A Developmental Framework for Cumulative Learning Robots

• Angulo-Kinzler, R., Ulrich, B., Thelen, E. (2002). Three-month-old infants can select specific leg motor solutions. Motor Control, 6(1), 52–68.

  Google Scholar 

• Baraduc, P., Guigon, E., Burnod, Y. (2001). Recoding arm position to learn visuomotor transformations. Cerebral Cortex, 11, 906–917.

  Article  Google Scholar 

• Becker, J. D. (1973). A model for the encoding of experiental information. In R. C. Schank & K. M. Colby (Eds.), Computer models of thought and language (pp. 396–434). San Francisco: W.H. Freeman and Company.

  Google Scholar 

• Berthouze, L., & Lungarella, M. (2004). Motor skill acquisition under environmental perturbations: on the necessity of alternate freezing and freeing of degrees of freedom. Adaptive Behavior, 12(1), 47–64.

  Article  Google Scholar 

• Bodrova, E., & Leong, D. (2006). Tools of the mind: the Vygotskian approach to early childhood education. Columbus: Prentice Hall.

  Google Scholar 

• Braitenberg, V., & Schüz, A. (1991). Anatomy of the cortex: Statistics and geometry. Berlin: Springer.

  Book  Google Scholar 

• Bruner, J. (1990). Acts of meaning. Cambridge: Harvard University Press.

  Google Scholar 

• Bullock, D., & Grossberg, S. (1988). Neural dynamics of planned arm movements: emergent invariants and speed-accuracy properties during trajectory formation. Psychology Review, 95(1), 49–90.

  Article  Google Scholar 

• Caligiore, D., Ferrauto, T., Parsi, D., Accornero, N., Capozza, M., Baldassare, G. (2008). Using motor babbling and Hebb rules for modeling the development of reaching with obstacles and grasping. In Proceedings of the international conference on cognitive systems (CogSys 2008), University of Karlsruhe, Karlsruhe, Germany, April 2–4, 2008.

  Google Scholar 

• Campbell, R., & Bickhard, M. (1992). Types of constraints on development: an interactivist approach. Developmental Review, 12(3), 311–338.

  Article  Google Scholar 

• Carreira-Perpinan, M., Lister, R., Goodhill, G. (2005). A computational model for the development of multiple maps in primary visual cortex. Cerebral Cortex, 15(8), 1222–1233.

  Article  Google Scholar 

• Casey, B., Galvan, A., Hare, T. (2005). Changes in cerebral functional organization during cognitive development. Current Opinion in Neurobiology, 15(2), 239–244.

  Article  Google Scholar 

• Chao, F. (2009). Constraint lifting and its application in developmental robotics. PhD thesis, Department of Computer Science, Aberystwyth University, Wales.

  Google Scholar 

• Chao, F., Lee, M., Lee, J. (2010). A developmental algorithm for ocular motor coordination. Robotics and Autonomous Systems, 58, 239–248.

  Article  Google Scholar 

• Clifton, R., Muir, D., Ashmead, D., Clarkson, M. (1993). Is visually guided reaching in early infancy a myth? Child Development, 64(4), 1099–1110.

  Article  Google Scholar 

• Drescher, G. (1991). Made up minds: a constructivist approach to artificial intelligence. Cambridge: MIT.

  MATH  Google Scholar 

• Einarsdottir, H., Montani, F., Schultz, S. R. (2007). A mathematical model of receptive field reorganization following stroke. In IEEE 6th international conference on development and learning (pp. 211–216), Imperial College London, 11–13 July 2007, IEEE Computational Intelligence Society.

  Google Scholar 

• Foner, L., & Maes, P. (1994). Paying attention to what’s important: Using focus of attention to improve unsupervised learning. In Proceedings of the 3rd international conference on simulation of adaptive behaviour (pp. 256–265). Cambridge: MIT.

  Google Scholar 

• Gallahue, D. (1982). Understanding motor development in children. New York: Wiley.

  Google Scholar 

• Gasser, M., & Smith, L. B. (1998). Learning nouns and adjectives: a connectionist account. Language and cognitive processes, 13(2–3), 269–306.

