A VAGP-Based Adaptive Kalman Filter for Force Estimation of Robot

In human-robot interaction, external force measurement is fundamental to achieving robot compliance control. Parameter identification based on robot dynamics enables external force detection without expensive sensors. However, the unmodeled dynamic errors inherent in real robots pose a challenge to force estimation accuracy. In addition, existing force estimation methods often suffer from high computational dimensionality and an excessive number of tuning parameters, which limits their generalizability and migration to other platforms. In this letter, we employ a Variational Approximate Gaussian Process Regression (VAGP) model to learn the robot's dynamic errors, capturing both the mean and covariance of the error. Then we introduced the indirect measurement form and proposed a dimension-reduced Kalman filter (DRKF) to simplify the state space equation. Finally, we propose a VAGP-based adaptive Kalman filter (VAGAKF) that utilizes the least squares method to reduce the number of tuning parameters. VAGAKF effectively separates external forces from dynamics model uncertainty, reducing reliance on highly accurate robot and external force models. VAGAKF reduces average RMSE and time delay by${23.06\%}$and${66.7\%}$respectively, relative to existing methods.