Task-adaptive inertial parameter estimation of rigid-body dynamics with modeling error for model-based control using covariate shift adaptation

In this paper we consider the inertial parameter estimation problem of rigid-body dynamics with modeling errors for model-based control. Our approach focuses on the task-specific subspace, i.e., it estimates the task-adaptive inertial parameter set to be more suitable for accurately performing a given task. We present a task-adaptive inertial parameter estimation procedure using a modern statistical supervised learning framework called covariate shift adaptation equipped with a direct importance estimation method. The effectiveness of the proposed method is investigated on the trajectory tracking task with an anthropomorphic manipulator model in simulations.