Model-driven reinforcement learning and action dimension extension method for efficient asymmetric assembly

Complex assembly tasks remain huge challenge for robots because the traditional control methods rely on complicated contact state analysis. Reinforcement learning (RL) becomes one of the preferred embodiments to construct the control strategy of complex tasks. In this paper, the method of model-driven RL (MDRL) is employed to construct the control strategy. Then a completely innovative action dimension extension (ADE) mechanism is proposed to further accelerate the training process of RL. The simulation and experiment results demonstrate that the control strategy obtained through combining MDRL and ADE guarantees a more compliant assembly process. Besides, ADE method will enhance the data-efficiency of RL algorithms greatly (about 30%~40%) as well as increase the stable reward.