Active Transfer Learning Gaussian Process for Reliable Trajectory Prediction of the UR5 Robotic Manipulator

This paper presents a simulation-driven framework employing an Active Transfer Learning Gaussian Process (ATGP) model for accurate trajectory prediction and reliability analysis of the UR5 robotic manipulator. The method integrates transfer learning, Gaussian Process Regression, and active sampling to address challenges under limited target data. Preprocessing steps such as outlier removal, feature scaling, and Principal Component Analysis enhance data quality. A physically informed synthetic source domain facilitates effective knowledge transfer. Using DH-parameters as input, the ATGP predicts 3D end-effector trajectories over time. Results show a mean absolute error below 0.01, demonstrating consistency and scalability for real-time, uncertainty-aware robotic applications. This is the first ATGP-based UR5 framework that unites PCA-guided, physics-informed source synthesis with multi-output transfer GPR as well as coordinate- and time-resolved reliability analysis under scarce target-domain data.