  Article  Google Scholar 

• Gomez, G., Lungarella, M., Hotz, P. E., Matsushita, K., Pfeifer, R. (2004). Simulating development in a real robot: on the concurrent increase of sensory, motor, and neural complexity. In L. Berthouze, H. Kozima, C. G. Prince, G. Sandini, G. Stojanov, G. Metta, C. Balkenius (Eds.), Proceedings of the fourth international workshop on epigenetic robotics: modeling cognitive development in robotic systems (vol. 117, pp. 119–122). Lund University Cognitive Studies. Lund: LUCS.

  Google Scholar 

• Goodhill, G., & Xu, J. (2005). The development of retinotectal maps: a review of models based on molecular gradients. Network: Computation in Neural Systems, 16(1), 5–34.

  Article  Google Scholar 

• Hainline, L. (1998). How the visual system develops. In A. Slater (Ed.), Perceptual development: visual, auditory, and speech perception in infancy (pp. 5–50). Hove: Psychology.

  Google Scholar 

• Hendriks-Jensen, H. (1996). Catching ourselves in the act. Cambridge: MIT.

  Google Scholar 

• Hülse, M., & Lee, M. H. (2010). Adaptation of coupled sensorimotor mappings: an investigation towards developmental learning of humanoids. In S. Doncieux, B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, J.-B. Mouret (Eds.), From animals to animats 11. Lecture notes in computer science (vol. 6226, pp. 468–477). Berlin: Springer.

  Google Scholar 

• Hülse, M., McBride, S., Law, J., Lee, M. (2010a). Integration of active vision and reaching from a developmental robotics perspective. IEEE Transactions on Autonomous Mental Development, 2(4), 355–367.

  Article  Google Scholar 

• Hülse, M., McBride, S., Lee, M. (2010b). Fast learning mapping schemes for robotic hand-eye coordination. Cognitive Computation, 2(1), 1–16.

  Article  Google Scholar 

• Hülse, M., McBride, S., Lee, M. H. (2009a). Implementing inhibition of return; embodied visual memory for robotic systems. In L. Cañamero, P.-Y. Oudeyer, C. Balkenius (Eds) Proceedings of the ninth international conference on epigenetic robotics (vol. 146, pp. 213–214). Lund University Cognitive Studies. ISBN 978-91-977-380-7-1

  Google Scholar 

• Hülse, M., McBride, S., Lee, M. H. (2009b). Robotic hand-eye coordination without global reference: a biologically inspired learning scheme. In IEEE 8th international conference on development and learning, (ICDL) (pp. 1–6). New York: IEEE.

  Google Scholar 

• Johnson, M. (1990). Cortical maturation and the development of visual attention in early infancy. Journal of Cognitive Neuroscience, 2(2), 81–95.

  Article  Google Scholar 

• Kalaska, J. (1995). Reaching movements: implications of connectionist models. In M. A. Arbib (Ed.), The handbook of brain theory and neural networks (pp. 788–793). Cambridge: MIT.

  Google Scholar 

• Kalnins, I., & Bruner, J. (1973). The coordination of visual observation and instrumental behavior in early infancy. Perception, 2(3), 307–14.

  Article  Google Scholar 

• Kaplan, F., & Oudeyer, P.-Y. (2003). Motivational principles for visual know-how development. In C. G. Prince, L. Berthouze, H. Kozima, D. Bullock, G. Stojanov, C. Balkenius (Eds.), Proceedings of the third international workshop on epigenetic robotics (vol. 101, pp. 73–80). Lund University Cognitive Studies. ISBN 91-974741-X.

  Google Scholar 

• Kaplan, F., & Oudeyer, P.-Y. (2007). In search of the neural circuits of intrinsic motivation. Frontiers in Neuroscience, 1(1), 225–236.

  Article  Google Scholar 

• Keil, F. (1990). Constraints on constraints: Surveying the epigenetic landscape. Cognitive Science, 14(4), 135–168.

  Article  MathSciNet  Google Scholar 

• Law, J., Lee, M. H., Hülse, M., Tomassetti, A. (2011). The infant development timeline and its application to robot shaping. Adaptive Behaviour, 19(5), 335–358.

