Coordinate-Independent Robot Model Identification

• URL: http://arxiv.org/abs/2603.14656v1
• Date: Sun, 15 Mar 2026 23:20:45 GMT
• Title: Coordinate-Independent Robot Model Identification
• Abstract summary: This paper proposes a coordinate-independent identification method that weights inverse-dynamics residuals.<n>The dual metric provides a physically meaningful normalization of generalized forces.<n>Experiments on an inertia-dominated Crazyflie--pendulum system and a drag-dominated LandSalp robot show improved identification accuracy.
• Score: 5.08128537391027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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