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Part of the book series: Studies in Cognitive Systems ((COGS,volume 26))

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Abstract

Robots are a type of machine that we expect to solve tasks that are in many respects similar to those that confront animals and human beings. We want them to be able to perceive their environment through sensors that may include vision and touch, they should be able to move and to avoid collisions with obstacles, they should have manipulators that allow them to grasp and manipulate work pieces and, ideally, they should be able to cooperate with humans in a manner that is convenient — at least for us.

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References

  • Agre, P.E., & D. Chapman (1990). What are plans for? In P. Maes (ed.), Designing autonomous agents (pp. 17–34). Cambridge, MA: Elsevier/MIT Press.

    Google Scholar 

  • Alligood, K.T., T.D. Sauer, & J.A. Yorke (1997). Chaos. An introduction to dynamical systems. New York: Springer.

    Google Scholar 

  • Arrowsmith, D.K. (1994). An introduction to dynamical systems. Cambridge, UK: Cambridge University Press.

    MATH  Google Scholar 

  • Ballard, D. (1991). Animate vision. Artificial Intelligence 48, 57–86.

    Article  Google Scholar 

  • Borenstein, J., Everett, B., & Feng, L. (1996). Navigating mobile robots: Systems and techniques. Wellesly, MA: A.K. Peters Ltd.

    MATH  Google Scholar 

  • Brady, M. (1989). Robotics science. Cambridge, MA: MIT Press.

    Google Scholar 

  • Brady, M., J. Hollerbach, T. Johnson, T. Lozano-Pérez, & M. Mason (eds.), (1982). Robot motion: Path planning and control. Cambridge, MA: MIT Press.

    Google Scholar 

  • Braitenberg, V. (1984). Vehicles: Experiments in synthetic psychology. Cambridge, MA: MIT Press.

    Google Scholar 

  • Braun, H., & H. Ritter (2000). Introduction to Part XI: Plasticity and learning. In H. Ritter, H. Cruse, & J. Dean (eds.), Prerational intelligence: Adaptive behavior and intelligent systems without symbols and logic, Vol. 2 (pp. 589–594). Dordrecht, The Netherlands: Kluwer Academic Publishers.

    Google Scholar 

  • Brooks, R.A. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2(1), 14–23.

    Article  Google Scholar 

  • Brooks, R.A. (1989). The whole iguana. In M. Brady (ed.), Robotics science. Cambridge, MA: MIT Press.

    Google Scholar 

  • Brooks, R.A. (1990). Elephants don’t play chess. In P. Maes (ed.), Designing autonomous agents (pp. 3–16). Cambridge, MA: Elsevier/MIT Press.

    Google Scholar 

  • Brooks, R.A. (1991). Intelligence without representation. Artificial intelligence 47, 139–159.

    Article  Google Scholar 

  • Bülthoff, H., & S. Edelman (1992). Psychophysical support for a 2d view interpolation theory of object recognition. Proceedings of the National Academy of Sciences, USA, 89, 60–64.

    Article  Google Scholar 

  • Cruse, H. (1996). Neural networks as cybernetic systems. Stuttgart, Germany: Thieme Verlag.

    Google Scholar 

  • Cutzu, F., & S. Edelman (1994). Canonical views in object representation and recognition. Vision Research 34, 3037–3056.

    Article  Google Scholar 

  • Dario, P., P. Ferrante, G. Giacalone, L. Livaldi, B. Allotta, G. Buttazzo, & A.M. Sabatini (1992). Planning and executing tactile exploratory procedures. IEEE International Conference on Intelligent Robots and Systems (pp. 1896–1903). Raleigh, North Carolina.

    Chapter  Google Scholar 

  • Doyle, J.C., B.A. Francis, & A.J. Tannenbaum (1992). Feedback control theory. Prentice-Hall, NJ: Macmillan.

    Google Scholar 

  • Edelman, S., D. Reisfeld, & Y. Yeshurun (1992). Learning to recognize faces from examples. Proceedings of the 2nd European Conference on Computer Vision (S. Margherita Ligure, Italy). Springer Lecture Notes in Computer Science 588, 787–791.

    Article  Google Scholar 

  • Elfes, A. (1987). Sonar-based real-world mapping and navigation. IEEE Robotics and Automation Magazine 3, 249–265.

    Article  Google Scholar 

  • Feldman, A.G. (1991). Functional tuning of the nervous system with control of movement or maintenance of a steady posture. II: Controllable parameters of the muscles. Biophysics 11, 565–578.

    Google Scholar 

  • Fikes, R.E., & N.J. Nielsson (1971). Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2, 189–208.

    Article  MATH  Google Scholar 

  • Flash, T., & N. Hogan (1985). The coordination of arm movements: An experimentally confirmed mathematical method. Journal of Neuroscience 5 (7), 1688–1703.

    Google Scholar 

  • Fu, K.S., R.C. Gonzalez, & C.S.G. Lee (1987). Robotics: Control, sensing, vision, and intelligence. New York: McGraw-Hill.

