall, J., A survey on evolutionary-aided design in robotics, Robotica 36(12), 1804–1821 (2018).CrossRefGoogle Scholar
Floreano, D. and Mondada, F., Automatic creation of an autonomous agent: Genetic evolution of a neural-network driven robot, In: Cliff, D., Husbands, P., Meyer, J.-A., Wilson, S. (eds.), From Animals to Animats 3, (MIT Press, Cambridge, MA, 1994) pp. 421–430.Google Scholar
Miglino, O., Lund, H. and Nolfi, S., Evolving mobile robots in simulated and real environments, Artificial Life 2(4), 417–434 (1995).CrossRefGoogle ScholarPubMed
Mouret, J. and Chatzilygeroudis, K.20 Years of Reality Gap: A Few Thoughts About Simulators in Evolutionary Robotics, Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany (2017) pp. 1121–1124.Google Scholar
Woodford, G., du Plessis, M. and Pretorius, C., Concurrent controller and Simulator Neural Network development for a snake-like robot in Evolutionary Robotics, Robot. Auton. Syst., 88, 37–50 (2017).CrossRefGoogle Scholar
Bongard, J., Zykov, V. and Lipson, H., Resilient machines through continuous self-modeling, Science, Elsevier 314(5802), 1118–1121 (2006).Google ScholarPubMed
Grefenstette, J. and Ramsey, C., An Approach to Anytime Learning, Mach. Learn. Proc. 189–195 (1992).CrossRefGoogle Scholar
Zagal, J. and Ruiz-Del-Solar, J., Combining simulation and reality in evolutionary robotics, J. Intell. Robot. Syst. 50(1), 19–39 (2007).CrossRefGoogle Scholar
Cully, A., Clune, J., Tarapore, D. and Mouret, J., Robots that can adapt like animals, Nature 521(7553), 503–507 (2015).CrossRefGoogle ScholarPubMed
Koos, S., Mouret, J. and Doncieux, S., The transferability approach: Crossing the reality gap in evolutionary robotics, Evol. Computat. 17(1), 122–145 (2013).CrossRefGoogle Scholar
Pretorius, C., du Plessis, M. and Gonslaves, J., Neuroevolution of Inverted Pendulum Control: A Comparative Study of Simulation Techniques, J. Intell. Robotic Syst. 86(3–4), 419–445 (2017).CrossRefGoogle Scholar
Nardi, R. and Holland, O., Coevolutionary modelling of a miniature rotorcraft, Intell. Auton. Syst. 10, 364 (2008).Google Scholar
Woodford, G., Pretorius, C. and du Plessis, M., Concurrent controller and Simulator Neural Network development for a differentially-steered robot in Evolutionary Robotics, Robot. Auton. Syst. 76, 80–92 (2016).CrossRefGoogle Scholar
Koopaee, J., Van Huijgevoort, B., Pretty, C. and Chen, X., Parameters tuning of snake robots sidewinding gait using Bayesian optimization, 2018 4th International Conference on Control, Automation and Robotics (ICCAR), Auckland, New Zealand (2018) pp. 43–49.Google Scholar
Melo, K., Hernandez, M. and Gonzalez, D., Parameterized Space Conditions for the Definition of Locomotion Modes in Modular Snake Robots, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), Guangzhou, China (2012) pp. 2032–2038.Google Scholar
Dowling, K., Limbless locomotion: Learning to crawl, Proceedings of the IEEE International Conference on Robotics and Automation, 1999, Detroit, Michigan, USA, vol. 4 (1999) pp. 3001–3006.Google Scholar
Kamegawa, T., Matsuno, F. and Chatterjee, R., Proposition of twisting mode of locomotion and GA based motion planning for transition of locomotion modes of 3-dimensional snake-like robot, Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2002, Washington, DC, USA, vol. 2 (2002) pp. 1507–1512.CrossRefGoogle Scholar
Pretorius, C., du Plessis, M. and Gonsalves, J., Evolutionary robotics applied to hexapod locomotion: A comparative study of simulation techniques, J. Intell. Robotic Syst. 1–23 (2019).Google Scholar
Gal, Y. and Ghahramani, Z., Dropout as a Bayesian approximation: Insights and applications, Deep Learning Workshop, ICML (2015).Google Scholar
Melo, K., Paez, L. and Parra, C., Indoor and Outdoor Parametrized Gait Execution with Modular Snake Robots, IEEE International Conference on Robotics and Automation, St. Paul, MN, USA (2012).CrossRefGoogle Scholar
Murata, S. and Kurokawa, H., Self-reconfigurable robots, Robot. Autom. Mag. 14(1), 71–78 (2007).CrossRefGoogle Scholar
Chollet, F., Keras, Github (2015. https://github.com/keras-team/kerasGoogle Scholar
Martín, A. and Ashish, A., TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, (2015). https://www.tensorflow.orgGoogle Scholar
Kingma, D. and Ba, J., Adam: A method for stochastic optimization, (2014). arXiv preprint arXiv:1412.6980.Google Scholar