  Article  Google Scholar 

• Lee, M. H., & Meng, Q. (2005). Psychologically inspired sensory-motor development in early robot learning. International Journal of Advanced Robotic Systems, 2(4), 325–334.

  Google Scholar 

• Lee, M. H., Meng, Q., Chao, F. (2006). A content-neutral approach for sensory-motor learning in developmental robotics. In F. Kaplan, P.-Y Oudeyer, A. Revel, P. Gaussier, J. Nadel, L. Berthouze, H. Kozima, C.G. Prince, C. Balkenius (Eds), Proceedings of the sixth international workshop on epigenetic robotics (vol. 128, pp. 55–62), September 20–22, 2006, Paris, France, Lund University Cognitive Studies. ISBN 91-974741-6-9

  Google Scholar 

• Lee, M. H., Meng, Q., Chao, F. (2007a). Developmental learning for autonomous robots. Robotics and Autonomous Systems, 55(9), 750–759.

  Article  Google Scholar 

• Lee, M. H., Meng, Q., Chao, F. (2007b). Staged competence learning in developmental robotics. Adaptive Behaviour, 15(3), 241–255.

  Article  Google Scholar 

• Lungarella, M., & Berthouze, L. (2002). Adaptivity through physical immaturity. In C.G. Prince, Y. Demiris, Y. Marom, H. Kozima, C. Balkenius (Eds) Proceedings of the second international workshop on epigenetic robotics (vol. 94, pp. 79–86). Lund University cognitive studies, August 10–11, 2002, Edinburgh, Scotland. Studies, 94. Lund: LUCS

  Google Scholar 

• Lungarella, M., Metta, G., Pfeifer, R., Sandini, G. (2003). Developmental robotics: a survey. Connection Science, 15(4), 151–190.

  Article  Google Scholar 

• Mallot, H., Von Seelen, W., Giannakopoulos, F. (1990). Neural mapping and space-variant image processing. Neural Networks, 3(3), 245–263.

  Article  Google Scholar 

• Martinetz, T. (1993). Competitive Hebbian learning rule forms perfectly topology preserving maps. In Proceedings of the ICANN’93, International conference on artificial neural networks (vol. 93, pp. 427–434). Springer.

  Google Scholar 

• Meng, Q., & Lee, M. H. (2007). Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks. Connection Science, 19(1), 25–52.

  Article  Google Scholar 

• Meredith, M., Nemitz, J., Stein, B. (1987). Determinants of multisensory integration in superior colliculus neurons. I. Temporal factors. Journal of Neuroscience, 7(10), 3215–3229.

  Google Scholar 

• Metta, G., Craighero, L., Fadiga, L., Ijspeert, A., Rosander, K., Sandini, G., Vernon, D., von Hofsten, C. (2009). A roadmap for the development of cognitive capabilities in humanoid robots. Technical Report D2.1, University of Genoa.

  Google Scholar 

• Nakayama, K., & Silverman, G. (1986). Serial and parallel processing of visual feature conjunctions. Nature, 320(6059), 264–265.

  Article  Google Scholar 

• Neto, V., & Nehmzow, U. (2007). Visual novelty detection with automatic scale selection. Robotics and Autonomous Systems, 55(9), 693–701.

  Article  Google Scholar 

• Oudeyer, P., Kaplan, F., Hafner, V. (2007). Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation, 11(2), 265–286.

  Article  Google Scholar 

• Oztop, E., Bradley, N., Arbib, M. (2004). Infant grasp learning: a computational model. Experimental Brain Research, 158, 480–503.

  Article  Google Scholar 

• Pfeifer, R., & Scheier, C. (1997). Sensory-motor coordination: the metaphor and beyond. Robotics and Autonomous Systems, 20(2), 157–178.

  Article  Google Scholar 

• Pfeiffer, R., & Bongard, J. (2006). How the body shapes the way we think. Cambridge: MIT.

  Google Scholar 

• Piaget, J. (1973). The child’s conception of the world. London: Paladin.

  Google Scholar 

• Piek, J. P. (2002). The role of variability in early motor development. Infant Behavior and Development, 25(4), 452–465.

