Periodic motion control by modulating CPG parameters based on time-series recognition

Authors:
Toshiyuki Kondo;Koji Ito
Affiliations:
Dept. of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan;Dept. of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan
Venue:
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Year:
2005

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Abstract

This paper proposes a computational motion control model of a redundant manipulator inspired by biological brain-motor systems. The proposed model consists of two processing layers dubbed “CPG” and “Dynamical memory”. Likewise biological central pattern generators in spinal cord, the CPG layer plays a role in generating torque patterns for realizing periodic motions. On the contrary, the higher brain model, i.e. the Dynamical memory layer is a time-series pattern discriminator implemented by a recurrent neural networks (RNN). By associating time-series of the system states with optimized CPG parameters, the RNN can predictively modulate the generating torque patterns by recalling well-suited CPG parameters according to the sensorimotor time-series.