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A Self-Organizing Context-Based Approach to the Tracking of Multiple Robot Trajectories

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Abstract

We have combined competitive and Hebbian learning in a neural network designed to learn and recall complex spatiotemporal sequences. In such sequences, a particular item may occur more than once or the sequence may share states with another sequence. Processing of repeated/shared states is a hard problem that occurs very often in the domain of robotics. The proposed model consists of two groups of synaptic weights: competitive interlayer and Hebbian intralayer connections, which are responsible for encoding respectively the spatial and temporal features of the input sequence. Three additional mechanisms allow the network to deal with shared states: context units, neurons disabled from learning, and redundancy used to encode sequence states. The network operates by determining the current and the next state of the learned sequences. The model is simulated over various sets of robot trajectories in order to evaluate its storage and retrieval abilities; its sequence sampling effects; its robustness to noise and its tolerance to fault.

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References

  1. A.Y. Zomaya and T.M. Nabhan, “Trends in neuroadaptive control for robot manipulators,” in Handbook of Design, Manufacturing and Automation, edited by R.C. Dorf and A. Kusiak, John Wiley & Sons: New York, pp. 889–917, 1994.

    Google Scholar 

  2. O. Omidvar and P. van der Smagt (eds.), Neural Systems for Robotics, Academic Press: San Mateo, CA, 1997.

    Google Scholar 

  3. J.J. Craig, Introduction to Robotics: Mechanics and Control, 2nd edn., Addison-Wesley: Reading, MA, 1989.

    Google Scholar 

  4. P.C.Y. Chen, J.K. Mills, and K.C. Smith, “Performance improvement of robot continuous-path operation through iterative learning using neural networks,” Machine Learning, vol. 23, pp. 75–104, 1996.

    Google Scholar 

  5. J. Heikkonen and P. Koikkalainen, “Self-organization and autonomous robots,” in Neural Systems for Robotics, edited by O. Omidvar and P. van der Smagt, Academic Press: San Mateo, CA, pp. 297–337, 1997.

    Google Scholar 

  6. G. Bugmann, K.L. Koay, N. Barlow, M. Phillips, and D. Rodney, “Stable encoding of robot trajectories using normalised radial basis functions: Application to an autonomous wheelchair,” in Proceedings of the 29th International Symposium on Robotics (ISR'98), Birmingham, UK, 1998, pp. 232–235.

  7. D.-L.Wang and M.A. Arbib, “Timing and chunking in processing temporal order,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, pp. 993–1009, 1993.

    Google Scholar 

  8. M.J. Denham and S.L. McCabe, “Robot control using temporal sequence learning,” in Proceedings of the World Congress on Neural Networks (WCNN'95), vol. II, Washington, DC, 1995, pp. 346–349.

    Google Scholar 

  9. T. Kohonen, Self-Organizing Maps, 2nd extended edn., Springer Series in Information Sciences, vol. 30, Berlin, Heidelberg, 1997.

  10. A.F.R. AraÚjo and H. D'Arbo Jr., “Partially recurrent neural network to perform trajectory planning, inverse kinematics, and inverse dynamics,” in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, 1998, pp. 1784–1789.

  11. K. Althöfer and G. Bugmann, “Planning and learning goal directed sequences of robot arm movements,” in Proceedings of the International Conference on Artificial Neural Networks (ICANN'95), vol. 1, Paris, France, 1995, pp. 449–454.

    Google Scholar 

  12. R.N. Rao and O. Fuentes, “Learning navigational behaviors using a predictive sparse distributed memory,” in From Animals to Animats: Proceedings of the 4th International Conference on Simulation of Adaptive Behavior, Cape Cod, Massachusetts, 1996. MIT Press: Cambridge, MA, 1996, pp. 382–390.

    Google Scholar 

  13. S. Grossberg and M. Kuperstein, Neural Dynamics of Adaptive Sensory-Motor Control, Elsevier: Amsterdam, 1986.

    Google Scholar 

  14. M. Kuperstein and J. Rubistein, “Implementation of an adaptive neural controller for sensory-motor coordination,” IEEE Control Systems Magazine, vol. 9, no. 3, pp. 25–30, 1989.

    Google Scholar 

  15. T.M. Martinetz, H.J. Ritter, and K.J. Schulten, “Threedimensional neural net for learning visuomotor coordination of a robot arm,” IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 131–136, 1990.

