Neural computing increases robot adaptivity

• Aarts EHL and Korst JHM (1988) Simulated annealing and Boltzmann machines: A stochastic approach to combinatorial optimization and neural computing. John Wiley and Sons

• Albus JS (1975) A new approach to manipulator control: The cerebellar model articulation controller (CMAC). Transactions of the ASME. Journal of Dynamic Systems, Measurement and Control 97: 220–227

  Google Scholar 

• An G (1996) The effects of adding noise during backpropagation training on generalization performance. Neural Computation 8: 643–674

  Google Scholar 

• Arbib MA (1995) Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge, MA (An updated second edition will appear in November 2002)

  Google Scholar 

• Bäck T, Fogel DB and Michalewicz Z (eds) (1997) Handbook of Evolutionary Computation. Oxford University Press, New York, and Institute of Physics Publishing, Bristol

  Google Scholar 

• Beyer H-G and Schwefel H-P (2002) Evolution strategies-A comprehensive introduction. Natural Computing 1(1): 3–52

  Google Scholar 

• Barto AG, Sutton RS and Brouwer PS (1981) Associative Search Network: A reinforcement learning associative memory. Biological Cybernetics 40: 201–211

  Google Scholar 

• Bishop C (1995) Neural Networks for Pattern Recognition. Oxford University Press

• Bofill P, Fontdecaba E and Torras C (1995) Optimization networks for the generation of Block Designs. Journal of Artificial Neural Networks 2(4): 303–312

  Google Scholar 

• Bofill P and Torras C (2001) Neural cost functions and search strategies for the generation of Block Designs: An experimental evaluation. Intl. Journal of Neural Systems 11(2): 187–202

  Google Scholar 

• Cherkassky V (2002) Model complexity control and statistical learning theory. Natural Computing 1(1): 109–133

  Google Scholar 

• Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 2(4): 303–314

  Google Scholar 

• D'Souza A, Vijayakumar S and Schaal S (2001) Learning inverse kinematics. Proc. IEEE/RSJ Conf. on Intel, Robots and Systems, pp. 298–303. Maui, Hawaii, USA

• Dorigo M (ed) (1996) Special issue on 'Learning Autonomous Robots'. IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics 26(3)

• Espiau B, Chaumette F and Rives P (1992) A new approach to visual servoing in robotics. IEEE Trans. on Robotics and Automation 8(3): 313–326

  Google Scholar 

• Feldman JA and Ballard DH (1982) Connectionist models and their properties. Cognitive Science 6: 205–254

  Google Scholar 

• Fu KS, González RC and Lee CSG (1987) Robotics: Control, Sensing, Vision, and Intelligence. McGraw-Hill Book Company, New York

  Google Scholar 

• Funahashi K-I (1989) On the approximate realization of continuous mappings by neural networks. Neural Networks 2(3): 183–192

  Google Scholar 

• Gerstner W (1999) Spiking neurons. In: Maass W and Bishop CM (eds) Pulsed Neural Networks. MIT Press, Cambridge, MA

  Google Scholar 

• Girosi F and Poggio T (1990) Networks and the best approximation property. Biological Cybernetics 63: 169–176

  Google Scholar 

• Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA

  Google Scholar 

• Griñó R, Cembrano G and Torras C (2000) Nonlinear system identification using additive dynamic neural networks. Two on-line approaches. IEEE Trans. on Circuits and Systems I: Fundamental Theory and Applications 47(4): 150–165

  Google Scholar 

• Grossberg S (1987) Competitive learning: from interactive activation to adaptive resonance. Cognitive Science 11: 23–63

  Google Scholar 

• Gullapalli V, Barto AG and Grupen R (1994) Learning admittance mappings for force-guided assembly. Proc. IEEE Intl. Conf. on Robotics and Automation, pp. 2633–2638. IEEE Computer Society Press, Los Alamitos, CA

  Google Scholar 

• Hebb DO (1949) The Organization of Behavior. Wiley, New York

  Google Scholar 

• Hertz J, Krogh A and Palmer RG (1993) Introduction to the Theory of Neural Computation. Addison-Wesley

• Higuchi T, Iwata M and Liu W (eds) (1997) Evolvable Systems: From Biology to Hardware. Springer-Verlag, Berlin Heidelberg New York

  Google Scholar 

• Hinton GE and Anderson A (1981) Parallel Models of Associative Memory. Erlbaum, Hillsdale, NJ

  Google Scholar 

• Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proceedings National Academy of Sciences USA 79: 2554–2558

