左右の脳、人間の運動制御、そしてロボティクスへの示唆
Left/Right Brain, human motor control and the implications for robotics
人間の左右脳の半球特化を模倣し、異なる損失関数で訓練した二半球ニューラルネットワーク制御器を構築。到達運動と保持運動のタスクで、二半球モデルが片手モデルを上回ることを示した。
著者: Jarrad Rinaldo, Levin Kuhlmann, Jason Friedman, Gideon Kowadlo
分類: cs.RO, cs.AI, cs.LG, cs.NE, q-bio.NC
原文アブストラクト
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.
