Deep-reinforcement learning aided dynamic parameter identification of multi-joints manipulator

To obtain more accurate dynamics equation parameters, this paper proposed a deep reinforcement learning (DRL) method for parameter identification. After using the least square (LS) method to identify the base parameters, we establish a training strategy where the friction coefficient serves as the DRL action. This strategy controls both the source and target manipulators, employing the concept of imitation learning. After using our strategy, the parameters of the target manipulator tend to converge to those of the source manipulator. In the experiment, we perform parameter identification of a 7-degree-of-freedom (DOF) manipulator in a real environment, and then identify friction coefficient for each joint based on the MuJoCo environment to theoretically validate the parameter identification using DRL. The identification results demonstrated that in a simulation environment, the use of DRL outperforms the traditional LS method, resulting in improved accuracy.