A simple mechanical system for studying adaptive oscillatory neural networks

Central Pattern Generators (CPG) are oscillatory
systems that are responsible for generating rhythmic patterns
at the origin of many biological activities such as for example
locomotion or digestion. These systems are generally modelled
as recurrent neural networks whose parameters are tuned
so that the network oscillates in a suitable way, this tuning
being a non trivial task. It also appears that the link with
the physical body that these oscillatory entities control has a
fundamental importance, and it seems that most bodies used
for experimental validation in the literature (walking robots,
lamprey model, etc.) might be too complex to study. In this
paper, we use a comparatively simple mechanical system,
the nonholonomic vehicle referred to as the Roller-Racer, as
a means towards testing different learning strategies for an
Recurrent Neural Network-based (RNN) controller/guidance
system. After a brief description of the Roller-Racer, we
present as a preliminary study an RNN-based feed-forward
controller whose parameters are obtained through the
well-known teacher forcing learning algorithm, extended to
learn signals with a continuous component.