    Google Scholar 

  16. H. Ritter, T. Martinetz, and K. Schulten, Neural Computation and Self-Organizing Maps: An Introduction. Addison-Wesley: Reading, MA, 1992.

    Google Scholar 

  17. D. Bullock and S. Grossberg, “Neural dynamics of planned arm movements: Emergent invariants and speed-accuracy properties during trajectory formation,” Psychological Review, vol. 95, pp. 49–90, 1988.

    Google Scholar 

  18. J.A. Walter and K.J. Schulten, “Implementation of selforganizing networks for visuo-motor control of an industrial robot,” IEEE Transactions on Neural Networks, vol. 4, no. 1, pp. 86–95, Jan. 1993.

    Google Scholar 

  19. D.-L. Wang and M.A. Arbib, “Complex temporal sequence learning based on short-term memory,” Proceedings of the IEEE, vol. 78, pp. 1536–1543, 1990.

    Google Scholar 

  20. M.C. Mozer, “Neural net architectures for temporal sequence processing,” in Predicting the Future and Understanding the AraÚjo and Barreto Past, edited by A. Weigend and N. Gershenfeld, Addison-Wesley: Redwood City, CA, 1993, pp. 243–264.

    Google Scholar 

  21. D.-L. Wang, “Temporal pattern processing,” in The Handbook of Brain Theory and Neural Networks, edited by M.A. Arbib, MIT Press, 1995, pp. 967–971.

  22. G. de A. Barreto and A.F.R. AraÚjo, “Time in self-organizing maps: An overview of models,” International Journal of Computer Research, vol. 10, no. 2, pp. 139–179, 2001.

    Google Scholar 

  23. A.V.M. Herz, “Spatiotemporal association in neural networks,” in The Handbook of Brain Theory and Neural Networks, edited by M.A. Arbib, MIT Press: Cambridge, MA, 1995, pp. 902–905.

    Google Scholar 

  24. D.E. Rumelhart and J.L. McClelland (eds.), Parallel Distributed Processing, vol. 1, MIT Press: Cambridge, MA, 1986.

    Google Scholar 

  25. G. de A. Barreto and A.F.R. AraÚjo, “Fast learning of robot trajectories via unsupervised neural networks,” in Proceedings of the 14th IFAC World Congress, Pergamon Press: Oxford, Beijing, China, 1999, pp. 373–378.

    Google Scholar 

  26. G. de A. Barreto and A.F.R. AraÚjo, “Unsupervised learning and recall of temporal sequences: An application to robotics,” International Journal of Neural Systems, vol. 9, no. 3, pp. 235–242, 1999.

    Google Scholar 

  27. D.L. James and R. Miikkulainen, “A self-organizing feature map for sequences,” in Advances in Neural Processing Systems, vol. 7, edited by G. Tesauro, D.S. Touretzky, and T.K. Leen, MIT Press: Cambridge, MA, 1995, pp. 577–584.

    Google Scholar 

  28. J. Hertz, A. Krogh, and R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley: Redwood City, CA, 1991.

    Google Scholar 

  29. D.O. Hebb, The Organization of Behavior, Wiley: New York, 1949.

    Google Scholar 

  30. S. Amari, “Learning patterns and pattern sequences by selforganizing nets of threshold elements,” IEEE Transactions on Computers, vol. C-21, no. 11, pp. 1197–1206, 1972.

    Google Scholar 

  31. P.I. Corke, “A robotics toolbox for MATLAB,” IEEE Robotics and Automation Magazine, vol. 3, no. 1, pp. 24–32, 1996.

    Google Scholar 

  32. A.F.R. AraÚjo and M. Vieira, “Associative memory used for trajectory generation and inverse kinematics problem,” in Proceedings of the IEEEWorld Congress on Computational Intelligence, (WCCI'98), Anchorage, USA, 1998, pp. 2052–2057.

  33. D.-L. Wang and B. Yuwono, “Incremental learning of complex temporal patterns,” IEEE Transactions on Neural Networks, vol. 7, no. 6, pp. 1465–1481, 1996.

    Google Scholar 

  34. N.B. Toomarian and J. Barhen, “Learning a trajectory using adjoint functions and teacher forcing,” Neural Networks, vol. 5, pp. 473–484, 1992.

