  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.
