Accuracy Analysis and Enhancement via Transformer-based Robot Calibration of the da Vinci Research Kit Si (dVRK-Si)

Autonomous robotic surgery has the potential to achieve better patient outcomes and greater efficiency than manual approaches by leveraging precise robotic motion for task execution. Unlike articulated robots, tendon-driven surgical robots suffer from lower motion precision due to the hysteresis and friction inherent in their cable-pulley design. These nonlinear mechanical effects complicate precise end-effector control, posing challenges to autonomous task performance and safety concerns. In this paper, we present a calibration workflow using machine learning models to enhance the positioning accuracy of a tendon-driven surgical robot, evaluated on the real da Vinci Research Kit Si (dVRK-Si). An accuracy analysis was carried out for the dVRK-Si and a transformer-based model was trained to compensate for the nonlinear effects of the system, enhancing system positioning accuracy. The calibrated system was evaluated on trajectory-following tasks in Cartesian space, demonstrating 51.37% and 45.87% reduction in position and orientation errors. Additionally, we tested the models’ transferability with two uncalibrated surgical instruments and reported accuracy improvements over the first-generation dVRK-Classic.