## 2 Related Work  
The robot assembly strategy is divided into traditional control strategy and learning-based assembly control strategy. The traditional control strategy mainly depends on position-based impedance control, which produces submissive characteristics to the contact environment via assembling objects. That could effectively reduce jam or clam in order to complete the assembly work. The learning-based assembly control strategy is mainly to obtain the optimal assembly strategy using the learning algorithm, including the parameter optimization of impedance control model. The traditional assembly strategy is that the robot uses the control algorithm to realize the assembly using the feedback information of the sensor [7]. The force control strategy is the main method of peg-in-hole assembly [8]. By analyzing the contraction state and contact force during assembly, it would reduce the contact force and torque to speed up the assembly process. [PERSON] uses impedance control with combining force error and motion error to control SCARA robots to complete the PCB assembly [9]. [PERSON] proposes a gradient-based control strategy to continuously update the robot for the smooth control of the assembly process [10]. [PERSON] and [PERSON] et al. divide peg-in-hole assembly into search and jack phases [11]. [PERSON] solves the problem of the zero value change of the force sensor through the gravity compensation system of the force sensor [12]. In order to achieve good assembly effect, the traditional control strategy requires a higher contact environment. The contact state is generally complex and dynamic [13], and all the information must be obtained by means of the accurate environmental contact model. In the actual industrial assembly scene, noise interference, environmental complexity and change could not be achieved. The application based on the learning algorithm ensures the automatic operation of assembly work. At present, the model-less intensive learning method is widely used in the manipulation task with rich contact characteristics [14; 15]. [PERSON] et al. [16] put forward a value function-based method, using the greedy strategy to select discrete assembly action with long short-term memory network (LSTM). The circular neural network estimation Q value function was used to achieve peg-in-hole assembly beyond the resolution of the robot [1]. The robot action based on the enhanced learning method of value function is discrete and low dimensional. [PERSON] et al. [17] put forward two components, the actor and the critic, to derive the robot assembly strategy and evaluate the action performed. [PERSON] et al. [18] propose a model-driven DDPG method, combined with the feedback exploration strategy, for multi-axis hole assembly. Guide policy search [15] combines track optimization models with neural network strategies to learn a wealth of operational skills. [PERSON] et al. [2] propose the MDGPS (mirror descent guided policy search) method, which does not rely on joint torque and uses force-torque signals from wrist sensors to complete the assembly of deformable and rigid objects. Stiffness control could be obtained via analyzing models. [PERSON] et al. [1] propose robotic skill acquisition methods that train push neural networks through intensive learning. However, the resulting high stiffness discrete action constrains the smoothness and safety of the assembly process. [PERSON] et al. [19] obtain the contact force error through force sensors and estimated environmental damping stiffness models online. The environmental models are combined with impedance control to achieve the expected force impedance control parameter. Under the indirect excitation of the observation signal, it is difficult to obtain accurate environmental damping parameters. [PERSON] et al. [20] propose a method of impedance control parameter regulation based on fuzzy adaptive, and verify the robot's regulation of end contact force on the PUMA560 robot.