In the Fourth Industrial Revolution, wherein artificial intelligence and the automation of machines occupy a central role, the deployment of robots is indispensable. However, the manufacturing process using robots, especially in collaboration with humans, is highly intricate. In particular, modeling the friction torque in robotic joints is a longstanding problem due to the lack of a good mathematical description. This motivates the usage of data-driven methods in recent works. However, model-based and data-driven models often exhibit limitations in their ability to generalize beyond the specific dynamics they were trained on, as we demonstrate in this paper. To address this challenge, we introduce a novel approach based on residual learning, which aims to adapt an existing friction model to new dynamics using as little data as possible. We validate our approach by training a base neural network on a symmetric friction data set to learn an accurate relation between the velocity and the friction torque. Subsequently, to adapt to more complex asymmetric settings, we train a second network on a small dataset, focusing on predicting the residual of the initial network’s output. By combining the output of both networks in a suitable manner, our proposed estimator outperforms the conventional model-based approach, an extended LuGre model, and the base neural network significantly. Furthermore, we evaluate our method on trajectories involving external loads and still observe a substantial improvement, approximately 60%–70%, over the conventional approach. Our method does not rely on data with external load during training, eliminating the need for external torque sensors. This demonstrates the generalization capability of our approach, even with a small amount of data – less than a minute – enabling adaptation to diverse scenarios based on prior knowledge about friction in different settings.

中文翻译：

---

基于学习的机器人摩擦模型自适应

在人工智能和机器自动化占据核心地位的第四次工业革命中，机器人的部署是不可或缺的。然而，使用机器人的制造过程，尤其是与人类协作的制造过程，非常复杂。特别是，由于缺乏良好的数学描述，对机器人关节中的摩擦扭矩进行建模是一个长期存在的问题。这促使在最近的工作中使用数据驱动的方法。然而，正如我们在本文中所证明的那样，基于模型和数据驱动的模型在泛化超出其所训练的特定动态的能力方面通常表现出局限性。为了应对这一挑战，我们引入了一种基于残差学习的新方法，其目的是使用尽可能少的数据使现有的摩擦模型适应新的动力学。我们通过在对称摩擦数据集上训练基础神经网络来学习速度和摩擦扭矩之间的准确关系来验证我们的方法。随后，为了适应更复杂的非对称设置，我们在小数据集上训练第二个网络，重点是预测初始网络输出的残差。通过以适当的方式组合两个网络的输出，我们提出的估计器显着优于传统的基于模型的方法、扩展的 LuGre 模型和基础神经网络。此外，我们在涉及外部载荷的轨迹上评估我们的方法，仍然观察到比传统方法有显着的改进，大约 60%–70%。我们的方法在训练期间不依赖于外部负载的数据，从而无需外部扭矩传感器。这证明了我们的方法的泛化能力，即使使用少量数据（不到一分钟），也能够根据有关不同设置中摩擦的先验知识来适应不同的场景。