Abstract
Non-rigid physical elements attached to robotic systems introduce non-linear dynamics that requires innovative control approaches. This paper describes some of our results applying Q-Learning to learn the control commands to solve a hose transportation problem. The learning process is developed in a simulated environment. Computationally expensive but dynamically accurate Geometrically Exact Dynamic Splines (GEDS) have been used to model the hose to be transported by a single robot, showing the difficulties of controlling flexible elastic passive linking elements.
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References
Duro, R.J., Graña, M., de Lope, J.: On the potential contributions of hybrid intelligent approaches to multicomponen robotic system development. Information Sciences 180(14), 2635–2648 (2010)
Prieto, A., Bellas, F., Caamaño, P., Duro, R.J.: Automatic behavior pattern classification for social robots. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010. LNCS, vol. 6076, pp. 88–95. Springer, Heidelberg (2010)
Echegoyen, Z.: Contributions to visual servoing for legged and linked multicomponent robots. Ph.D. dissertation, UPV/EHU (2009)
Echegoyen, Z., Villaverde, I., Moreno, R., Graña, M., d’Anjou, A.: Linked multi-component mobile robots: modeling, simulation and control. In: Robotics and Autonomous Systems, vol. 58(12), pp. 1292–1305 (2010)
Fernandez-Gauna, B., Lopez-Guede, J.M., Zulueta, E.: Linked multicomponent robotic systems: Basic assessment of linking element dynamical effect. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010. LNCS, vol. 6076, pp. 73–79. Springer, Heidelberg (2010)
Fernandez-Gauna, B., Lopez-Guede, J.M., Graña, M.: Learning hose transport control with q-learning. Neural Network World 20(7), 913–923 (2010)
Maravall, D., de-Lope, J., Martin, J.A.: Hybridizing evolutionary computation and reinforcement learning for the design of almost universal controllers for autonomous robots. Neurocomputing 72(4-6), 887–894 (2009)
Martin, J.A., de Lope, J., Maravall, D.: Adaptation, anticipation and rationality in natural and artificial systems: Computational paradigms mimicking nature. Natural Computing 8(4), 757–775 (2009)
Martin, J.A., de Lope, J., Santos, M.: A method to learn the inverse kinematics of multi-link robots by evolving neuro-controllers. Neurocomputing 72(13-15), 2806–2814 (2009)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Theetten, A., Grisoni, L., Andriot, C., Barsky, B.: Geometrically exact dynamic splines. Computer-Aided Design 40(1), 35–48 (2008)
Graña, M., Torrealdea, F.J.: Hierarchically structured systems. European Journal of Operational Research 25, 20–26 (1986)
Graña, M., d’Anjou, A., Albizuri, F.X., Hernandez, M., Torrealdea, F.J., Gonzalez, A.I.: Experiments of fast learning with high order boltzmann machines. Applied Intelligence 7(4), 287–303 (1997)
d’Anjou, A., Graña, M., Torrealdea, F.J., Hernandez, M.: Solving satisfiability via boltzmann machines. IEEE Transactions on pattern analysis and machine intelligence 15, 514–521 (1993)
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Lopez-Guede, J.M., Fernandez-Gauna, B., Graña, M., Zulueta, E. (2011). Empirical Study of Q-Learning Based Elemental Hose Transport Control. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_55
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DOI: https://doi.org/10.1007/978-3-642-21222-2_55
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