Iterative Learning Procedure with Reinforcement for High-Accuracy Force Tracking in Robotized Tasks

The paper focuses on industrial interaction robotics tasks, investigating a control approach involving multiples learning levels for training the manipulator to execute a repetitive (partially) changeable task, accurately controlling the interaction. Based on compliance control, the proposed approach consists in two main control levels: i) iterative friction learning compensation controller with reinforcement and ii) iterative force-tracking learning controller with reinforcement. The learning algorithms relies on the iterative learning and reinforcement learning procedures to automatize the controllers parameters tuning. The proposed procedure has been applied to an automotive industrial assembly task. A standard industrial UR 10 Universal Robot has been used, equipped by a compliant pneumatic gripper and a force/torque sensor at the robot end-effector.