Share This Article:

A Tree-Type Memory Formation by Sensorimotor Feedback: A Possible Approach to the Development of Robotic Cognition

Full-Text HTML XML Download Download as PDF (Size:561KB) PP. 154-165
DOI: 10.4236/ica.2013.42020    3,002 Downloads   4,018 Views  

ABSTRACT

Based on indications from neuroscience and psychology, both perception and action can be internally simulated in or- ganisms by activating sensory and/or motor areas in the brain without actual external sensory input and/or without any resulting behavior (a phenomenon called Thinking). This phenomenon is usually used by the organisms to cope with missing external inputs. Applying such phenomenon in a real robot recently has taken the attention of many researchers. Although some work has been reported on this issue, none of this work has so far considered the potential of the robot’s vision at the sensorimotor abstraction level, where extracting data from the environment takes place. In this study, a novel visiomotor abstraction is presented into a physical robot through a memory-based learning algorithm. Experi- mental results indicate that our robot with its vision could develop a kind of simple anticipation mechanism into its tree-type memory structure through interacting with the environment which would guide its behavior in the absence of external inputs.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Alnajjar, F. , Zin, I. , Hafiz, A. and Murase, K. (2013) A Tree-Type Memory Formation by Sensorimotor Feedback: A Possible Approach to the Development of Robotic Cognition. Intelligent Control and Automation, 4, 154-165. doi: 10.4236/ica.2013.42020.

References

[1] M. S. Gazzaniga, “The Cognitive Neurosciences III,” MIT Press, Cambridge, 2004.
[2] F. J. Varela, E. Thompson and E. Rosch, “The Embodied Mind: Cognitive Science and Human Experience,” MIT Press, Cambridge, 1991.
[3] A. Clark and R. Grush, “Towards a Cognitive Robotics,” Adaptive Behavior, Vol. 7, No. 1, 1999, pp. 5-16. doi:10.1177/105971239900700101
[4] R. Grush, “The Emulation Theory of Representation: Motor Control, Imagery, and Perception,” Behavioral and Brain Sciences, Vol. 27, No. 3, 2004, pp. 377-435. doi:10.1017/S0140525X04000093
[5] G. Hesslow, “Conscious Thought as Simulation of Behaviour and Perception,” Trends in Cognitive Science, Vol. 6, No. 6, 2002, pp. 242-247. doi:10.1016/S1364-6613(02)01913-7
[6] F. Lin?ker and L. Niklasson, “Extraction and Inversion of Abstract Sensory Flow Representations,” Proceedings of the 6th International Conference on Simulation of Adaptive Behavior, from Animals to Animates, Vol. 6, MIT Press, Cambridge, 2000, pp. 199-208.
[7] J. Stening, H. Jacobsson and T. Ziemke, “Imagination and Abstraction of Sensorimotor Flow: Towards a Robot Model,” In: R. Chrisley, R. Clowes and S. Torrance, Eds., Proceedings of the Symposium on Next Generation Approaches to Machine Consciousness, Hatfield, 2005, pp. 50-58.
[8] J. Stening, “Exploring Internal Simulations of Perception in a Mobile Robot Using Abstractions,” Masters Thesis, School of Humanities and Informatics, University of Sk?vde, Sweden, 2004.
[9] T. Ziemke, D. A. Jirenhed and G. Hesslow, “Internal Simulation of Perception: A Minimal Neuro-Robotic Model,” Neurocoputing, Vol. 68, 2005, pp. 85-104. doi:10.1016/j.neucom.2004.12.005
[10] F. Lin?ker and L. Niklasson, “Time Series Segmentation Using an Adaptive Resource Allocating Vector Quantization Network Based on Change Detection,” Proceedings of the International Joint Conference on Neural Networks, IEEE Computer Society, Vol. 6, 24-27 July 2000, pp. 323328.
[11] D. S. Nolfi and J. Tani, “Extracting Regularities in Space and Time through a Cascade of Prediction Networks: The Case of a Mobile Robot Navigating in a Structured Environment,” Connection Science, Vol. 11, No. 2, 1999, pp. 125-148. doi:10.1080/095400999116313
[12] D. N. Lee and J. A. I. Thompson, “Vision in Action: The Control of Locomotion,” In: D. Ingle, M. A. Goodale and R. J. W. Mansfield, Eds., Analysis of Visual Behavior, MIT Press, Cambridge, 1982, pp. 411-433.
[13] G. Hesslow, “Will Neuroscience Explain Consciousness?” Journal of Theoretical Biology, Vol. 171, No. 1, 1994, pp. 29-39. doi:10.1006/jtbi.1994.1209
[14] D. A. Jirenhed, G. Hesslow and T. Ziemke, “Exploring Internal Simulation of Perception in Mobile Robots,” In: K. Arras, C. Balkenius, A. Baerfeldt, W. Burgard and R. Siegwart, Eds., The 4th European Workshop on Advanced Mobile Robotics, Lund University Cognitive Studies, Vol. 86, Lund, 2001, pp. 107-113.
[15] T. Ziemke, D. A. Jirenhed and G. Hesslow, “Blind Adaptive Behavior Based on Internal Simulation of Perception,” Department of Computer Science, University of Sk?vde, Sweden, 2002.
[16] N. Jakobi, P. Husbands and I. Harvey, “Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics,” Proceedings of the Third European Conference on Advances in Artificial Life, Lecture Notes in Computer Science, Vol. 929, Springer Verlag, London, 1995, pp. 702-720.
[17] T. Taylor, S. Geva and W. W. Boles, “Monocular Vision as a Range Sensor,” In: M. Mohammadian, Ed., Proceedings of International Conference on Computational Intelligence for Modeling, Control and Automation, 2004, pp. 566-575.
[18] S. Schaal and C. G. Atkenson, “Robot Juggling: An Implementation of Memory-Based Learning,” Control System Magazine, Vol. 14, No. 1, 1994, pp. 57-71. doi:10.1109/37.257895
[19] F. Alnajjar, I. MohdZin and K. Murase, “A Spiking Neural Network with Dynamic Memory for a Real Autonomous Mobile Robot in Dynamic Environment,” Proceedings of International Joint Conference on Neural Networks, Hong Kong, 1-6 June 2008, pp. 2207-2213.
[20] F. Alnajjar and K. Murase, “Self Organization of Spiking Neural Network that Generates Autonomous Behavior in a Real Mobile Robot,” International Journal of Neural Systems, Vol. 16, No. 4, 2006, pp. 229-239. doi:10.1142/S0129065706000640
[21] R. Vaughan and M. Zuluaga, “Use Your Illusion Sensorimotor Self-Simulation Allows Complex Agents to Plan with Incomplete Self-Knowledge,” Proceedings of Ninth International Conference on Simulation of Adaptive Behavior, Rome, 2006, pp. 298-309.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.