Coordinate-Independent Robot Model Identification
This addresses a fundamental issue in robotics for improving model accuracy, though it is incremental as it builds on existing inverse-dynamics methods.
The paper tackled the problem of robot model identification being dependent on coordinate choices, units, and scaling, by proposing a coordinate-independent method that uses a dual metric to normalize forces, resulting in improved identification accuracy on systems like a Crazyflie-pendulum and LandSalp robot.
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.