Coordinate-Independent Robot Model Identification

TL;DR

This paper introduces a coordinate-independent method for robot model identification that normalizes residuals using a Riemannian metric, leading to more accurate and physically meaningful models.

Contribution

It proposes a novel coordinate-independent identification approach using a dual metric, improving accuracy and eliminating coordinate bias in robot models.

Findings

Improved identification accuracy on Crazyflie--pendulum system.

Enhanced shape coordinate estimation in experiments.

Method remains convex and compatible with physical constraints.

Abstract

Robot model identification is commonly performed by least-squares regression on inverse dynamics, but existing formulations measure residuals directly in coordinate force space and therefore depend on the chosen coordinate chart, units, and scaling. This paper proposes a coordinate-independent identification method that weights inverse-dynamics residuals by the dual metric induced by the system Riemannian metric. Using the force--velocity vector--covector duality, the dual metric provides a physically meaningful normalization of generalized forces, pulling coordinate residuals back into the ambient mechanical space and eliminating coordinate-induced bias. The resulting objective remains convex through an affine-metric and Schur-complement reformulation, and is compatible with physical-consistency constraints and geometric regularization. Experiments on an inertia-dominated Crazyflie--pendulum system and a drag-dominated LandSalp robot show improved identification accuracy, especially on shape coordinates, in both low-data and high-data settings.