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.
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Kondo, T., Ito, K. (2005). Periodic Motion Control by Modulating CPG Parameters Based on Time-Series Recognition. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_91
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DOI: https://doi.org/10.1007/11553090_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28848-0
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