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Robot Learning pp 193-213 | Cite as

Real Robots, Real Learning Problems

  • Rodney A. Brooks
  • Maja J. Mataric
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 233)

Abstract

The weaknesses of existing learning techniques, and the variety of knowledge necessary to make a robot perform efficiently in the real world, suggest that many concurrent, complementary, and redundant learning methods are necessary. We propose a division of learning styles into four main types based on the amount of built-in structure and the type of information being learned. Using this classification, we discuss the effectiveness of various learning methodologies when applied in a real robot context.

Keywords

Mobile Robot Reinforcement Learning Real Robot Physical Robot Robot Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1993

Authors and Affiliations

  • Rodney A. Brooks
    • 1
  • Maja J. Mataric
    • 1
  1. 1.Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA

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