Chaotic exploration and learning of locomotor behaviours

Shim, Yoonsik (2013) Chaotic exploration and learning of locomotor behaviours. Doctoral thesis (PhD), University of Sussex.

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Recent developments in the embodied approach to understanding the generation of
adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic
motor patterns should not be done in isolation from an appreciation, and indeed
exploitation, of neural-body-environment interactions. Utilising spontaneous mutual
entrainment between neural systems and physical bodies provides a useful passage
to the regions of phase space which are naturally structured by the neuralbody-
environmental interactions. A growing body of work has provided evidence
that chaotic dynamics can be useful in allowing embodied systems to spontaneously
explore potentially useful motor patterns. However, up until now there has
been no general integrated neural system that allows goal-directed, online, realtime
exploration and capture of motor patterns without recourse to external monitoring,
evaluation or training methods. For the first time, we introduce such a system
in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics,
for the exploration and learning of the possible locomotion patterns of an articulated
robot of an arbitrary morphology in an unknown environment. The controller
is modelled as a network of neural oscillators which are coupled only through physical
embodiment, and goal directed exploration of coordinated motor patterns is
achieved by a chaotic search using adaptive bifurcation. The phase space of the
indirectly coupled neural-body-environment system contains multiple transient or
permanent self-organised dynamics each of which is a candidate for a locomotion
behaviour. The adaptive bifurcation enables the system orbit to wander through
various phase-coordinated states using its intrinsic chaotic dynamics as a driving
force and stabilises the system on to one of the states matching the given goal
criteria. In order to improve the sustainability of useful transient patterns, sensory
homeostasis has been introduced which results in an increased diversity of motor outputs,
thus achieving multi-scale exploration. A rhythmic pattern discovered by this
process is memorised and sustained by changing the wiring between initially disconnected
oscillators using an adaptive synchronisation method. The dynamical nature
of the weak coupling through physical embodiment allows this adaptive weight learning
to be easily integrated, thus forming a continuous exploration-learning system.
Our result shows that the novel neuro-robotic system is able to create and learn a
number of emergent locomotion behaviours for a wide range of body configurations
and physical environment, and can re-adapt after sustaining damage. The implications
and analyses of these results for investigating the generality and limitations of
the proposed system are discussed.

Item Type: Thesis (Doctoral)
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QP Physiology > QP0351 Neurophysiology and neuropsychology
Depositing User: Library Cataloguing
Date Deposited: 17 Apr 2013 08:39
Last Modified: 08 Sep 2015 14:40

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