Before revision: The deep reinforcement learning method for human-robot cooperation (HRC) is promising for its high performance when robots are learning complex tasks. However, the applicability of such an approach in a real-world context is limited due to long training time, additional training difficulty caused by inconsistent human performance and the inherent instability of policy exploration. With this approach, the robot has two dynamics to learn: how to accomplish the given physical task and how to cooperate with the human partner.
After revision: Applying Deep Reinforcement Learning (DRL) to Human-Robot Cooperation (HRC) in dynamic control problems is promising yet challenging as the robot needs to learn the dynamics of the controlled system and dynamics of the human partner.