  Google Scholar 

• Hopfield JJ and Tank DW (1985) 'Neural' computation of decisions for optimization problems. Biological Cybernetics 52: 141–152

  Google Scholar 

• Hornik K, Stinchcombe M and White H (1989) Multilayer feedforward networks are universal approximators. Neural Networs 2(5): 359–366

  Google Scholar 

• Husbands P and Meyer JA (1998) Proc. 1st European Workshop on Evolutionary Robotics (EvoRobot'98). Springer-Verlag, Berlin Heidelberg New York

  Google Scholar 

• Jordan MI (1993) Computational aspects of motor control and motor learning. In: Heuer H and Keele S (eds) Handbook of Perception and Action: Motor Skills, Academic Press, New York

  Google Scholar 

• Jordan MI and Rumelhart DE (1992) Forward models: Supervised learning with a distal teacher. Cognitive Science 16: 307–354

  Google Scholar 

• Kohonen T (1988) Self-Organization and Associative Memory (second edition). Springer-Verlag, Berlin, Heidelberg, New-York, Tokyo

  Google Scholar 

• Kohonen T (2001) Self-Organizing Maps (third edition). Series in Information Sciences 30, Springer-Verlag, Berlin, Heidelberg, New York, Tokyo

  Google Scholar 

• Koza JR (1992) Genetic Programming: On Programming Computers by means of Natural Selection. MIT Press, Cambridge, MA

• Kröse BJA (ed) (1995) Special issue on 'Reinforcement Learning and Robotics'. Robotics and Autonomous Systems 15(4)

• Kröse BJA and van der Smagt PP (1993) An Introduction to Neural Networks (5th edition). University of Amsterdam

• LeCun Y (1985) Une procedure d'aprentissage pour reseau au seuil assymetrique. Proc. of COGNITIVA, pp. 599–604

• McCulloch WS and Pitts WH (1943) A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5: 115–133

  Google Scholar 

• Millán JR and Torras C (1992) A reinforcement learning connectionist approach to robot path finding in non-maze-like environments. Machine Learning 8(3/4): 363–395

  Google Scholar 

• Millán JR and Torras C (1999) Learning sensor-based navigation. In: Morik K, Kaiser M and Klingspor V (eds) Making Robots Smarter: Combining Sensing and Action through Robot Learning. Kluwer Academic Publishers, Boston, MA

  Google Scholar 

• Miller WT, Hewes RP, Glanz FH and Kraft LG (1990a) Real-time dynamic control of an industrial manipulator using a neural-network-based learning controller. IEEE Trans. on Robotics and Automation 6(1): 1–9

  Google Scholar 

• Miller WT, Sutton RS and Werbos PJ (1990b) Neural Networks for Control. MIT Press, Cambridge

  Google Scholar 

• Minsky M and Papert S (1969) Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge, MA (An expanded second edition appeared in 1988)

  Google Scholar 

• Mitchell T, Franklin J and Thrun S (1996) Recent Advances in Robot Learning. Kluwer Academic Publishers, Boston, MA

  Google Scholar 

• Miyamoto H, Kawato M, Setoyama T and Suzuki R (1988) Feedback-error-learning neural network for trajectory control of a robotic manipulator. Neural Networks 1: 251–265

  Google Scholar 

• Morik K, Kaiser M and Klingspor V (1999) Making Robots Smarter: Combining Sensing and Action through Robot Learning. Kluwer Academic Publishers, Boston, MA

  Google Scholar 

• Oja E and Kaski S (eds) (1999) Kohonen Maps. Elsevier Science, Amsterdam, Holland

  Google Scholar 

• Omidvar O and van der Smagt P (1997) Neural Systems for Robotics. Academic Press, San Diego, CA

  Google Scholar 

• Park DC, El-Sharkawi MA and Marks II RJ (1991) An adaptively trained neural network. IEEE Trans. on Neural Networks 2(3): 334–345

  Google Scholar 

• Pavlov IP (1927) Conditioned Reflexes. Oxford University Press Peterson C and Södeberg B (1989) A new method for mapping optimization problems onto neural networks. Intl. Journal of Neural Systems 1(1): 3–2

  Google Scholar 

• Ritter H, Martinetz T and Schulten K (1992) Neural Computation and Self-Organizing Maps. Addison Wesley, New York

  Google Scholar 

• Ruiz de Angulo V and Torras C (1995) On-line learning with minimum degradation in feedforward networks. IEEE Trans. on Neural Networks 6(3): 657–668

