Learning high-performance deep neural networks for dynamic modeling of high Degree-Of-Freedom (DOF) robots remains challenging due to the sampling complexity. Typical unknown system disturbance caused by unmodeled dynamics (such as internal compliance, cables) further exacerbates the problem. In this paper, a novel framework characterized by both high data efficiency and disturbance-adapting capability is proposed to address the problem of modeling gravitational dynamics using deep nets in feedforward gravity compensation control for high-DOF master manipulators with unknown disturbance. In particular, Feedforward Deep Neural Networks (FDNNs) are learned from both prior knowledge of an existing analytical model and observation of the robot system by Knowledge Distillation (KD). Through extensive experiments in high-DOF master manipulators with significant disturbance, we show that our method surpasses a standard Learning-from-Scratch (LfS) approach in terms of data efficiency and disturbance adaptation. Our initial feasibility study has demonstrated the potential of outperforming the analytical teacher model as the training data increases.

翻译：由于取样的复杂性,高自由度机器人动态模型的学习高性能深神经网络仍具有挑战性,因为取样复杂,未经改造的动态(如内部合规、电缆)造成的典型未知系统扰动进一步加剧了这一问题。在本文件中,提议了一个具有高数据效率和扰动适应能力的新框架,以解决在数据效率和扰动适应方面,利用深网进行数据引力动态模型模拟的问题。我们的初步可行性研究表明,随着培训数据的增加,我们的方法超过了标准的Scratch-LfS方法。我们的初步可行性研究表明,随着培训数据的增加,分析教师模型可能比分析教师模型表现得更好。