    Google Scholar 

  35. D.-T. Lin, J.E. Dayhoff, and P.A. Ligomenides, “Trajectory production with the adaptive time-delay neural network,” Neural Networks, vol. 8, no. 3, pp. 447–461, 1995.

    Google Scholar 

  36. D.-T. Lin, “Sampling effects on trajectory production and attractor prediction,” Journal of Information Science and Engineering, vol. 13, no. 2, pp. 293–310, 1997.

    Google Scholar 

  37. P. van der Smagt, “Simderella: A robot simulator for neurocontroller design,” Neurocomputing, vol. 6, no. 2, pp. 281–285, 1994.

    Google Scholar 

  38. B. Pearlmutter, “Gradient calculations for dynamic recurrent neural networks: A survey,” IEEE Transactions on Neural Networks, vol. 6, no. 5, pp. 1212–1228, 1995.

    Google Scholar 

  39. B. Kosko, “Bidirectional associative memories,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 18, no. 1, pp. 49–60, 1988.

    Google Scholar 

  40. M.J. Healy, T.P. Caudell, and S.D. Smith, “A neural architecture for pattern sequence verification through inferencing,” IEEE Transactions on Neural Networks, vol. 4, no. 1, pp. 9–20, 1993.

    Google Scholar 

  41. M. Hagiwara, “Time-delay ART for spatio-temporal patterns,” Neurocomputing, vol. 6, no. 5/6, pp. 513–521, 1994.

    Google Scholar 

  42. S. Grossberg, “Some networks that can learn, remember, and reproduce any number of complicated space-time patterns, I,” Journal of Mathematics and Mechanics, vol. 19, pp. 53–91, 1969.

    Google Scholar 

  43. G.A. Carpenter and S. Grossberg, “Amassively parallel architecture for a self-organizing neural pattern recognition machine,” Computer Vision, Graphics, and Image Processing, vol. 37, pp. 54–115, 1987.

    Google Scholar 

  44. S. Becker, “Implicit learning in 3D object recognition: The role of temporal context,” Neural Computation, vol. 11, no. 2, pp. 347–374, 1999.

    Google Scholar 

  45. G. Tesauro, “Simple neural models of classical conditioning,” Biological Cybernetics, vol. 55, no. 4, pp. 187–200, 1986.

    Google Scholar 

  46. P.R. Montague and T.J. Sejnowski, “The predictive brain: Temporal coincidence and temporal order in synaptic learning mechanisms,” Learning & Memory, vol. 1, pp. 1–33, 1994.

    Google Scholar 

  47. G. Wallis, “Using spatio-temporal correlations to learn invariant object recognition,” Neural Networks, vol. 9, no. 9, pp. 1513–1519, 1996.

    Google Scholar 

  48. B. Schölkopf and H. Mallot, “View-based cognitive mapping and path-planning,” Adaptive Behavior, vol. 3, pp. 311–348, 1995.

    Google Scholar 

  49. M. Girolami and C. Fyfe, “A temporal model of linear anti-Hebbian learning,” Neural Processing Letters, vol. 4, no. 3, pp. 139–148, 1996.

    Google Scholar 

  50. K. Kopecz, “Unsupervised learning of sequences on maps of lateral connectivity,” in Proceedings of the International Conference on Artificial Neural Networks, Paris, 1995, pp. 431–436.

  51. H. Hyötyniemi, “Locally controlled optimization of spray painting robot trajectories,” in Proceedings of the IEEE International Workshop on Intelligent Motion Control, Istanbul, Turkey, 1990, pp. 283–287

  52. B. Fritzke, “Growing cell structures—a self-organizing network for unsupervised and supervised learning,” Neural Networks, vol. 7, no. 9, pp. 1441–1460, 1994.

    Google Scholar 

  53. N. Srinivasa and N. Ahuja, “A topological and temporal correlator network for spatiotemporal pattern learning, recognition, and recall,” IEEE Transactions on Neural Networks, vol. 10, no. 2, pp. 356–371, 1999.

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil

    Aluizio F.R. Araújo & Guilherme de A. Barreto

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  1. Aluizio F.R. Araújo
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  2. Guilherme de A. Barreto
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Araújo, A.F., Barreto, G.d.A. A Self-Organizing Context-Based Approach to the Tracking of Multiple Robot Trajectories. Applied Intelligence 17, 101–119 (2002). https://doi.org/10.1023/A:1015742406259

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