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A Soft-Computing basis for robots’ cognitive autonomous learning

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Abstract

The present work deals with development of a Soft-Computing-based intelligent system allowing to discover autonomously the surrounding world and to learn new knowledge about it by semantically interacting with human. The learning is accomplished by observation and by interaction with a human. We provide experimental validation of the proposed concept using as well simulated environment as implementing the approach on a humanoid robot in a real-world environment. We show, that our approach allows a humanoid robot to learn without negative input and from small number of samples.

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Notes

  1. Available on: http://research.microsoft.com/en-us/um/people/jiansun/salientobject/salient_object.htm.

  2. Developed by the ICL at University of Stuttgart, available online at http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger.

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Authors and Affiliations

  1. Signals, Images, and Intelligent Systems Laboratory (LISSI/EA 3956), University Paris-Est Creteil, Senart-FB Institute of Technology, 36-37 rue Charpak, 77127, Lieusaint, France

    Dominik M. Ramík, Kurosh Madani & Christophe Sabourin

Authors
  1. Dominik M. Ramík
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  2. Kurosh Madani
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  3. Christophe Sabourin
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Corresponding author

Correspondence to Kurosh Madani.

Additional information

Communicated by I.R. Ruiz.

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Ramík, D.M., Madani, K. & Sabourin, C. A Soft-Computing basis for robots’ cognitive autonomous learning. Soft Comput 19, 2407–2421 (2015). https://doi.org/10.1007/s00500-014-1495-2

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