Recent work has shed light on the often unreliable performance of constrained least-squares estimation methods for robot mass-inertial parameter identification, particularly for high degree-of-freedom systems subject to noisy and incomplete measurements. Instead, differential geometric identification methods have proven to be significantly more accurate and robust. These methods account for the fact that the mass-inertial parameters reside in a curved Riemannian space, and allow perturbations in the mass-inertial properties to be measured in a coordinate-invariant manner. Yet, a continued drawback of existing geometric methods is that the corresponding optimization problems are inherently nonconvex, have numerous local minima, and are computationally highly intensive to solve. In this paper, we propose a convex formulation under the same coordinate-invariant Riemannian geometric framework that directly addresses these and other deficiencies of the geometric approach. Our convex formulation leads to a globally optimal solution, reduced computations, faster and more reliable convergence, and easy inclusion of additional convex constraints. The main idea behind our approach is an entropic divergence measure that allows for the convex regularization of the inertial parameter identification problem. Extensive experiments with the 3-DoF MIT Cheetah leg, the 7-DoF AMBIDEX tendon-driven arm, and a 16-link articulated human model show markedly improved robustness and generalizability vis-à-vis existing vector space methods while ensuring fast, guaranteed convergence to the global solution.

中文翻译：

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几何机器人动态识别：凸规划方法

最近的工作揭示了用于机器人质量惯性参数识别的约束最小二乘估计方法通常不可靠的性能，特别是对于受噪声和不完整测量影响的高自由度系统。相反，差分几何识别方法已被证明更加准确和稳健。这些方法解释了质量惯性参数位于弯曲黎曼空间中的事实，并允许以坐标不变的方式测量质量惯性属性的扰动。然而，现有几何方法的一个持续缺点是相应的优化问题本质上是非凸的，具有许多局部最小值，并且需要大量的计算来解决。在本文中，我们在相同的坐标不变黎曼几何框架下提出了一个凸公式，它直接解决了几何方法的这些和其他缺陷。我们的凸公式导致全局最优解，减少计算，更快和更可靠的收敛，并容易包含额外的凸约束。我们的方法背后的主要思想是熵发散度量，它允许惯性参数识别问题的凸正则化。对 3-DoF MIT Cheetah 腿、7-DoF AMBIDEX 肌腱驱动臂和 16 连杆关节人体模型进行的大量实验表明，与现有矢量空间方法相比，鲁棒性和泛化性显着提高，同时确保快速、有保证的收敛到全局解决方案。