Learning with a Quadruped Chopstick Robot

  • Wei-Chung Lee
  • Jong-Chen Chen
  • Shou-zhe Wu
  • Kuo-Ming Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5632)

Abstract

Organisms exhibit a close structure-function relationship and a slight change in structure may in turn change their outputs accordingly [1]. This feature is important as it is the main reason why organisms have better malleability than computers in dealing with environmental changes. A quadruped chopstick robot controlled by a biologically-motivated neuromolecular model, named Miky, has been developed. Miky’s skeleton and its four feet were comprised of 16 deposable chopsticks, with each foot being controlled by an actuator (motor). The neuromolecular model is a multilevel neural network which captures the biological structure-function relationship and serves to transform signals sent from its sensors into a sequence of signals in space and time for controlling Miky’s feet (through actuators). The task is to teach Miky to walk, jump, pace, gallop, or make a turn. Our experimental result shows that Miky exhibits a close structure-function relationship that allows it to learn to accomplish these tasks in a continuous manner.

Keywords

Evolutionary learning Robot Neural networks Sensors 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Wei-Chung Lee
    • 1
  • Jong-Chen Chen
    • 1
  • Shou-zhe Wu
    • 1
  • Kuo-Ming Lin
    • 1
  1. 1.National Yunlin University of Science and TechnologyTaiwan, R.O.C.

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