An active semi-supervised transfer learning method for robot pose error prediction and compensation

Robots are widely employed in industrial settings owing to their efficiency, flexibility, and extensive operational ranges. However, their application in high-precision scenarios is limited owing to their low absolute accuracies. Existing methods suffer from high measurement costs, and limited applicability and accuracy. To address these issues, an active semi supervised transfer learning method (ASTL) is introduced. The pose error prediction problem was modelled as a transfer learning paradigm for the first time. It leverages the proposed multi-stage greedy sampling (MGS) for informed sample labelling combined with coarse calibration and semi supervised transfer learning (STL) to embed theoretical knowledge for globally accurate predictions. The proposed method is compared with other prediction and compensation approaches for four robotic motion tasks. It significantly reduces the time consumption by approximately 89.3% compared with direct measurements and achieves a maximum reduction of approximately 90% in robot pose errors. This substantial enhancement in pose accuracy promotes high-quality applications of robots in the industrial sector.