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.

Abstract-- In this paper we consider the problem of allowing a humanoid robot that is subject to a persistent disturbance, in the form of a payload-carrying task, to follow given planned footsteps. MPC is augmented with terms handling the disturbance and regularizing the parameter. Finally, the effect of using the parametrization on the computational time of the controller is briefly studied. The high-level control layer typically utilizes "template" models to reason about the center of mass and feet trajectories [2], while the whole-body control layer uses the robot full model to track the adapted trajectories (see Figure 1). This paper focuses on designing a high-level trajectory adjustment controller leveraging a Figure 1: The controller highlighted in a typical multi-layer template model to allow for humanoid robots locomotion bipedal locomotion control architecture.