High force/torque (F/T) sensor calibration accuracy is crucial to achieving successful force estimation/control tasks with humanoid robots. State-of-the-art affine calibration models do not always approximate correctly the physical phenomenon of the sensor/transducer, resulting in inaccurate F/T measurements for specific applications such as thrust estimation of a jet-powered humanoid robot. This paper proposes and validates nonlinear polynomial models for F/T calibration, increasing the number of model coefficients to minimize the estimation residuals. The analysis of several models, based on the data collected from experiments with the iCub3 robot, shows a significant improvement in minimizing the force/torque estimation error when using higher-degree polynomials. In particular, when using a 4th-degree polynomial model, the Root Mean Square error (RMSE) decreased to 2.28N from the 4.58N obtained with an affine model, and the absolute error in the forces remained under 6N while it was reaching up to 16N with the affine model.

翻译：高精度力/力矩(F/T)传感器标定对于实现仿人机器人的力估计/控制任务至关重要。现有仿射标定模型无法始终准确逼近传感器/换能器的物理现象，导致在特定应用（如喷气动力仿人机器人的推力估计）中产生不精确的F/T测量结果。本文提出并验证了针对F/T标定的非线性多项式模型，通过增加模型系数数量以最小化估计残差。基于iCub3机器人实验数据的多模型分析表明，采用高次多项式可显著降低力/力矩估计误差。具体而言，当使用四阶多项式模型时，均方根误差(RMSE)从仿射模型的4.58N降至2.28N，且力的绝对误差保持在6N以下，而仿射模型该误差高达16N。