Incremental embodied chaotic exploration of self-organized motor behaviors with proprioceptor adaptation

Shim, Yoonsik and Husbands, Phil (2015) Incremental embodied chaotic exploration of self-organized motor behaviors with proprioceptor adaptation. Frontiers in Robotics and AI, 2 (7). ISSN 2296-9144

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This paper presents a general and fully dynamic embodied artificial neural system, which incrementally explores and learns motor behaviors through an integrated combination of chaotic search and reflex learning. The former uses adaptive bifurcation to exploit the
intrinsic chaotic dynamics arising from neuro-body-environment interactions, while the latter is based around proprioceptor adaptation. The overall iterative search process formed from this combination is shown to have a close relationship to evolutionary methods. The
architecture developed here allows realtime goal-directed exploration and learning of the possible motor patterns (e.g., for locomotion) of embodied systems of arbitrary morphology. Examples of its successful application to a simple biomechanical model, a simulated swimming robot, and a simulated quadruped robot are given. The tractability of the biomechanical systems allows detailed analysis of the overall dynamics of the search process.
This analysis sheds light on the strong parallels with evolutionary search.

Item Type: Article
Keywords: embodiment, goal-directed chaotic search, self-organization, adaptive robotics, evolutionary robotics, chaotic neural dynamics, neural spinal model, proprioceptor adaptation
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Q Science > QP Physiology > QP0351 Neurophysiology and neuropsychology
Depositing User: Phil Husbands
Date Deposited: 08 Apr 2015 08:52
Last Modified: 07 Mar 2017 09:32

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Project NameSussex Project NumberFunderFunder Ref
INSIGHT-II Darwinian NeurodynamicsG1087EUROPEAN UNION308943