Behavior Coordination and its Modification on Monkey-type Mobile Robot

  • Toshio Fukuda
  • Yasuhisa Hasegawa
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 109)


In this chapter, we present a motion generation and adaptation method for dynamical motion. First we introduce a self-scaling reinforcement learning algorithm for fuzzy controllers and we apply it to the control of a robot with a single actuator. We then extend the controller and the learning algorithm in order to control a robot with multiple degrees of freedom. In this case, a hierarchical behavior-based controller architecture is used to simplify the design process. Finally we introduce the Newton Raphson method so as to adjust a hierarchical behavior-based controller through an on-line learning process to some modifications in the environment or task. The methods proposed are applied to a brachiation robot control, both in numerical simulations and in real experiments. This brachiation robot has been developed to imitate a long-armed ape.


Mobile Robot Fuzzy Rule Fuzzy Controller Input Torque Feedforward Controller 
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-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Toshio Fukuda
    • 1
  • Yasuhisa Hasegawa
    • 2
  1. 1.Center for Cooperative Research in Advanced Science and TechnologyNagoya UniversityFuro-cho, Chikusa-ku, NagoyaJapan
  2. 2.Dept. of Micro System EngineeringNagoya UniversityFuro-cho, Chikusa-ku, NagoyaJapan

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