Abstract
Methods for dealing with the problem of the “reality gap” in evolutionary robotics are described. The focus is on simulator tuning, in which simulator parameters are adjusted in order to more accurately model reality. We investigate sample selection, which is the method of choosing the robot controllers, evaluated in reality, that guide simulator tuning. Six strategies for sample selection are compared on a robot locomotion task. It is found that strategies that select samples that show high fitness in simulation greatly outperform those that do not. One such strategy, which selects the sample that is the expected fittest as well as the most informative (in the sense of producing the most disagreement between potential simulators), results in the creation of a nearly optimal simulator in the first iteration of the simulator tuning algorithm.
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References
Doncieux, S., Bredeche, N., Mouret, J.-B.: Exploring new horizons in evolutionary design of robots. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE Press (2009)
Harvey, I., Husbands, P., Cliff, D., Thompson, A., Jakobi, N.: Evolutionary robotics: the sussex approach. Robotics and Autonomous Systems 20, 205–224 (1997)
Sims, K.: Evolving virtual creatures. In: SIGGRAPH 1994: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pp. 15–22. ACM, New York (1994)
Linden, D., Hornby, G., Lohn, J., Globus, A., Krishunkumor, K.: Automated antenna design with evolutionary algorithms. American Institute of Aeronautics and Astronautics 5, 1–8 (2006)
Rieffel, J., Trimmer, B., Lipson, H.: Mechanism as mind: What tensegrities and caterpillars can teach us about soft robotics. In: Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems (2008)
Glette, K., Hovin, M.: Evolution of Artificial Muscle-Based Robotic Locomotion in PhysX. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2010)
Rieffel, J., Saunders, F., Nadimpalli, S., Zhou, H., Hassoun, S., Rife, J., Trimmer, B.: Evolving soft robotic locomotion in PhysX. In: GECCO 2009: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, pp. 2499–2504. ACM, New York (2009)
Bongard, J.C.: Incremental Approaches to the Combined Evolution of a Robot’s Body and Brain. PhD thesis, University of Zurich (2003)
Macinnes, I., Di Paolo, E.: Crawling out of the simulation: Evolving real robot morphologies using cheap reusable modules. In: Pollack, J., Bedau, M., Husbands, P., Ikegami, T., Watson, R. (eds.) Artificial Life IX: Proceedings of the Ninth Interational Conference on the Simulation and Synthesis of Life, pp. 94–99. MIT Press, Cambridge (2004)
Klaus, G., Glette, K., Høvin, M.: Evolving Locomotion for a Simulated 12-DOF Quadruped Robot. In: Lones, M.A., Smith, S.L., Teichmann, S., Naef, F., Walker, J.A., Trefzer, M.A. (eds.) IPCAT 2012. LNCS, vol. 7223, pp. 90–98. Springer, Heidelberg (2012)
Zykov, V., Bongard, J.C., Lipson, H.: Evolving dynamic gaits on a physical robot. In: Proceedings of Genetic and Evolutionary Computation Conference, Late Breaking Paper, GECCO (2004)
Jakobi, N.: Minimal Simulations for Evolutionary Robotics. PhD thesis, University of Sussex (1998)
Jakobi, N., Husbands, P., Harvey, I.: Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics. In: Morán, F., Merelo, J.J., Moreno, A., Chacon, P. (eds.) ECAL 1995. LNCS, vol. 929, pp. 704–720. Springer, Heidelberg (1995)
Miglino, O., Lund, H.H., Nolfi, S.: Evolving mobile robots in simulated and real environments. Artificial Life 2, 417–434 (1996)
Koos, S., Mouret, J.-B., Doncieux, S.: The transferability approach: Crossing the reality gap in evolutionary robotics. IEEE Transactions on Evolutionary Computation (2012)
Koos: The transferability approach- an answer to the problems of reality gap, generalization, and adaptation. PhD thesis, Institut des Systémes Intelligents et de Robotique Université Pierre et Marie CURIE (2011)
Floreano, D., Urzelai, J.: Evolution of Plastic Control Networks. Autonomous Robots 11(3), 311–317 (2001)
Hartland, C., Bredeche, N.: Evolutionary robotics, anticipation and the reality gap. In: IEEE International Conference on Robotics and Biomimetics, ROBIO 2006, pp. 1640–1645 (December 2006)
Glette, K., Klaus, G., Zagal, J.C., Tørresen, J.: Evolution of locomotion in a simulated quadruped robot and transferral to reality. In: Artificial Life and Robotics (2012)
Zagal, J.C., Ruiz-del-Solar, J., Vallejos, P.: Back to reality: Crossing the reality gap in evolutionary robotics. In: Proceedings of IAV 2004, the 5th IFAC Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal (2004)
Zagal, J.C., Ruiz-Del-Solar, J.: Combining simulation and reality in evolutionary robotics. J. Intell. Robotics Syst. 50, 19–39 (2007)
Bongard, J.C., Lipson, H.: Once more unto the breach: co-evolving a robot and its simulator. In: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE9), pp. 57–62 (2004)
Bongard, J., Lipson, H.: Nonlinear system identification using coevolution of models and tests. IEEE Transactions on Evolutionary Computation 9, 361–384 (2005)
Hemker, T., Sakamoto, H., Stelzer, M., Stryk, O.V.: Hardware-in-the-loop optimization of the walking speed of a humanoid robot. In: CLAWAR 2006: 9th International Conference on Climbing and Walking Robots, pp. 614–623 (2006)
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Klaus, G., Glette, K., Tørresen, J. (2012). A Comparison of Sampling Strategies for Parameter Estimation of a Robot Simulator. In: Noda, I., Ando, N., Brugali, D., Kuffner, J.J. (eds) Simulation, Modeling, and Programming for Autonomous Robots. SIMPAR 2012. Lecture Notes in Computer Science(), vol 7628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34327-8_18
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DOI: https://doi.org/10.1007/978-3-642-34327-8_18
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