Learning-Based Variable Compliance Control for Robotic Assembly

The assembly task is of major difficulty for manufacturing automation. Wherein the peg-in-hole problem represents a group of manipulation tasks that feature continuous motion control in both unconstrained and constrained environments, so that it requires extremely careful consideration to perform with robots. In this work, we adapt the ideas underlying the success of human to manipulation tasks, variable compliance and learning, for robotic assembly. Based on sensing the interaction between the peg and the hole, the proposed controller can switch the operation strategy between passive compliance and active regulation in continuous spaces, which outperforms the fixed compliance controllers. Experimental results show that the robot is able to learn a proper stiffness strategy along with the trajectory policy through trial and error. Further, this variable compliance policy proves robust to different initial states and it is able to generalize to more complex situation.