強化学習による多指ハンドの低次元制御を実現するシナジーの獲得

抄録

We propose a reinforcement learning platform to learn to perform various tasks with a robotic hand to acquire a synergy. The model of deep reinforcement learning is trained to grasp an object with a multi-fingered hand. The synergy space is calculated by principal component analysis of hand postures when the task is successfully executed. The reward system is designed to minimize the distance of orthogonal projection between the posture and the synergy space, and the synergy space is acquired simultaneously with reinforcement learning.