In this paper, we propose to estimate the forward dynamics equations of mechanical systems by learning a model of the inverse dynamics and estimating individual dynamics components from it. We revisit the classical formulation of rigid body dynamics in order to extrapolate the physical dynamical components, such as inertial and gravitational components, from an inverse dynamics model. After estimating the dynamical components, the forward dynamics can be computed in closed form as a function of the learned inverse dynamics. We tested the proposed method with several machine learning models based on Gaussian Process Regression and compared them with the standard approach of learning the forward dynamics directly. Results on two simulated robotic manipulators, a PANDA Franka Emika and a UR10, show the effectiveness of the proposed method in learning the forward dynamics, both in terms of accuracy as well as in opening the possibility of using more structured~models.

翻译：本文提出通过学习逆动力学模型并从中估计各个动力学分量，来估计机械系统的前向动力学方程。我们重新审视刚体动力学的经典公式，以便从逆动力学模型中推断物理动力学分量，如惯性分量和重力分量。在估计出动力学分量后，前向动力学可以作为所学逆动力学的一个闭式函数进行计算。我们使用基于高斯过程回归的多种机器学习模型测试了所提方法，并将其与直接学习前向动力学的标准方法进行了比较。在两个模拟机器人操作臂（PANDA Franka Emika 和 UR10）上的实验结果表明，所提方法在学习前向动力学方面具有有效性，不仅在准确性上表现出色，而且为使用更具结构化的模型提供了可能性。