UGP-KD: An unsupervised generalized prediction framework for robot machining quality under historical task knowledge distillation for new tasks

Despite the extensive use of robots in numerous fields, condition-sensitive robotic machining errors represent a significant obstacle to their high-precision implementation. Prediction-based compensatory control represents a crucial approach to enhancing robot accuracy. The extant machining error prediction methods are beset with shortcomings, including inadequate feature extraction, limited generalizability with respect to working conditions, and the squandering of knowledge. Therefore, the influence mechanisms of robot errors by different working conditions and spatial ontology properties are explored in this paper. A spatial-temporal dual-view error prediction model is constructed for a single condition. Moreover, an innovative unsupervised generalized prediction strategy of machining error for new conditions under the historical task knowledge distillation of Multi-Teacher-Single-Student (MTSS) is proposed. This strategy enables the extraction and reuse of knowledge at three levels: teacher-teaching, student-learning, and generalized expansion. It also ensures the high-precision, lightweight, and high-efficiency prediction of machining error for unseen conditions. The proposed method was validated on constructed complex part inner wall features. The minimum mean absolute error (MAE) indicator for single condition prediction is 0.005 mm, which is a significantly more accurate result than other methods under comparison. Furthermore, the average MAE of unsupervised generalization for new conditions is 0.019 mm, which meets the practical application requirements. Furthermore, the distilled model complexity is reduced by 75 %, and the average inference efficiency is enhanced by over 95 %. This provides the potential for lightweight online deployment. The proposed method offers a robust foundation for prediction-based error online compensation, which is anticipated to facilitate the expansion of robots in high-precision scenarios.