Cartesian Tasks Oriented Friction Compensation Through a Reinforcement Learning Approach

The paper describes an algorithm to compensate for the friction in the robot joints, while executing a target impedance controlled Cartesian task. The proposed method relies on the reinforcement learning procedure: given a target task in the Cartesian space and a joint space friction model, the algorithm is capable to adapt the friction model parameters based on a specified error function. The proposed error function correlates the Cartesian position tracking error to the joint space friction torques, allowing to independently learn the friction parameters for each joint. In such a way, the friction model parameters can be updated in subsequent iterations, compensating for the friction effects. The proposed algorithm has been validated through experiments. A target Cartesian motion has been specified (such as in a pick and place operation) and the proposed method has allowed to learn the friction model parameters. A Universal Robot UR10 has been used as a test platform, developing the impedance control with robot dynamics compensation.