Skip to main content
SpringerLink
Log in

Designing Neural Control Architectures for an Autonomous Robot Using Vision to Solve Complex Learning Tasks

  • Chapter
Book cover Biologically Inspired Robot Behavior Engineering

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 109))

Summary

In this chapter, we intend to present a way to design Neural Network architectures to control an autonomous mobile robot using vision as the main sensor. We start discussing the notion of autonomy. In particular we show how it constrains the learning and the architecture of the control system. We propose a set of neural tools developed to solve problems linked with autonomous learning: the Perception-Action (PerAc) architecture, the Probabilistic Conditioning Rule (PCR) and a system allowing to plan actions (integrating a system for transition learning and prediction). Illustrations on different examples (visual homing, maze problem and planning) of how the tools that has been elaborated can be assembled to form a generic control system reusable for several tasks are presented along the description of the tools. Finally, future developments and the way to integrate these works in a general cognitive science framework will be discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. Chatila, Deliberation and Reactivity in Autonomous Mobile Robots, Robotics and Autonomous System, 1995, December, 16, 2–4, 197–211

    Google Scholar 

  2. R.A. Brooks, A Robust Layered Control System for a Mobile Robot, IEEE Journal of Robotics and Automation, 1981, 40, 201–211

    Google Scholar 

  3. R. Pfeifer and P. Verschure, The Artificial Life Route to Artificial Intelligence, The challenge of autonomous systems: Pitfalls and how to avoid them, 1994, L. Steels and R. Brooks, MIT Press, Cambridge, MA

    Google Scholar 

  4. R. Pfeifer and C. Scheier, Sensory-motor coordination: the metaphor and beyond, Robotics and Autonomous Systems, R.Pfeifer and R.A. Brooks, 1996

    Google Scholar 

  5. D.C. Dennett, Consciousness Explained, 1991, Little, Brown, Boston, Massachusetts

    Google Scholar 

  6. David McFarland, Animal Robotics - From Self-sufficiency to Autonomy, From Perception to Action, IEEE, 1994, September, P. Gaussier and J.D. Nicoud, IEEE Computer Society Press, Lausanne, Switzerland

    Google Scholar 

  7. Luc Steels, When are robots intelligent autonomous agents?, Robotics and Autonomous Systems, 1995, 15, 3–9

    Article  Google Scholar 

  8. G.A. Carpenter and S. Grossberg, Invariant Pattern Recognition and Recall by an Attentive Self-Organizing ART Architecture in a Nonstationary World, Proceeding of Neural Network, 1987, 2, 737–745

    Google Scholar 

  9. P. Gaussier and S. Zrehen, Avoiding the World Model Trap: An Acting Robot Does Not Need to Be So Smart!, Robotics and Computer-Integrated Manufacturing, 1995, 11, 4, 279–286

    Google Scholar 

  10. V. Braitenberg, Vehicles: Experiments in Synthetic Psychology, 1984, MIT Press, Bradford Books, Cambridge

    Google Scholar 

  11. F. Varela and E. Thompson and E. Rosch, The Embodied Mind, 1993, MIT Press

    Google Scholar 

  12. S. Hamad, The Symbol Grounding Problem, Physica D, 1990, 42, 335–346

    Article  Google Scholar 

  13. J.R. Searle, Du cerveau au savoir, 1987, Hermann

    Google Scholar 

  14. J.A. Meyer and S.W. Wilson, From Animals to Animats, First International Conference on Simulation of Adaptive Behavior, 1991, MIT Press, Bardford Books

    Google Scholar 

  15. J. Stewart, The Implication for Understanding High-level Cognition of a Grounding in Elementary Adaptive Systems, Robotics and Autonomous Systems, 1995, December, 16, 2–4, 107–116

    Google Scholar 

  16. J. Piaget, La naissance de l’intelligence chez l’enfant, 1936, Delachaux et Niestle Editions, Neuchâtel-Paris, Geneve

    Google Scholar 

  17. I.P. Pavlov, Conditioned Reflexes, 1927, Oxford University Press

    Google Scholar 

  18. E.C. Tolman, Purposive behavior of animals and men, Irvington, 1932, New York, 189–208

    Google Scholar 

  19. E.C. Tolman, Cognitive maps in rats and men, The Psychological Review, 1948, 55, 4

    Google Scholar 

  20. P. Gaussier and S. Zrehen, PerAc: A Neural Architecture to Control Artificial Animals, Robotics and Autonomous System, 1995, December, 16, 2–4, 291–320

    Google Scholar 

  21. J.S. Albus, Outline for a Theory of Intelligence, IEEE trans. on syst. and cybern., 1991, may/june, 21, 3, 473–509

    Google Scholar 

  22. Rodney A. Brooks, A Robust Layered Control System for a Mobile Robot, IEEE Journal of Robotics and Automation, 1986, March, R.A. 2, 1, 14–23

