Application of a neural network to the generation of a robot arm trajectory

Shuya Imajo, Masami Konishi, Tatsushi Nishi, Jun Imai

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


We propose a neural network model generating a robot arm trajectory. The developed neural network model is based on a recurrent-type neural network (RNN) model calculating the proper arm trajectory based on data acquired by evaluation functions of human operations as the training data. A self-learning function has been added to the RNN model. The proposed method is applied to a 2-DOF robot arm, and laboratory experiments were executed to show the effectiveness of the proposed method. Through experiments, it is verified that the proposed model can reproduce the arm trajectory generated by a human. Further, the trajectory of a robot arm is successfully modified to avoid collisions with obstacles by a self-learning function.

Original languageEnglish
Pages (from-to)107-111
Number of pages5
JournalArtificial Life and Robotics
Issue number3
Publication statusPublished - Jul 1 2005


  • Learning
  • Neural network
  • Robot arm
  • Trajectory generator

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence


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