  Google Scholar 

• Ruiz de Angulo V and Torras C (1997) Self-calibration of a space robot. IEEE Trans. on Neural Networks 8(4): 951–963

  Google Scholar 

• Ruiz de Angulo V and Torras C (2000) A framework to deal with interference in connectionist systems. AI Communications 13(4): 259–274

  Google Scholar 

• Ruiz de Angulo V and Torras C (2001a) Architecture-independent approximation of functions. Neural Computation 13(5): 1119–1135

  Google Scholar 

• Ruiz de Angulo V and Torras C (2001b) Neural learning invariant to network size changes. Proc. Intl. Conf. on Artificial Neural Networks (ICANN'01), Vienna, Austria, Lecture Notes in Computer Science 2130: 33–40

  Google Scholar 

• Ruiz de Angulo V and Torras C (2002a) Learning inverse kinematics via cross-point function decomposition. Proc. Intl. Conf. on Artificial Neural Networks (ICANN-2002), Madrid, Spain, Lecture Notes in Computer Science 2415: 856–861

• Ruiz de Angulo V and Torras C (2002b) A deterministic algorithm that emulates learning with random weights. Neurocomputing 48(1–4): 975–1002

  Google Scholar 

• Rumelhart DE, Hinton GE and Williams RJ (1986) Learning representations by backpropagating errors. Letters to Nature 323: 533–535

  Google Scholar 

• Rumelhart DE and Zipser D (1985) Feature discovery by competitive learning. Cognitive Science 9: 75–112

  Google Scholar 

• Samson C, LeBorgne M and Espiau B (1990) Robot Control: The Task Function Approach. Oxford Engineering Science Series 22, Oxford Science Publications

• Schram G, van der Linden FX, Kröse BJA and Groen FCA (1996) Visual tracking of moving objects using a neural network controller. Robotics and Autonomous Systems 18: 293–299

  Google Scholar 

• Simon HA (1969) The Sciences of the Artificial. MIT Press, Cambridge, MA

  Google Scholar 

• Skinner BF (1938) The Behavior of Organisms: An Experimental Analysis. Appleton Century

• Steels L (1995) The Biology and Technology of Intelligent Autonomous Agents. NATO ASI Series F, Springer-Verlag, Berlin, Heidelberg, New York

  Google Scholar 

• Sutton RS (1988) Learning to predict by the methods of temporal differences. Machine Learning 3: 9–44

  Google Scholar 

• Sutton RS and Barto AG (1998) Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA

  Google Scholar 

• Torras C (1985) Temporal-Pattern Learning in Neural Models. Lecture Notes in Biomathematics 63, Springer-Verlag, Berlin, Heidelberg, New York

  Google Scholar 

• Torras C (1989) Sensorimotor integration in robots. In: Ewert P and Arbib MA (eds) Visuomotor Coordination: Experiments, Comparisons, Models and Robots, pp. 673–689. Plenum Press

• Torras C (1993) Symbolic planning versus neural control in robots. In: Rudomín P, Arbib MA, Cervantes-Pérez F and Romo R (eds) Neuroscience: From Neural Networks to ArtiNEURAL ficial Intelligence, Research Notes in Neural Computing 4, pp. 509–523. Springer-Verlag: Berlin, Heidelberg, New-York

  Google Scholar 

• Torras C (ed) (2001) Special issue on 'Neural Networks at IJCAI'01'. Intl. Journal of Computational Intelligence and Applications 1(4)

• van del Smagt P and Bullock D (eds) (1997) Can Artificial Cerebellar Models Compete to Control Robots? DLR Technical Report #515–97–28, German Aerospace Center

• van de Velde W (ed) (1993) Special issue on 'Towards Learning Robots'. Robotics and Autonomous Systems 8(1–2)

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

  Google Scholar 

• Wells G and Torras C (2001) Assessing image features for vision-based robot positioning. Journal of Intelligent and Robotic Systems 30(1): 95–118

  Google Scholar 

• Wells G, Venaille Ch and Torras C (1996) Vision-based robot positioning using neural networks. Image and Vision Computing 14: 715–732

  Google Scholar 

• Widrow B and Hoff ME (1960) Adaptative switching capatibility and its relation to the mechanisms of association. Kybernetik 12: 204–215

  Google Scholar 

• Ziemke T and Sharkey N (eds) (1998) Special issue on 'Biorobotics'. Connection Science 10(3–4)

  Google Scholar 

Download references