Learning-Based Torque Estimation for Harmonic Drive Actuators

Abstract

Accurate torque estimation in robotic actuators with harmonic drives is challenging due to nonlinear hysteresis and efficiency losses, often necessitating external torque sensors. This paper presents a learning-based torque estimation method that leverages encoder-derived features and mechanical compliance to enhance estimation accuracy without additional sensors. An actuator design incorporating a compliant helical tube provides deformation features that are effectively modeled using a Long Short-Term Memory (LSTM) network. Unlike conventional calibration or parametric approaches, the proposed framework captures nonlinear, history-dependent behaviors across varying operating conditions. Experimental evaluations demonstrate that compliant tubes significantly improve estimation accuracy compared with designs using stiffer or even rigid tubes, enabling more robust generalization under different torques, impedance modes, and stiffness levels. These results highlight the importance of co-designing actuator compliance and deep learning models to achieve reliable and compact torque estimation for harmonic drive actuators.