  A reinforcement learning (RL) based method that enables the robot to
accomplish the assembly-type task with safety regulations is proposed. The
overall strategy consists of grasping and assembly, and this paper mainly
considers the assembly strategy. Force feedback is used instead of visual
feedback to perceive the shape and direction of the hole in this paper.
Furthermore, since the emergency stop is triggered when the force output is too
large, a force-based dynamic safety lock (DSL) is proposed to limit the
pressing force of the robot. Finally, we train and test the robot model with a
simulator and build ablation experiments to illustrate the effectiveness of our
method. The models are independently tested 500 times in the simulator, and we
get an 88.57% success rate with a 4mm gap. These models are transferred to the
real world and deployed on a real robot. We conducted independent tests and
obtained a 79.63% success rate with a 4mm gap. Simulation environments:
https://github.com/0707yiliu/peg-in-hole-with-RL.
