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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-21222-2_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21221-5
Online ISBN: 978-3-642-21222-2
eBook Packages: Computer ScienceComputer Science (R0)