Variable Admittance Control for Robotic Contact Force Tracking in Dynamic Environment Based on Reinforcement Learning

Abstract

The manipulators usually need to contact with the environment when executing the tasks. Maintaining the stability of the contact force between the manipulator end-effector and the environment is very crucial. However, constant admittance control method cannot maintain the stability of dynamic force tracking if the environment is uncalibrated. A variable admittance control algorithm based on reinforcement learning is proposed, which adjusts the damping parameter of admittance control through reinforcement learning agent. Through the simulation experiments, it is found that this method can maintain the stability of dynamic contact force tracking on a sloped surface and a sine surface when an estimation error of the environmental position exists. Compared with the traditional admittance control with constant coefficients, the adaptive admittance control algorithm performs better.