    Google Scholar 

  • Ghanea-Hercock, R., & A.P. Fraser (1994). Evolution of autonomous robot control architectures. In T.C. Fogarty (ed.), Lecture Notes in Computer Science 865: Evolutionary Computing. Heidelberg: Springer.

    Google Scholar 

  • Hager, G., & M. Mintz (1991). Computational methods for task-directed sensor data fusion and sensor planning. International Journal of Robotics Research 10, 285–313.

    Article  Google Scholar 

  • Hager, G., & Hutchinson, S. (eds.), (1996). IEEE Robotics and Automation. Special issue on visual servoing.

    Google Scholar 

  • Heidemann, G., & H. Ritter (1996). A neural 3d-object recognition architecture using optimized gabor filters. Proceedings of the 13th International Conference on Pattern Recognition (pp. 70–74). Wien: IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Hollerbach, J.M. (1980). A recursive lagrangian formulation of manipulator dynamics and a comparative study of dynamics formulation complexity. IEEE Transactions on Systems, Man and Cybernetics SMC-10, 730–736.

    Article  MathSciNet  Google Scholar 

  • Horn, B.K. (1977). Understanding image intensities. Artificial Intelligence 8, 201–231.

    Article  MATH  Google Scholar 

  • Hurlbert, A., & T. Poggio (1988). Making machines (and artificial intelligence) see. In S.R. Graubard (ed.), The artificial intelligence debateFalse starts, real foundations (pp. 213–240). Cambridge, MA: MIT Press.

    Google Scholar 

  • Jacobs, O.L.R. (1993). Introduction to control theory. Oxford, UK: Oxford Science Publications.

    MATH  Google Scholar 

  • Johansson, R.S. (1996). Sensory control of dextrous manipulation in humans. In A.M. Wing, P. Haggard, & J.R. Flanagan (eds.), Hand and brain (pp. 381–414) New York: Academic Press.

    Chapter  Google Scholar 

  • Kandel, E., & J. Schwartz (1985). Principles of neural science. New York: Elsevier.

    Google Scholar 

  • Kawato, M. (1990). Computational schemes and neural network models for formation and control of multijoint arm trajectory. In W.T. Miller, R.S. Sutton, & P.J. Werbos (eds.), Neural networks for control (pp. 197–228). Cambridge, MA: MIT Press.

    Google Scholar 

  • Khatib, O. (1991). Real-time obstacle avoidance for manipulators and mobile robots. In S.S. Lyengar & A. Elfes (eds.), Autonomous mobile robots: Perception, mapping and navigation (Vol. 1) (pp.428–436). Los Alamitos, CA: IEEE Computer Society Press.

    Google Scholar 

  • Khosla, P., & R. Volpe (1988). Superquadric artifical potentials for obstacle avoidance and approach. Proceedings of the 1988 IEEE International Conference on Robotics and Automation (pp. 1778–1784). Philadelphia, PA.

    Chapter  Google Scholar 

  • Kindermann, T., H. Cruse, & Ch. Bartling (1995). High-pass filtered positive feedback: decentralized control of cooperation. In P. Chacon, F. Moran, A. Moreno, & J.J. Merelo (eds.), Advances in artificial life (pp. 668–678). Heidelberg: Springer.

    Google Scholar 

  • Koza, J.R. (1992). Evolution of subsumption using genetic programming. In F.J. Varela & P. Bourgine (eds.), Proceedings of the 1st European Conference on Artificial Life (pp. 110–119). Cambridge, MA: MIT Press.

    Google Scholar 

  • Kurz, A. (1992). Building maps for path planning and navigation using learning classification of external sensor data. In I. Alexander & J. Taylor (ed.), Artificial neural networks2 (pp. 587–590). Amsterdam: Elsevier.

    Google Scholar 

  • Littmann, E., A. Drees, & H. Ritter (1996). Visual gesture-based robot guidance with a modular neural system. In D. Touretzky, M. Mozer, & M. Hasselmo (eds.), Advances in neural information processing systems8 (pp. 903–909). Cambridge, MA: MIT Press.

    Google Scholar 

  • Logothetis, N.K., J. Pauls, H.H. Bülthoff, & T. Poggio (1994). View-dependent object recognition by monkeys. Current Opinion in Biology 4, 401–414.

    Google Scholar 

  • Lozano-Perez, T. (1987). A simple motion-planning algorithm for general robot manipulators. IEEE Journal of Robotics and Automation RA-3(3), 224–238.

    Article  Google Scholar 

  • Lozano-Perez, T., J.L. Jones, E. Mazer, & P.A. O’Donnell (1989). Task-level planning of pick-and-place robot motions. Computer 22(3), 21–29.

    Article  Google Scholar 

  • Maes, P. (ed.), (1991). Designing autonomous agents. Cambridge, MA: Elsevier/MIT Press.

    Google Scholar 

  • Marr, D. (1982). Vision. San Francisco, CA: Freeman.

