Abstract
In this paper we address the physical realization of proof of concept experiments demonstrating the suitability of the controllers learned by means of Reinforcement Learning (RL) techniques to accomplish tasks involving Linked Multi-Component Robotic System (LMCRS). In this paper, we deal with the task of transporting a hose by a single robot as a prototypical example of LMCRS, which can be extended to much more complex tasks. We describe how the complete system has been designed and built, explaining its different main components: the RL controller, the communications, and finally, the monitoring system. A previously learned RL controller has been tested solving a concrete problem with a determined state space modeling and discretization step. This physical realization validates our previous published works carried out through computer simulations, giving a strong argument in favor of the suitability of RL techniques to deal with real LMCRS systems.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Duro R, Graña M, de Lope J (2010) On the potential contributions of hybrid intelligent approaches to multicomponen robotic system development. Inf Sci 180(14):2635–2648
Lopez-Guede JM, Graña M, Zulueta E (2008) On distributed cooperative control for the manipulation of a hose by a multirobot system. In: Corchado E, Abraham A, Pedrycz W (eds) Hybrid artificial intelligence systems. Lecture notes in artificial intelligence, vol 5271, pp 673–679. 3rd international workshop on hybrid artificial intelligence systems, pp 24–26. University of Burgos, Burgos, Spain
Echegoyen Z (2009) Contributions to visual servoing for legged and linked multicomponent robots. Ph.D. dissertation, UPV/EHU
Echegoyen Z, Villaverde I, Moreno R, Graña M, d’Anjou A (2010) Linked multi-component mobile robots: modeling, simulation and control. Rob Auton Syst 58(12, SI):1292–1305
Boor CD (1994) A practical guide to splines. Springer
Rubin M (2000) Cosserat theories: shells. Kluwer, Rods and Points
Theetten A, Grisoni L, Andriot C, Barsky B (2008) Geometrically exact dynamic splines. Comput Aided Des 40(1):35–48
Fernandez-Gauna B, Lopez-Guede J, Zulueta E (2010) Linked multicomponent robotic systems: basic assessment of linking element dynamical effect. In: Manuel Grana Romay MGS, Corchado ES (eds.) Hybrid artificial intelligence systems, Part I, vol 6076. Springer, pp 73–79
Sutton R, Barto A (1998) Reinforcement learning: an introduction. MIT Press
Bellman R (1957) A markovian decision process. Indiana Univ Math J 6:679–684
Tijms HC (2004) Discrete-time Markov decision processes. John Wiley & Sons Ltd, pp 233–277. http://dx.doi.org/10.1002/047001363X.ch6
Watkins C (1989) Learning from delayed rewards. Ph.D. dissertation, University of Cambridge, England
Watkins C, Dayan P (1992) Technical note: Q-learning. Mach Learn 8:279–292. doi:10.1023/A:1022676722315. http://dx.doi.org/10.1023/A:1022676722315
Fernandez-Gauna B, Lopez-Guede J, Zulueta E, Graña M (2010) Learning hose transport control with q-learning. Neural Netw World 20(7):913–923
Graña M, Fernandez-Gauna B, Lopez-Guede J (2011) Cooperative multi-agent reinforcement learning for multi-component robotic systems: guidelines for future research. Paladyn. J Behav Rob 2:71–81. doi:10.2478/s13230-011-0017-5. http://dx.doi.org/10.2478/s13230-011-0017-5
Fernandez-Gauna B, Lopez-Guede JM, Zulueta E, Echegoyen Z, Graña M (2011) Basic results and experiments on robotic multi-agent system for hose deployment and transportation. Int J Artif Intell 6(S11):183–202
Fernandez-Gauna B, Lopez-Guede J, Graña M (2011) Towards concurrent q-learning on linked multi-component robotic systems. In: Corchado E, Kurzynski M, Wozniak M (eds) Hybrid artificial intelligent systems. Lecture notes in computer science, vol 6679. Springer, Berlin/Heidelberg, pp 463–470
Fernandez-Gauna B, Lopez-Guede J, Graña M (2011) Concurrent modular q-learning with local rewards on linked multi-component robotic systems. In: Ferrández J, Alvarez Sánchez J, de la Paz F, Toledo F (eds) Foundations on natural and artificial computation. Lecture notes in computer science, vol 6686. Springer, Berlin/Heidelberg, pp 148–155
Lopez-Guede JM, Fernandez-Gauna B, Graña M, Zulueta E (2011) Empirical study of q-learning based elemental hose transport control. In: Corchado E, Kurzynski M, Wozniak M (eds) Hybrid artificial intelligent systems. Lecture notes in computer science, vol 6679. Springer, Berlin/Heidelberg, pp 455–462
Lopez-Guede J, Fernandez-Gauna B, Graña M, Zulueta E (2012) Improving the control of single robot hose transport. Cybern Syst 43(4):261–275
Lopez-Guede JM, Fernandez-Gauna B, Moreno R, Graña M (2012) Robotic vision: technologies for machine learning and vision applications. In: José García-Rodríguez MC (ed.) IGI Global
Acknowledgments
The research was supported by the Computational Intelligence Group of the Basque Country University (UPV/EHU) through Grant IT874-13 of Research Groups Call 2013-2017 (Basque Country Government).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lopez-Guede, J.M., Estévez, J., Graña, M. (2015). Reinforcement Learning in Single Robot Hose Transport Task: A Physical Proof of Concept. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-319-19719-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-19719-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19718-0
Online ISBN: 978-3-319-19719-7
eBook Packages: EngineeringEngineering (R0)