Abstract
This paper describes the implementation of a neural network for sequence learning that is based on a neurocomputational theory of learning. The network is implemented on a physical mobile robot in order to learn to reproduce sequences of motor actions. At the onset of a conditioned stimulus the robot is presented with a sequence of visual stimuli that produce reactive motor actions of different duration. Initial results show that after learning the robot can approximate the motor sequence with no visual stimulation.
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References
Ali, K.S., and Arkin, R.C.: Implementing schema-theoretic models of animal behavior in robotic systems. In Proceedings of the fifth international workshop on advanced motion control (1998).
Arkin, R.C.: Behavior-based robotics. Intelligent robotics and autonomous agents series, (1998), MIT Press.
Baloch, A., Waxman, A.M.: Visual learning, adaptive expectations, and behavioral conditioning of the mobile robot. Neural Networks, 4, (1991) 271–302.
Brooks, R.A.: A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, (1986) 2.
Chang, C., Gaudiano, P.: Application of biological learning theories to mobile robot avoidance and approach behaviors. Journal of Complex Systems, 1(1), (1998) 79–114.
Gaudiano, P., Chang, C.: Adaptive obstacle avoidance with a neural network for operant conditioning: experiments with real robots. In Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA),(1997) 13–18 Monterey, California.
Grossberg, S.: Some networks that can learn, remember and reproduce any number of complicated space-time patterns, I. Journal of Mathematics and Mechanics, (1969) 19(1), 53–91.
Grossberg, S.: Studies of Mind and Brain: neural principles of learning, perception, development, cognition and motor control. (1982) Reidel, Boston, Massachusetts.
Grossberg, S. On the dynamics of operant conditioning. Journal of Theoretical Biology, 33, (1971) 225–255.
Grossberg, S., Levine, D. Neural dynamics of attentionally modulated Pavlovian conditioning: blocking, interstimulus interval, and secondary reinforcement. Applied Optics, 26, (1987) 5015–5030.
Lund, H.H., Webb, B., and Hallam, J.: A Robot Attracted to the Cricket Species Gryllus bimaculatus. In Proceedings of the Fourth European Conference on Artificial Life, (1997) 246–255. MIT Press.
McFarland, D.: The Oxford Companion to Animal Behavior. Oxford University Press (1981).
Şahin E., and Gaudiano P.: KITE: The Khepera Integrated Testing Environment. Proceedings of the First International Khepera Workshop. PAderborn, Germany. (1999) 199–208.
Sun, R., and Giles, C.L.: Sequence Learning: Paradigms, Algorithms, and Applications. Springer Verlag, LNAI 1828 (2001).
Verschure, P.F.M.J., Kröse, Ben J.A., Pfeifer, R.: Distributed adaptive control: The self-organization of structured behavior Robotics and Autonomous Systems, 9, (1992) 181–196.
Verschure, P.F.M.J., Voegtlin, T.: A bottom up approach towards the acquisition and expression of sequential representations applied to a behaving real-world device: distributed adaptive control III. Neural Networks, (1998) 11(7-8), 1531–1549. 1998 Special Issue on Neural Control and Robotics: Biology and Technology.
Walter, W.G.: An Imitation of Life. Scientific American, 182(5), (1950) 42–45.
Webb, B.: Using robots to model animals: a cricket test Robotics and Autonomous Systems, (1995) 16, 117–135.
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Quero, G., Chang, C. (2001). Sequence Learning in Mobile Robots Using Avalanche Neural Networks. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_61
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DOI: https://doi.org/10.1007/3-540-45723-2_61
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