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 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-only 43 seconds of a robot movement-enabling adaptation to diverse scenarios based on prior knowledge about friction in different settings.

在第四次工业革命中，机器人的部署是不可或缺的，但是机器人与人类的合作制造过程非常复杂，其中建模机器人关节的摩擦力矩一直存在问题。为了解决这个挑战，我们提出了一种基于残差学习的新方法，旨在利用尽可能少的数据将现有摩擦模型适应新的动力学。通过合理地结合两个网络的输出，我们的方法在预测摩擦力矩方面表现优于传统的基于模型的方法和基础神经网络。此外，我们的方法在涉及外部负载的轨迹上进行评估，仍然观察到显著的改进，相比传统方法，提高了约60-70％。我们的方法在训练时不依赖外部负载数据，消除了对外部扭矩传感器的需求，证明了我们的方法的泛化能力，即使只有少量数据-仅有43秒的机器人运动，也能根据对不同环境中摩擦的先前知识进行适应。

基于学习的机器人摩擦模型适应