    Google Scholar 

  • Massone, L., & E. Bizzi (1989). A neural network model for limb trajectory formation. Biological Cybernetics 61, 417–425.

    Article  Google Scholar 

  • Murase, H., & S. Nayar (1995).Visual learning and recognition of 3d-objects from appearance. Internationaljournal of Computer Vision 14, 5–24.

    Article  Google Scholar 

  • Narendra, K.S., & K. Parthasarathy (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1, 4–27.

    Article  Google Scholar 

  • Nashner, L.M. (1976). Adapting reflexes controlling the human posture. Journal for Experimental Brain Research 26, 59–72.

    Google Scholar 

  • Nayar, S.K., & T. Poggio (1996). Early visual learning. Oxford, UK: Oxford University Press.

    MATH  Google Scholar 

  • Nölker, C., & H. Ritter (1998). Illumination independent recognition of deictic arm postures. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (pp. 2006–2011). Aachen, Germany: IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Nordin, P., & W. Banzhaf (1997). An on-line method to evolve behavior and to control a miniature robot in real time with genetic programming. Adaptive Behavior 5, 107–140.

    Article  Google Scholar 

  • Paul, R. (1981). Robot manipulators: Mathematics, programming, and control. Cambridge, MA: MIT Press.

    Google Scholar 

  • Poggio, T., V Torre, & C. Koch (1985). Computational vision and regularization therory. Nature 317(6035), 314–319.

    Article  Google Scholar 

  • Richards, W. (ed.), (1988). Natural computation. Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  • Ritter, H. Cruse, & J. Dean (eds.), (2000). Introduction to Part IX: Navigation. In H. Ritter, H. Cruse, & J. Dean (eds.), Prerational intelligence: Adaptive behavior and intelligent systems without symbols and logic, Vol. 2 (pp. 361–365). Dordrecht, The Netherlands: Kluwer Academic Publishers.

    Google Scholar 

  • Roberts, L.G. (1965). Machine perception of three-dimensional solids. In J.P. Tippet (ed.), Optical and electro-optical information processing (pp. 159–197).

    Google Scholar 

  • Reprinted in J.K. Aggarwal, R.D. Duda, & A. Rosenfeld (eds.) (1977), Computer methods in image analysis (pp. 285–323). Cambridge, MA: MIT Press.

    Google Scholar 

  • Salganicoff, M., M. Rucci, & R. Bajcsy (1996). Unsupervised visuo-tactile learning for control of manipulation. In S.K. Nayar & T. Poggio (eds.), Early visual learning (pp. 329–362). Oxford, UK: Oxford University Press.

    Google Scholar 

  • Schuster, H.G. (1988). Deterministic chaos. Weinheim, Germany: Verlagsgesellschaft.

    Google Scholar 

  • Shadmehr, R. (1995). Equilibrium point hypothesis. In M. Arbib (ed.), The handbook of brain theory and neural networks (pp. 370–372). Cambridge, MA: MIT Press.

    Google Scholar 

  • Shadmehr, R., F.A. Mussa-Ivaldi, & E. Bizzi (1993). Postural force fields of the human arm and their role in generating multi-joint arm movements. Journal of Neuroscience 13, 45–63.

    Google Scholar 

  • Thrun, S. (1998). Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence 99, 21–71.

    Article  MATH  Google Scholar 

  • Utkin, V. (1992). Sliding modes in control optimization. Berlin: Springer.

    Book  MATH  Google Scholar 

  • Verschure, P.F.J.M., B. Kröse, & R. Pfeifer (1992). Distributed adaptive control: The self-organization of structured behavior. Robotics and Autonomous Systems 9, 247–265.

    Article  Google Scholar 

  • Walker, M.W., & D.E. Orin (1982). Efficient dynamic computer simulation of robotic mechanisms. Journal of Dynamic Systems, Measurement and Control 104,205–211.

    Article  MATH  Google Scholar 

  • Walter, J., & H. Ritter (1996). Rapid learning with parametrized self-organizing maps. Neurocomputing 12, 131–153.

    Article  MATH  Google Scholar 

  • Waltz, D.I. (1972). Generating semantic descriptions from drawings of scenes with shadows. PhD thesis, AI Lab. Cambridge, MA: MIT Press.

    Google Scholar 

  • Weng, J.J., N. Ahuja, & T.S. Huang (1993). Learning recognition and segmentation of 3d objects from 2d images. Proceedings of the International Conference on Computer Vision (pp. 121–128). Berlin: IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

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Ritter, H. (2000). Prerational Intelligence from the Perspectives of Robotics and Engineering. In: Cruse, H., Dean, J., Ritter, H. (eds) Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic, Volume 1, Volume 2 Prerational Intelligence: Interdisciplinary Perspectives on the Behavior of Natural and Artificial Systems, Volume 3. Studies in Cognitive Systems, vol 26. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0870-9_81

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  • DOI: https://doi.org/10.1007/978-94-010-0870-9_81

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