Deep Learning-Based Friction Compensation in Low Velocity for Enhanced Direct Teaching in Collaborative Manipulators

Abstract

Direct teaching in collaborative manipulators, an essential method for intuitive trajectory control, faces significant challenges due to friction in robot joints. To address this, we present a novel friction compensation framework to improve direct teaching methods for robots. Our approach focuses on mitigating friction in the joints most susceptible to frictional effects, ensuring smoother and more precise motion. The proposed framework uses deep neural networks (DNN) to model the complex friction behavior. This approach circumvents the difficulties associated with traditional friction compensation model selection. We develop specific data input preprocessing algorithms that optimize friction estimation when paired with standard encoders commonly used in collaborative robots. In addition, our custom loss function is specifically designed to improve DNN training in these low-velocity regions. To evaluate the effectiveness of our framework, we conduct comprehensive ablation studies assessing the impact of two critical components: the preprocessing algorithms and the custom loss function. These studies provide insight into the contributions of each element to overall performance. Experimental validation using two 6-DoF collaborative robots demonstrates the practical applicability and effectiveness of our approach.