Before revision:  In this work we propose residual learning from demonstration (rLfD) , a framework that combines dynamic movement primitives (DMP) that rely on behavioural cloning with a reinforcement learning (RL) based residual correction policy.
After revision:  In this work we study ways for adapting dynamic movement primitives (DMP) to improve their performance in the context of contact rich insertion. We propose a framework we refer to as residual learning from demonstration (rLfD) , a framework that combines dynamic movement primitives (DMP) that rely on behavioural cloning with a reinforcement learning (RL) based residual correction policy.