In this paper, we propose a novel kernel for the identification of the inverse dynamics of
robotic manipulators based on Gaussian Process Regression. The proposed kernel, called …

In this paper, we propose a black-box model based on Gaussian process regression for the
identification of the inverse dynamics of robotic manipulators. The proposed model relies on …

In recent years, black-box estimators for robot inverse dynamics have drawn the attention of
the robotics community. This paper compares two recent black-box approaches that try to …

In this letter, we introduce a novel data-driven inverse dynamics estimator based on
Gaussian Process Regression. Driven by the fact that the inverse dynamics can be …

The Geometrically Inspired Polynomial Kernel (GIP)[1] has been recently proposed in the
context of black box inverse dynamics estimation based on Gaussian Processes, driven by …

In this extended abstract we present two recent contributions in the context of Physics
Informed black-box inverse dynamics identification using Gaussian Processes (GPs). The …

Abstract The Lagrangian Inspired Polynomial (LIP) estimator (Giacomuzzo et al., 2023) is a
black-box estimator based on Gaussian Process Regression, recently presented for the …

In recent years machine learning techniques have received an increasing interest, since
they can be successfully applied to several application domains, among which robotics. In …

Robotics systems are becoming more and more autonomous and reconfigurable. In this
context, the design of algorithms capable of deriving kinematics and dynamics models …

This paper shows the experimental identification of the inverse dynamics model of a KUKA
iiwa lightweight robot. We use experimental data from optimal identification experiments to …