    Google Scholar 

  23. R. Hecht-Nielsen, Counterpropagation Networks, Applied Optics, 1987, 26, 23, 4979–4984

    Article  Google Scholar 

  24. G.A. Carpenter and S. Grossberg, A massively parallel architecture for self-organizing neural pattern recognition machine, Computer Vision, Graphics, and Image Processing, 1987, 37, 54–115

    Article  MATH  Google Scholar 

  25. P. Gaussier and S. Zrehen, PerAc: A neural architecture to control artificial animals, Robotics and Autonomous Systems, 1995, 16, 2–4, 291–320

    Google Scholar 

  26. P. Gaussier and S. Zrehen, Avoiding the World Model Trap: An Acting Robot Does Not Need to Be So Smart!, Journal of Robotics and Computer-Integrated Manufacturing, 1994, 11, 4, 279–286

    Article  Google Scholar 

  27. A.G. Barto and R.S. Sutton and D.S. Brouwer, Associative search network: A reinforcement learning associative memory, Biological cybernetics, 1981, 40, 201–211

    Article  MATH  Google Scholar 

  28. A.G. Barto and R.S. Sutton and C.W. Anderson, Neuronlike adaptive elements that can solve difficult control problems, IEEE transactions on system, man and cybernetics, 1983, Sep/Oct, SMC-13, 5, 834–846

    Google Scholar 

  29. S. Mahadevan and J. Connell, Automatic programming of behavior -based robots using reinforcement learning, Ninth National Conference on Artificial Intelligence, 1991, Menlo Park, CA

    Google Scholar 

  30. J R Milian, Learning Efficient Reactive Behavioral Sequences from Basic Reflexes in a Goal-Directed Autonomous Robot, From Animals to Animats: SAB’94, 1994, D.Cliff and P. Husbands and J.A. Meyer and S.W. Wilson, 266–274

    Google Scholar 

  31. J.L. McClelland and D.E. Rumeihart and G.E. Hinton, PDP, The Appeal of Parallel Distributed Processing, 1986, MIT Press, Cambridge

    Google Scholar 

  32. U. Nehmzow and T.Smithers, Mapbuilding using self-organising networks, From Animals to Animats: SAB’91, 1991, J.A. Meyer and S. Wilson, MIT Press, Cambridge, MA

    Google Scholar 

  33. I. Krechevsky, The genesis of “hypotheses” in rats., Univ. Calif. Publ. Psychol., 1932, 6, 4, 46

    Google Scholar 

  34. M. Levine, Hypothesis Theory and Nonlearning Despite Ideal S-RReinforcement Contingencies, Psychological Review, 1971, 78, 2, 130–140

    Article  Google Scholar 

  35. T. Trabasso, Stimulus emphasis and all-or-none learning of concept identification, Journal of Experimental Psychology, 1963, 65, 395–406

    Article  Google Scholar 

  36. S.E. Weaver and A.H. Klopf and J.S. Morgan, A hierarchical network of control systems that learn: modeling nervous system function during classical and instrumental conditioning, Adaptive behavior, 1993, 1, 3, 263–319

    Article  Google Scholar 

  37. M. Levine, A Model of Hypothesis Behavior in Discrimination Learning Set, Psychological Review, 1959, 66, 6, 353–366

    Article  Google Scholar 

  38. J.P. Changeux, Neuronal Man: The Biology of Mind, 1985, Oxford University Press

    Google Scholar 

  39. G. Edelman, Neural Darwinism: The Theory of Neuronal Group Selection, 1987, Basic Books, New York

    Google Scholar 

  40. G.N. Reeke and O. Sporns and G.M. Edelman, Synthetic Neural Modeling: The “Darwin” Series of Recognition Automata, IEEE Proceedings, Special issue on Neural Networks, IEEE, 1990, September, C. Lau and B. Widrow, 1498–1530

    Google Scholar 

  41. A.G. Barto and R.S. Sutton, Landmark learning: an illustration of associative search, Biological cybernetics, 1981, 42, 1–8

    Article  MATH  Google Scholar 

  42. C. Joulain and P. Gaussier, What can robots take for free ? Learning to build visual categories from sensori-motor associations, ETIS-ENSEA, 1996

    Google Scholar 

  43. P. Gaussier and J.P. Cocquerez, Neural networks for complex scene recognition: simulation of a visual system with several cortical areas, IJCNN Baltimore, 1992, 233–259

    Google Scholar 

  44. S. Zrehen and P. Gaussier, Why topological maps are useful for learning in an autonomous agent, From perception to action conference, 1994, J.D. Nicoud and P. Gaussier, IEEE Press, Los Alamitos, CA

    Google Scholar 

  45. C.R. Gallistel, The organization of learning, 1993, MIT Press

    Google Scholar 

  46. O. Trullier and S.I. Wiener and A. Berthoz and J.A. Meyer, Biologically based artificial navigation systems: review and prospects, Progress in Neurobiology, 1997, 51, 483–544

    Article  Google Scholar 

  47. N.A. Schmajuk and A.D. Thieme, Purposive behavior and cognitive mapping: a neural network model, Biological Cybernetics, 1992, 67, 165–174

