This paper discusses online algorithms for inverse dynamics modeling in robotics. Several model classes, including rigid body dynamics models, data-driven models and semiparametric models (which are combination of the previous two classes), are placed in a common framework. While model classes used in the literature typically exploit joint velocities and accelerations, which needs to be approximated resorting to numerical differentiation schemes, in this paper, a new “derivative-free” (DF) framework is proposed, which does not require this preprocessing step. An extensive experimental study with real data from the right arm of the iCub robot is presented, comparing different model classes and estimation procedures, showing that the proposed DF methods outperform existing methodologies.

中文翻译：

---

逆动力学模型的无导数在线学习

本文讨论了机器人逆动力学建模的在线算法。几个模型类，包括刚体动力学模型，数据驱动模型和半参数模型（它们是前两个类的组合），被放置在一个公共框架中。虽然文献中使用的模型类通常利用联合速度和加速度，这需要借助数值微分方案来近似，但在本文中，提出了一种新的“无导数”（DF）框架，该框架不需要此预处理步骤。提出了来自iCub机器人右臂的真实数据的广泛实验研究，比较了不同的模型类和估计程序，表明所提出的DF方法优于现有方法。