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 toward 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 out-performed the non-favoured system in its preferred task. For both tasks, a bilateral model outperforms the 'non-preferred' hand, and is as good or better than the 'preferred' hand. The Corpus Callosum tends to improve performance, but not always for the specialised models.

通过训练具有不同损失函数的半球，该研究探讨了一种用于运动任务的双侧神经网络架构作为控制系统，以实现类似于人类的半球专门化，其中协调和运动效率较好的领先体系在协调性任务上表现出色，而非领先体系在需要位置稳定性的任务上表现更好，并发现双侧模型优于单侧模型，在两项任务中都优于“非首选”手，并且不同模型之间的神经连接对于专门化的模型不一定总是改进性能。

左/右脑、人类运动控制及对机器人的影响