    Article  MATH  Google Scholar 

  48. G. Bugmann and J.G. Taylor and M J Denham, Neural Networks, Route finding by neural nets, Alfred Waller Ltd., 1995, J.G. Taylor, 217–230, Henley-on-Thames

    Google Scholar 

  49. R.E. Bellman, On a routing problem, Quarterly of Applied Mathematics, 1958, 16, 87–90

    MathSciNet  MATH  Google Scholar 

  50. Y. Burnod, An adaptive neural network: the cerebral cortex, 1989, Collection biologie théorique, Masson

    Google Scholar 

  51. J.P. Banquet and J.L. Contreras-Vidal and P. Gaussier and Y. Burnod, Fundamentals of neural network modelling for neuropsychologists, The corticalhippocampal system as a multirange temporal processor: A neural model, 1996, R. Park and D. Levin, MIT Press, Boston

    Google Scholar 

  52. P. Gaussier and C. Joulain and S. Zrehen and J.P. Banquet and A. Revel, Visual Navigation in an open environment without map, International Conference on Intelligent Robots and Systems–IROS’97, 1997, IEEE/RSJ, Grenoble, France, September, 545–550

    Google Scholar 

  53. C. Thinus-blanc, Animal saptial cognition, 1996, World Scientific

    Google Scholar 

  54. Alexei Samsonovich and Bruce McNaughton, Path integration and cognitive mapping in a continuous attractor neural network model, Journal of Neuroscience, 1997, 17, 5900–5920

    Google Scholar 

  55. R. Wehner and B. Michel and P. Antonsen, Visual navigation in insects: coupling of egocentric and geocentric information, Journal of Experimental Biology, 1996, 199, 129–140

    Google Scholar 

  56. A. Redish and D. Touretzky, Separating hippocampal maps, The hippocampus and parietal foundations of spatial cognition, 1999, K. Jeffery and N. Burgess and J. O’Keefe, Monte Verita, Ticino, Switzerland, Oxford University Press

    Google Scholar 

  57. A. Etienne, Mammalian Navigation, Neural Models and Biorobotics, Connection Science, 1998, 10, 3–4, 271–289

    Google Scholar 

  58. C.J.C.H. Watkins, Learning from delayed rewards, 1989, Cambridge, England, Psychology Department, Cambridge University

    Google Scholar 

  59. L. Pack Kaelbling and M.L. Littman and A.W. Moore, Reinforcement Learning: A Survey, Journal of Artificial Intelligence Research, 1996, Practice and Future of Autonomous Agents, R. Pfeifer, Monte Verita, Ticino, Switzerland

    Google Scholar 

  60. M.L. Littman, Memoryless policies: Theoritical limitations and practical results., From Animals to Animats: SAB’94, 1994, D. Cliff and P. Husbands and J.A.Meyer and S.W.Wilson, MIT Press, Cambridge, MA, 238–245

    Google Scholar 

  61. Steven D. Whitehead, Complexity and cooperation in Q-learning, Eight International Conference on Machine Learning, 1991, Morgan Kaufman, Evanston, IL, 363–367

    Google Scholar 

  62. S. Thrun and T.M. Mitchell, Lifelong Robot Learning, Robotics and Autonomous Systems, 1995, 15, 25–46

    Article  Google Scholar 

  63. I.A. Bachelor and A.M. Waxman, Mobile robot visual mapping and localization: a view-based neuro computational architecture that emulates hippocampal place learning, Neural Networks, 1994, 6 /7, 1083–1099

    Article  Google Scholar 

  64. G. Schöner and M. Dose and C. Engels, Dynamics of behavior: theory and applications for autonomous robot architectures, Robotics and Autonomous System, 1995, December, 16, 2–4, 213–245

    Google Scholar 

  65. J.Y. Donnant and J.A. Meyer, Learning reactive and planning rules in a motivationnally autonomous animat, IEEE Transactions on Systems, Man and Cybernetics-Part B, 1996, 26, 3, 381–395

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Neurocybernetic Team - ETIS ENSEA/UCP, 6, Avenue du Ponceau, 95000, Cergy-Pontoise Cedex, France

    A. Revel & P. Gaussier

Authors
  1. A. Revel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Gaussier
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, Universidade da Coruña, c/Mendizábal s/n, 15403, Ferrol (La Coruña), Spain

    Richard J. Duro

  2. Departamento de Computación, Universidade da Coruña, Campus de Elviña, 15701, La Coruña, Spain

    José Santos

  3. Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, UPV/EHU, 20018, San Sebastian, Spain

    Manuel Graña

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Revel, A., Gaussier, P. (2003). Designing Neural Control Architectures for an Autonomous Robot Using Vision to Solve Complex Learning Tasks. In: Duro, R.J., Santos, J., Graña, M. (eds) Biologically Inspired Robot Behavior Engineering. Studies in Fuzziness and Soft Computing, vol 109. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1775-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1775-1_11

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2517-6

  • Online ISBN: 978-3-7908-1775-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation