Friction-Aware Actuator Modeling for Accurate Torque Estimation Using External Sensors

Abstract

Modern robotic controllers are typically designed in simulation and subsequently deployed on real robots. However, discrepancies between simulated and actual actuator torque often lead to sim-to-real (sim2real) problems. Various actuator approaches have been proposed to address this problem, but when external torque sensors are used, it is difficult to measure the intrinsic actuator output torque due to disturbances from external load systems. This paper proposes an actuator modeling method that minimizes the influence of external systems. The friction torque of the actuator is first identified under no-load conditions, and the measured torque under loaded conditions is compensated accordingly to estimate the pure output torque. Experimental results across various actuators and load conditions demonstrate that the proposed model closely matches the measured torque, even in actuators with large friction. The proposed approach overcomes the modeling limitation using external sensors and provides an effective solution for reducing sim2real problems in diverse actuator systems.