Embodied neuromechanical chaos through homeostatic regulation

Yoonsik Shim, Phil Husbands
2019 Chaos  
In this paper we present detailed analyses of the dynamics of a number of embodied neuromechanical systems of a class that has been shown to efficiently exploit chaos in the development and learning of motor behaviors for bodies of arbitrary morphology. This class of systems has been successfully used in robotics, as well as to model biological systems. At the heart of these systems are neural central pattern generating (CPG) units connected to actuators which return proprioceptive information
more » ... ia an adaptive homeostatic mechanism. Detailed dynamical analyses of example systems, using high resolution LLE maps, demonstrate the existence of chaotic regimes within a particular region of parameter space, as well as the striking similarity of the maps for systems of varying size. Thanks to the homeostatic sensory mechanisms, any single CPG 'views' the whole of the rest of the system as if it was another CPG in a two coupled system, allowing a scale invariant conceptualization of such embodied neuromechanical systems. The analysis reveals chaos at all levels of the systems; the entire brain-body-environment system exhibits chaotic dynamics which can be exploited to power an exploration of possible motor behaviors. The crucial influence of the adaptive homeostatic mechanisms on the system dynamics is examined in detail, revealing chaotic behavior characterized by mixed mode oscillations (MMO). An analysis of the mechanism of the MMO concludes that they stems from dynamic Hopf bifurcation, where a number of slow variables act as 'moving' bifurcation parameters for the remaining part of the system. It has been known for some time that chaos is prevalent at many levels in biological motor behaviors, from neural dynamics to bodily movements 1,2 . This has inspired a number of models that can be used to shed light on the biological mechanisms involved, as well as providing a new approach in robotics. One such class of models has been shown to exploit chaotic dynamics in a powerful way, allowing goal driven exploration and learning of motor behaviors in robots with arbitrary body morphology 3,4 . Chaos is used to power a kind of search process that seeks out high performing behavior. For the first time, the dynamics of this class of system is analyzed in detail, revealing the nature of the chaos and how it can be exploited.
doi:10.1063/1.5078429 fatcat:ql3vsrtoqbcfflhwftcmq7bgye