A Framework for Learning Dynamic Movement Primitives with Deep Reinforcement Learning

Abstract

Dynamic movement primitives (DMPs) are a learning from demonstration (LfD) method that imitates movement primitives. Recently, reinforcement learning (RL) has been used to modify DMPs. In this regard, most methods have concentrated on improving DMPs with RL after DMPs are learned with supervised learning. In this way, there is no guarantee that the DMPs stay close to demonstrations while modified. To address this problem, in this study, a framework is proposed for learning DMPs with deep RL, which can be further used to modify DMPs. To do so, first, DMPs are modeled in the framework of deep RL. Then, after the reward function for learning is defined, they are learned with deep RL. The deep RL algorithm used in the present study is the twin-delayed deep deterministic policy gradient (TD3) algorithm. Our proposed algorithm is evaluated with various demonstrations, and a case study of a pick-and-place task is done. The results show that our method can learn all demonstrations with high accuracy.