  Article  Google Scholar 

• Piek, J. P., & Carman, R. (1994). Developmental profiles of spontaneous movements in infants. Early Human Development, 39(2), 109–126.

  Article  Google Scholar 

• Pouget, A., & Snyder, L. (2000). Computational approaches to sensorimotor transformations. Nature Neuroscience, 3, 1192–1198.

  Article  Google Scholar 

• Prescott, T., Montes González, F., Gurney, K., Humphries, M., Redgrave, P. (2006). A robot model of the basal ganglia: behavior and intrinsic processing. Neural Networks, 19(1), 31–61.

  Article  MATH  Google Scholar 

• Prince, C., Helder, N., Hollich, G. (2005). Ongoing emergence: a core concept in epigenetic robotics. In L. Berthouze, F. Kaplan, H. Kozima, H. Yano, J. Konczak, G. Metta, J. Nadel, G. Sandini, G. Stojanov, C. Balkenius (Eds.) Proceedings of the fifth international workshop on epigenetic robotics (vol. 123, pp. 63–70). Lund University cognitive studies. ISBN 91-974741-4-2.

  Google Scholar 

• Redding, G., & Wallace, B. (2006). Generalization of prism adaptation. Journal of Experimental Psychology: Human Perception and Performance, 32(4), 1006–1022.

  Article  Google Scholar 

• Rochat, P. (2003). Five levels of self-awareness as they unfold early in life. Consciousness and Cognition, 12(4), 717–731.

  Article  Google Scholar 

• Rochat, P., & Striano, T. (1999). Emerging self-exploration by 2-month-old infants. Developmental Science, 2(2), 206–218.

  Article  Google Scholar 

• Rutkowska, J. (1994). Scaling up sensorimotor systems: constraints from human infancy. Adaptive Behaviour, 2, 349–373.

  Article  Google Scholar 

• Schmidhuber, J. (1990). Learning algorithms for networks with internal and external feedback. In Proceedings of the 1990 connectionist models summer school (pp. 52–61), San Mateo, CA.

  Google Scholar 

• Smith, L., & Gasser, M. (2005). The development of embodied cognition: six lessons from babies. Artificial Life, 11(1–2), 13–29.

  Article  Google Scholar 

• Spelke, E. (1998). Nativism, empiricism, and the origins of knowledge. Infant Behavior and Development, 21(2), 181–200.

  Article  Google Scholar 

• Sporns, O., & Edelman, G. (1993). Solving Bernstein’s problem: a proposal for the development of coordinated movement by selection. Child Development, 64(4), 960–981.

  Article  Google Scholar 

• Sterling, P. (1999). Deciphering the retina’s wiring diagram. Nature Neuroscience, 2, 851–852.

  Article  Google Scholar 

• Thelen, E. (1995). Motor development. American Psychologist, 50, 79–95.

  Article  Google Scholar 

• Thelen, E., & Whitmyer, V. (2005). Using dynamic field theory to conceptualize the interface of perception, cognition, and action. In J. Lockman & J. Rieser (Eds.), Action as an organizer of learning and development, Minnesota symposium on child psychology (vol. 33, pp. 243–277). New York: Lawrence Erlbaum Associates, Inc.

  Google Scholar 

• Tronick, E. (1972). Stimulus control and the growth of the infant’s effective visual field. Perception and Psychophysics, 11(5), 373–376.

  Article  Google Scholar 

• Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.

  Article  MathSciNet  Google Scholar 

• Ververs, I., Gelder-Hasker, V., Marja, R., De Vries, J., Hopkins, B., Van Geijn, H. (1998). Prenatal development of arm posture. Early Human Development, 51(1), 61–70.

  Article  Google Scholar 

• Westermann, G., & Mareschal, D. (2004). From parts to wholes: mechanisms of development in infant visual object processing. Infancy, 5(2), 131–151.

  Article  Google Scholar 

• Wurtz, R., & Goldberg, M. (1972). The primate superior colliculus and the shift of visual attention. Investigative Ophthalmology & Visual Science, 11(6), 441–450.

  Google Scholar 

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