Creating and modulating rhythms by controlling the physics of the body

Abstract

The motion behaviors of vertebrates require the correct coordination of the muscles and of the body limbs even for the most stereotyped ones like the rhythmical patterns. It means that the neural circuits have to share some part of the control with the material properties and the body morphology in order to rise any of these motor synergies. To this respect, the chemical downward neuromodulators that supervise the pattern generators in the spinal cord create the conditions to merge (or to disrupt) them by matching the phase of the neural controllers to the body dynamics. In this paper, we replicate this control based on phase synchronization to implement neuromodulators and investigate the interplay between control, morphology and material. We employ this mechanism to control three robotic setups of gradual complexity and actuated by McKibben type air muscles: a single air muscle, an elbow-like system and a leg-like articulation. We show that for specific values, the control parameters modulate the internal dynamics to match those of the body and of the material physics to either the rhythmical and non-rhythmical gait patterns.

An electromyography-driven central pattern generator model for robotic control application

“…In fact, the chemical downward neuronmodulators supervise the CPG in spinal cords to match the phase of neural controllers with the body dynamics. Pitti and et al [13] replicated this control strategy based on phase synchronization as an implement of neuromodulators to employ in robot control. Ronsse and et al [14] designed an adaptive oscillator able to observe and track the frequency and phase of input signals synchronously.…”

Evolutionary Robotics and Neuroscience

“…The possibility of exploiting intrinsic chaotic dynamics has recently attracted the attention of both neurobiologists interested in how animals learn to coordinate their limbs (Kelso 1995, Korn and Faure 2003, Mpitsos et al 1988, for instance in locomotion behaviors, and roboticists striving to develop better, more efficient locomotion systems for articulated autonomous robots (Kuniyoshi and Suzuki 2004, Pitti et al 2010, Steingrube et al 2010. Chaotic dynamics emerging spontaneously from interactions between neural circuitry, bodies and environments can be used to power a kind of search process as an embodied system explores its own possible motor behaviors.…”

“…A recent strand of work has built on the growing body of observations of intrinsic chaotic dynamics in nervous systems to suggest that such dynamics can underpin crucial periods in animal development when brain-body-environment dynamics are explored in a spontaneous way as part of the process of acquiring motor skills. Recent robotics studies have demonstrated that chaotic neural networks can indeed power the self-exploration of brain-bodyenvironment dynamics in an embodied system, discovering stable patterns that can be incorporated into motor behaviors (Pitti et al 2010, Kuniyoshi andSangawa 2006). In the work outlined here we significantly generalize and extend this previous research to demonstrate how chaotic neural dynamics can be harnessed to develop a kind of system not seen in previous models: one where intrinsic neural dynamics can be used to autonomously explore, capture and learn whole goal-directed sensorimotor behaviors in an embodied system without recourse to external monitoring, evaluation or training methods.…”

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

“…Using a chaotic oscillator coupled to different robotic architectures, Pitti and collaborators (Pitti et al, 2009(Pitti et al, , 2010 explored the mechanisms underlying the control of motor synergies and showed a correspondence between synchronisation and robust behaviour. Lungarella & Sporns (2006) used different information theoretic tools to analyse the information flow in sensorimotor networks of various robotics systems, stressing the relationship between body, environment and information processing.…”