Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
2D/3D Image Data Analysis for Object Tracking and Classification Read chapter Read first chapter

2010 | OriginalPaper | Chapter

2. Robot Competence Development by Constructive Learning

Authors : Q. Meng, M. H. Lee, C. J. Hinde

Published in: Advances in Machine Learning and Data Analysis

Publisher: Springer Netherlands

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system’s adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter 2D/3D Image Data Analysis for Object Tracking and Classification
next chapter Using Digital Watermarking for Securing Next Generation Media Broadcasts
Literature
1.
Bousquet, O, Balakrishnan, K., and Honavar, V (1998). Is the hippocampus a Kalman filter? In Pacific Symposium on Biocomputing, pages 655–666, Hawaii.
2.
Hihara, S., Notoya, T., Tanaka, M., Ichinose, S., Ojima, H., Obayashi, S., Fujii, N., and Iriki, A. (2006). Extension of corticocortical afferents into the anterior bank of the intraparietal sulcus by tool-use training in adult monkeys. Neuropsychologia, 44(13):2636–2646.CrossRef
3.
Hihara, S., Obayashi, S., Tanaka, M., and Iriki, A. (2003). Rapid learning of sequential tool use by macaque monkeys. Physiology and Behavior, 78:427–434.CrossRef
4.
Huys, Quentin JM, Zemel, Richard S, Natarajan, Rama, and Dayan, Peter (2007). Fast population coding. Neural Computation, 19(2):404–441.MATHCrossRefMathSciNet
5.
Imamizu, H., Miyauchi, S., Tamada, T., Sasaki, Y., Takino, R., Puetz, B., Yoshioka, T., and Kawato, M. (2000). Human cerebellar activity reflecting an acquired internal model of a novel tool. Nature, 403:192–195.CrossRef
6.
Johnson-Frey, Scott H. (2004). The neural bases of complex tool use in humans. Trends in Cognitive Science, 8(2):71–78.CrossRef
7.
Lee, M.H., Meng, Q., and Chao, F. (2007). Developmental learning for autonomous robots. Robotics and Autonomous Systems, 55(9):750–759.CrossRef
8.
Lu, Yingwei, Sundararajan, N., and Saratchandran, P. (1998). Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Transactions on neural networks, 9(2):308–318.CrossRef
9.
Maguire, Eleanor A., Gadian, David G., Johnsrude, Ingrid S., Goodd, Catriona D., Ashburner, John, Frackowiak, Richard S. J., and Frith, Christopher D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. PNAS, 97(8):4398–4403.CrossRef
10.
Maravita, A. and Iriki, A. (2004). Tools for the body (schema). Trends in Cognitive Science, 8(2):79–86.CrossRef
11.
O’Keefe, J. (1989). Computations the hippocampus might perform. In Nadel, L., Cooper, L.A., Culicover, P., and Harnish, R.M., editors, Neural connections, mental computation. MIT Press, Cambridge, MA.
12.
Piaget, Jean (1952). The Origins of Intelligence in Children. Norton, New York, NY.CrossRef
13.
Poggio, Tomaso (1990). A theory of how the brain might work. MIT AI. memo No. 1253.
14.
Poggio, Tomaso and Girosi, Federico (1990). Networks for approximation and learning. Proceedings of the IEEE, 78(9):1481–1497.CrossRef
15.
Pouget, A. and Snyder, L.H. (2000). Computational approaches to sensorimotor transformations. Nature Neuroscience supplement, 3:1192–1198.CrossRef
16.
Quartz, S.R. and Sejnowski, T.J. (1997). The neural basis of cognitive development: A constructivist manifesto. Brain and Behavioral Sciences, 20:537–596.
17.
Rao, R. and Ballard, D. (1997). Dynamic model of visual recognition predicts neural response properties in the visual cortex. Neural Computation, 9(4):721–763.CrossRef
18.
Rao, R. and Ballard, D. (1999). Predictive coding in the visual cortex. Nature Neuroscience, 2(1):79–87.CrossRef
19.
Shultz, T.R (2006). Constructive learning in the modeling of psychological development. In Munakata, Y. and Johnson, M.H., editors, Processes of change in brain and cognitive development: Attention and performance XXI, pages 61–86. Oxford: Oxford University Press.
20.
Shultz, T.R., Mysore, S.P., and Quartz, S. R. (2007). Why let networks grow. In Mareschal, D., Sirois, S., Westermann, G., and Johnson, M.H., editors, Neuroconstructivism: Perspectives and prospects, volume 2, chapter 4, pages 65–98. Oxford: Oxford University Press.
21.
Szirtes, Gábor, Póczos, Barnabás, and Lőrincz, András (2005). Neural Kalman filter. Neurocomputing, 65–66:349–355.
22.
Todorov, E. and Jordan, M.I. (2002). Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5(11):1226–1235.CrossRef
23.
Weng, Juyang, McClelland, James, Pentland, Alex, Sporns, Olaf, Stockman, Ida, Sur, Mriganka, and Thelen, Esther (2001). Autonomous mental development by robots and animals. Science, 291(5504):599–600.CrossRef
24.
Westermann, G. and Mareschal, D. (2004). From parts to wholes: Mechanisms of development in infant visual object processing. Infancy, 5(2):131–151.CrossRef
Metadata
Title
Robot Competence Development by Constructive Learning
Authors
Q. Meng
M. H. Lee
C. J. Hinde
Copyright Year
2010
Publisher
Springer Netherlands
Book
Advances in Machine Learning and Data Analysis

Print ISBN: 978-90-481-3176-1

Electronic ISBN: 978-90-481-3177-8

Copyright Year: 2010

DOI
https://doi.org/10.1007/978-90-481-3177-8_2

Premium Partner

    Image Credits