提出不依赖坐标系的机器人模型识别方法，提升精度与物理一致性。
Coordinate-Independent Robot Model Identification
- 用系统黎曼度量的对偶度量加权逆动力学残差，消除坐标系影响。
- 在低数据和高数据下均显著提升惯性主导与阻力主导系统的识别精度。
- 适合需要高物理一致性的机器人建模场景，尤其对形状坐标敏感的任务。
机器人模型识别通常通过最小二乘法对逆动力学进行回归，但现有方法在坐标力空间直接测量残差，因而依赖于选定的坐标图、单位和缩放。本文提出一种坐标无关的识别方法，利用系统黎曼度量诱导的对偶度量对逆动力学残差加权。借助力-速度向量-余向量对偶性，对偶度量为广义力提供物理意义明确的归一化，将坐标残差拉回环境机械空间，消除坐标引起的偏差。所提目标函数通过仿射度量与舒尔补重构保持凸性，兼容物理一致性约束与几何正则化。在惯性主导的Crazyflie-摆系统与阻力主导的LandSalp机器人上的实验表明，该方法在低数据与高数据设置下均显著提升识别精度，尤其在形状坐标上表现更优。
原文摘要 · Abstract (English)
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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