Abstract
Control of robotic joints movements requires the generation of appropriate torque and force patterns, coordinating the kinematically and dynamically complex multijoints systems. Control theory coupled with inverse and forward internal models are commonly used to map a desired endpoint trajectory into suitable force patterns. In this paper, we propose the use of tacit learning to successfully achieve similar tasks without using any kinematic model of the robotic system to be controlled. Our objective is to design a new control strategy that can achieve levels of adaptability similar to those observed in living organisms and be plausible from a neural control viewpoint. If the neural mechanisms used for mapping goals expressed in the task-space into control-space related command without using internal models remain largely unknown, many neural systems rely on data accumulation. The presented controller does not use any internal model and incorporates knowledge expressed in the task space using only the accumulation of data. Tested on a simulated two-link robot system, the controller showed flexibility by developing and updating its parameters through learning. This controller reduces the gap between reflexive motion based on simple accumulation of data and execution of voluntarily planned actions in a simple manner that does not require complex analysis of the dynamics of the system.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Minsky, M.L., Papert, S.A.: Perceptron. MIT Press, Cambridge (1969)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by backpropagating errors. Nature 323(6088), 533–536 (1986)
Kuniyoshi, Y., Yorozu, Y., Suzuki, S., Sangawa, S., Ohmura, Y., Terada, K., Nagakubo, A.: Emergence and development of embodied cognition: A constructivist approach using robots. Prog. Brain Res. 164, 425–445 (2007)
Barto, A.G., Sutton, R.S., Anderson, C.W.: Neuron-like adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst., Man, Cybern. SMC-13(5), 834–846 (1983)
Doya, K.: Reinforcement learning in continuous time and space. Neural Comput. 12(1), 219–245 (2000)
Tedrake, R., Zhang, T.W., Seung, H.S.: Stochastic policy gradient reinforcement learning on a simple 3D biped. In: Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 2849–2854 (2004)
Astrom, K.J., Wittenmark, B.: Adaptive Control. Addison-Wesley, Reading (1989)
Slotin, J.E., Li, W.: Applied Nonlinear Control. Prentice-Hall, Englewood Cliffs (1991)
Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man, Cybern. SMC-3(1), 28–44 (1973)
Juang, J.G.: Fuzzy neural network control CMAC of a biped walking robot. IEEE Trans. Syst., Man, Cybern. B, Cybern. 30(4), 594–601 (2000)
Shimoda, S., Kimura, H.: Bio-mimetic Approach to Tacit Learning based on Compound Control. IEEE Transactions on Systems, Man, and Cybernetics- Part B 40(1), 77–90 (2010)
Shimoda, S., Kimura, H.: Adaptability of tacit learning in bipedal locomotion. IEEE Transactions on Autonomous Mental Development 5(2), 152–161 (2013)
Hayashibe, M., Shimoda, S.: Emergence of Motor Synergy in Vertical Reaching Task via Tacit Learning. In: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4985–4988 (2013)
Cheah, C.C., Hirano, M., Kawamura, S., Arimoto, S.: Approximate Jacobian control for robots with uncertain kinematics and dynamics. IEEE Transactions on Robotics and Automation 19(4), 692–702 (2003)
Dixon, W.E.: Adaptive regulation of amplitude limited robot manipulators with uncertain kinematics and dynamics. IEEE Transactions on Automatic Control 52(3), 488–493 (2007)
Ozawa, R., Oobayashi, Y.: Adaptive task space PD control via implicit use of visual information. In: Int. Sym. Robot Control, pp. 209–214 (2009)
Smith, R.: Open Dynamics Engine, http://www.ode.org/
Shimoda, S., Yoshihara, Y., Fujimoto, K., Yamamoto, T., Maeda, I., Kimura, H.: Stability analysis of tacit learning based on environmental signal accumulation. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 7-12 (2012)
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
Berenz, V., Alnajjar, F., Hayashibe, M., Shimoda, S. (2015). Tacit Learning for Emergence of Task-Related Behaviour through Signal Accumulation. In: Sinčák, P., Hartono, P., Virčíková, M., Vaščák, J., Jakša, R. (eds) Emergent Trends in Robotics and Intelligent Systems. Advances in Intelligent Systems and Computing, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-319-10783-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-10783-7_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10782-0
Online ISBN: 978-3-319-10783-7
eBook Packages: EngineeringEngineering (R0)