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Learning and Fast Object Recognition in Robot Skill Acquisition: A New Method

  • I. Lopez-Juarez
  • R. Rios-Cabrera
  • M. Peña-Cabrera
  • R. Osorio-Comparan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)

Abstract

Invariant object recognition aims at recognising an object independently of its position, scale and orientation. This is important in robot skill acquisition during grasping operations especially when working in unstructured environments. In this paper we present an approach to aid the learning of manipulative skills on-line. We introduce and approach based on an ANN for object learning and recognition using a descriptive vector built on recurrent patterns. Experimental learning results using a fast camera are presented. Some simple parts (i.e. circular, squared and radiused-square) were used for comparing different connectionist models (Backpropagation, Perceptron and FuzzyARTMAP) and to select the appropriate model. Later during experiments, complex figures were learned using the chosen FuzzyARTMAP algorithm showing a 93.8% overall efficiency and 100% recognition rate with not so complex parts. Recognition times were lower than 1 ms, which clearly indicates the suitability of the approach to be implemented in robotic real-world operations.

Keywords

ART Theory Artificial Neural Networks Invariant Object Recognition Machine Vision Robotics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • I. Lopez-Juarez
    • 1
  • R. Rios-Cabrera
    • 1
  • M. Peña-Cabrera
    • 2
  • R. Osorio-Comparan
    • 2
  1. 1.Centro de Investigación y de Estudios Avanzados del I.P.N. (CINVESTAV)Ramos ArizpeMéxico
  2. 2.Instituto de Investigaciones en Matemáticas Aplicadas y en SistemasUniversidad Nacional Autónoma de México (UNAM)México

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