  Neural Network movement controllers promise a variety of advantages over
conventional control methods, however, they are not widely adopted due to their
inability to produce reliably precise movements. This research explores a
bilateral neural network architecture as a control system for motor tasks. We
aimed to achieve hemispheric specialisation similar to what is observed in
humans across different tasks; the dominant system (usually the right hand,
left hemisphere) excels at tasks involving coordination and efficiency of
movement, and the non-dominant system performs better at tasks requiring
positional stability. Specialisation was achieved by training the hemispheres
with different loss functions tailored to the expected behaviour of the
respective hemispheres. We compared bilateral models with and without
specialised hemispheres, with and without inter-hemispheric connectivity
(representing the biological Corpus Callosum), and unilateral models with and
without specialisation. The models were trained and tested on two tasks common
in the human motor control literature: the random reach task, suited to the
dominant system, a model with better coordination, and the hold position task,
suited to the non-dominant system, a model with more stable movement. Each
system outperformed the non-preferred system in its preferred task. For both
tasks, a bilateral model outperformed the non-preferred hand and was as good or
better than the preferred hand. The results suggest that the hemispheres could
collaborate on tasks or work independently to their strengths. This study
provides ideas for how a biologically inspired bilateral architecture could be
exploited for industrial motor control.
